Compare the Top LLMOps Tools in 2025
LLMOps tools are a set of tools and techniques that are used to manage the lifecycle of large language models (LLMs). These tools can help with tasks such as LLM testing, data management, model development, deployment, and monitoring. LLMOps stands for Large Language Model Operations. It's similar to MLOps but focuses on the operational capabilities and infrastructure required to fine-tune existing foundational models and large language models (LLMs) and deploying these refined models as part of a product. LLMs are deep learning models that can generate outputs in human language. They have billions of parameters and are trained on billions of words. This makes them very powerful, but also very complex to manage. Here's a list of the best LLMOps tools:
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1
Vertex AI
Google
LLMOps in Vertex AI is a comprehensive platform for managing the lifecycle of large language models (LLMs), from training to deployment and monitoring. It provides tools for fine-tuning, versioning, and tracking LLM performance, ensuring that these powerful models are optimized for real-world use cases. By leveraging LLMOps, businesses can maintain their LLMs’ relevance and accuracy over time, even as the underlying data evolves. New customers receive $300 in free credits, enabling them to experiment with the LLMOps capabilities and gain deeper insights into their models' behavior. With this functionality, businesses can ensure that their LLMs remain effective and continue to deliver value across applications like text generation, translation, and summarization.Starting Price: Free ($300 in free credits) -
2
Google AI Studio
Google
LLMOps in Google AI Studio focuses on the management, monitoring, and optimization of large language models (LLMs) throughout their lifecycle. This includes tasks such as deployment, scaling, versioning, and continuous performance tracking, ensuring that LLMs deliver reliable and efficient results in production environments. By providing specialized tools for LLMs, Google AI Studio simplifies the complexities associated with managing these models and enables businesses to deploy them at scale. The platform also offers advanced monitoring capabilities to track model performance and detect potential issues before they affect the user experience.Starting Price: Free -
3
LM-Kit.NET
LM-Kit
LM-Kit.NET is a cutting-edge, high-level inference SDK designed specifically to bring the advanced capabilities of Large Language Models (LLM) into the C# ecosystem. Tailored for developers working within .NET, LM-Kit.NET provides a comprehensive suite of powerful Generative AI tools, making it easier than ever to integrate AI-driven functionality into your applications. The SDK is versatile, offering specialized AI features that cater to a variety of industries. These include text completion, Natural Language Processing (NLP), content retrieval, text summarization, text enhancement, language translation, and much more. Whether you are looking to enhance user interaction, automate content creation, or build intelligent data retrieval systems, LM-Kit.NET offers the flexibility and performance needed to accelerate your project.Starting Price: Free (Community) or $1000/year -
4
Stack AI
Stack AI
AI agents that interact with users, answer questions, and complete tasks, using your internal data and APIs. AI that answers questions, summarize, and extract insights from any document, no matter how long. Generate tags, summaries, and transfer styles or formats between documents and data sources. Developer teams use Stack AI to automate customer support, process documents, qualify sales leads, and search through libraries of data. Try multiple prompts and LLM architectures with the ease of a button. Collect data and run fine-tuning jobs to build the optimal LLM for your product. We host all your workflows as APIs so that your users can access AI instantly. Select from the different LLM providers to compare fine-tuning jobs that satisfy your accuracy, price, and latency needs.Starting Price: $199/month -
5
OpenAI
OpenAI
OpenAI’s mission is to ensure that artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work—benefits all of humanity. We will attempt to directly build safe and beneficial AGI, but will also consider our mission fulfilled if our work aids others to achieve this outcome. Apply our API to any language task — semantic search, summarization, sentiment analysis, content generation, translation, and more — with only a few examples or by specifying your task in English. One simple integration gives you access to our constantly-improving AI technology. Explore how you integrate with the API with these sample completions. -
6
Cohere
Cohere AI
Cohere is an enterprise AI platform that enables developers and businesses to build powerful language-based applications. Specializing in large language models (LLMs), Cohere provides solutions for text generation, summarization, and semantic search. Their model offerings include the Command family for high-performance language tasks and Aya Expanse for multilingual applications across 23 languages. Focused on security and customization, Cohere allows flexible deployment across major cloud providers, private cloud environments, or on-premises setups to meet diverse enterprise needs. The company collaborates with industry leaders like Oracle and Salesforce to integrate generative AI into business applications, improving automation and customer engagement. Additionally, Cohere For AI, their research lab, advances machine learning through open-source projects and a global research community.Starting Price: Free -
7
Langfuse
Langfuse
Langfuse is an open source LLM engineering platform to help teams collaboratively debug, analyze and iterate on their LLM Applications. Observability: Instrument your app and start ingesting traces to Langfuse Langfuse UI: Inspect and debug complex logs and user sessions Prompts: Manage, version and deploy prompts from within Langfuse Analytics: Track metrics (LLM cost, latency, quality) and gain insights from dashboards & data exports Evals: Collect and calculate scores for your LLM completions Experiments: Track and test app behavior before deploying a new version Why Langfuse? - Open source - Model and framework agnostic - Built for production - Incrementally adoptable - start with a single LLM call or integration, then expand to full tracing of complex chains/agents - Use GET API to build downstream use cases and export dataStarting Price: $29/month -
8
Lyzr
Lyzr AI
Lyzr Agent Studio is a low-code/no-code platform for enterprises to build, deploy, and scale AI agents with minimal technical complexity. Built on Lyzr's robust Agent Framework - the first and only agent framework to have safe and responsible AI natively integrated into the core agent architecture, this platform allows you to build AI Agents while keeping enterprise-grade safety and reliability in mind. The platform allows both technical and non-technical users to create AI-powered solutions that drive automation, improve operational efficiency, and enhance customer experiences—without the need for extensive coding expertise. Whether you're deploying AI agents for Sales, Marketing, HR, or Finance, or building complex, industry-specific applications for sectors like BFSI, Lyzr Agent Studio provides the tools to create agents that are both highly customizable and compliant with enterprise-grade security standards.Starting Price: $19/month/user -
9
LangChain
LangChain
LangChain is a powerful, composable framework designed for building, running, and managing applications powered by large language models (LLMs). It offers an array of tools for creating context-aware, reasoning applications, allowing businesses to leverage their own data and APIs to enhance functionality. LangChain’s suite includes LangGraph for orchestrating agent-driven workflows, and LangSmith for agent observability and performance management. Whether you're building prototypes or scaling full applications, LangChain offers the flexibility and tools needed to optimize the LLM lifecycle, with seamless integrations and fault-tolerant scalability. -
10
BenchLLM
BenchLLM
Use BenchLLM to evaluate your code on the fly. Build test suites for your models and generate quality reports. Choose between automated, interactive or custom evaluation strategies. We are a team of engineers who love building AI products. We don't want to compromise between the power and flexibility of AI and predictable results. We have built the open and flexible LLM evaluation tool that we have always wished we had. Run and evaluate models with simple and elegant CLI commands. Use the CLI as a testing tool for your CI/CD pipeline. Monitor models performance and detect regressions in production. Test your code on the fly. BenchLLM supports OpenAI, Langchain, and any other API out of the box. Use multiple evaluation strategies and visualize insightful reports. -
11
ClearML
ClearML
ClearML is the leading open source MLOps and AI platform that helps data science, ML engineering, and DevOps teams easily develop, orchestrate, and automate ML workflows at scale. Our frictionless, unified, end-to-end MLOps suite enables users and customers to focus on developing their ML code and automation. ClearML is used by more than 1,300 enterprise customers to develop a highly repeatable process for their end-to-end AI model lifecycle, from product feature exploration to model deployment and monitoring in production. Use all of our modules for a complete ecosystem or plug in and play with the tools you have. ClearML is trusted by more than 150,000 forward-thinking Data Scientists, Data Engineers, ML Engineers, DevOps, Product Managers and business unit decision makers at leading Fortune 500 companies, enterprises, academia, and innovative start-ups worldwide within industries such as gaming, biotech , defense, healthcare, CPG, retail, financial services, among others.Starting Price: $15 -
12
Valohai
Valohai
Models are temporary, pipelines are forever. Train, Evaluate, Deploy, Repeat. Valohai is the only MLOps platform that automates everything from data extraction to model deployment. Automate everything from data extraction to model deployment. Store every single model, experiment and artifact automatically. Deploy and monitor models in a managed Kubernetes cluster. Point to your code & data and hit run. Valohai launches workers, runs your experiments and shuts down the instances for you. Develop through notebooks, scripts or shared git projects in any language or framework. Expand endlessly through our open API. Automatically track each experiment and trace back from inference to the original training data. Everything fully auditable and shareable.Starting Price: $560 per month -
13
Amazon SageMaker
Amazon
Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models. Traditional ML development is a complex, expensive, iterative process made even harder because there are no integrated tools for the entire machine learning workflow. You need to stitch together tools and workflows, which is time-consuming and error-prone. SageMaker solves this challenge by providing all of the components used for machine learning in a single toolset so models get to production faster with much less effort and at lower cost. Amazon SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps. SageMaker Studio gives you complete access, control, and visibility into each step required. -
14
neptune.ai
neptune.ai
Neptune.ai is a machine learning operations (MLOps) platform designed to streamline the tracking, organizing, and sharing of experiments and model-building processes. It provides a comprehensive environment for data scientists and machine learning engineers to log, visualize, and compare model training runs, datasets, hyperparameters, and metrics in real-time. Neptune.ai integrates easily with popular machine learning libraries, enabling teams to efficiently manage both research and production workflows. With features that support collaboration, versioning, and experiment reproducibility, Neptune.ai enhances productivity and helps ensure that machine learning projects are transparent and well-documented across their lifecycle.Starting Price: $49 per month -
15
JFrog ML
JFrog
JFrog ML (formerly Qwak) offers an MLOps platform designed to accelerate the development, deployment, and monitoring of machine learning and AI applications at scale. The platform enables organizations to manage the entire lifecycle of machine learning models, from training to deployment, with tools for model versioning, monitoring, and performance tracking. It supports a wide variety of AI models, including generative AI and LLMs (Large Language Models), and provides an intuitive interface for managing prompts, workflows, and feature engineering. JFrog ML helps businesses streamline their ML operations and scale AI applications efficiently, with integrated support for cloud environments. -
16
Hugging Face
Hugging Face
A new way to automatically train, evaluate and deploy state-of-the-art Machine Learning models. AutoTrain is an automatic way to train and deploy state-of-the-art Machine Learning models, seamlessly integrated with the Hugging Face ecosystem. Your training data stays on our server, and is private to your account. All data transfers are protected with encryption. Available today: text classification, text scoring, entity recognition, summarization, question answering, translation and tabular. CSV, TSV or JSON files, hosted anywhere. We delete your training data after training is done. Hugging Face also hosts an AI content detection tool.Starting Price: $9 per month -
17
Comet
Comet
Manage and optimize models across the entire ML lifecycle, from experiment tracking to monitoring models in production. Achieve your goals faster with the platform built to meet the intense demands of enterprise teams deploying ML at scale. Supports your deployment strategy whether it’s private cloud, on-premise servers, or hybrid. Add two lines of code to your notebook or script and start tracking your experiments. Works wherever you run your code, with any machine learning library, and for any machine learning task. Easily compare experiments—code, hyperparameters, metrics, predictions, dependencies, system metrics, and more—to understand differences in model performance. Monitor your models during every step from training to production. Get alerts when something is amiss, and debug your models to address the issue. Increase productivity, collaboration, and visibility across all teams and stakeholders.Starting Price: $179 per user per month -
18
TrueFoundry
TrueFoundry
TrueFoundry is a Cloud-native Machine Learning Training and Deployment PaaS on top of Kubernetes that enables Machine learning teams to train and Deploy models at the speed of Big Tech with 100% reliability and scalability - allowing them to save cost and release Models to production faster. We abstract out the Kubernetes for Data Scientists and enable them to operate in a way they are comfortable. It also allows teams to deploy and fine-tune large language models seamlessly with full security and cost optimization. TrueFoundry is open-ended, API Driven and integrates with the internal systems, deploys on a company's internal infrastructure and ensures complete Data Privacy and DevSecOps practices.Starting Price: $5 per month -
19
Vald
Vald
Vald is a highly scalable distributed fast approximate nearest neighbor dense vector search engine. Vald is designed and implemented based on the Cloud-Native architecture. It uses the fastest ANN Algorithm NGT to search neighbors. Vald has automatic vector indexing and index backup, and horizontal scaling which made for searching from billions of feature vector data. Vald is easy to use, feature-rich and highly customizable as you needed. Usually the graph requires locking during indexing, which cause stop-the-world. But Vald uses distributed index graph so it continues to work during indexing. Vald implements its own highly customizable Ingress/Egress filter. Which can be configured to fit the gRPC interface. Horizontal scalable on memory and cpu for your demand. Vald supports to auto backup feature using Object Storage or Persistent Volume which enables disaster recovery.Starting Price: Free -
20
Langdock
Langdock
Native support for ChatGPT and LangChain. Bing, HuggingFace and more coming soon. Add your API documentation manually or import an existing OpenAPI specification. Access the request prompt, parameters, headers, body and more. Inspect detailed live metrics about how your plugin is performing, including latencies, errors, and more. Configure your own dashboards, track funnels and aggregated metrics.Starting Price: Free -
21
ZenML
ZenML
Simplify your MLOps pipelines. Manage, deploy, and scale on any infrastructure with ZenML. ZenML is completely free and open-source. See the magic with just two simple commands. Set up ZenML in a matter of minutes, and start with all the tools you already use. ZenML standard interfaces ensure that your tools work together seamlessly. Gradually scale up your MLOps stack by switching out components whenever your training or deployment requirements change. Keep up with the latest changes in the MLOps world and easily integrate any new developments. Define simple and clear ML workflows without wasting time on boilerplate tooling or infrastructure code. Write portable ML code and switch from experimentation to production in seconds. Manage all your favorite MLOps tools in one place with ZenML's plug-and-play integrations. Prevent vendor lock-in by writing extensible, tooling-agnostic, and infrastructure-agnostic code.Starting Price: Free -
22
Deep Lake
activeloop
Generative AI may be new, but we've been building for this day for the past 5 years. Deep Lake thus combines the power of both data lakes and vector databases to build and fine-tune enterprise-grade, LLM-based solutions, and iteratively improve them over time. Vector search does not resolve retrieval. To solve it, you need a serverless query for multi-modal data, including embeddings or metadata. Filter, search, & more from the cloud or your laptop. Visualize and understand your data, as well as the embeddings. Track & compare versions over time to improve your data & your model. Competitive businesses are not built on OpenAI APIs. Fine-tune your LLMs on your data. Efficiently stream data from remote storage to the GPUs as models are trained. Deep Lake datasets are visualized right in your browser or Jupyter Notebook. Instantly retrieve different versions of your data, materialize new datasets via queries on the fly, and stream them to PyTorch or TensorFlow.Starting Price: $995 per month -
23
Flowise
Flowise AI
Flowise is an open-source, low-code platform that enables developers to create customized Large Language Model (LLM) applications through a user-friendly drag-and-drop interface. It supports integration with various LLMs, including LangChain and LlamaIndex, and offers over 100 integrations to facilitate the development of AI agents and orchestration flows. Flowise provides APIs, SDKs, and embedded widgets for seamless incorporation into existing systems, and is platform-agnostic, allowing deployment in air-gapped environments with local LLMs and vector databases.Starting Price: Free -
24
Confident AI
Confident AI
Confident AI offers an open-source package called DeepEval that enables engineers to evaluate or "unit test" their LLM applications' outputs. Confident AI is our commercial offering and it allows you to log and share evaluation results within your org, centralize your datasets used for evaluation, debug unsatisfactory evaluation results, and run evaluations in production throughout the lifetime of your LLM application. We offer 10+ default metrics for engineers to plug and use.Starting Price: $39/month -
25
Klu
Klu
Klu.ai is a Generative AI platform that simplifies the process of designing, deploying, and optimizing AI applications. Klu integrates with your preferred Large Language Models, incorporating data from varied sources, giving your applications unique context. Klu accelerates building applications using language models like Anthropic Claude, Azure OpenAI, GPT-4, and over 15 other models, allowing rapid prompt/model experimentation, data gathering and user feedback, and model fine-tuning while cost-effectively optimizing performance. Ship prompt generations, chat experiences, workflows, and autonomous workers in minutes. Klu provides SDKs and an API-first approach for all capabilities to enable developer productivity. Klu automatically provides abstractions for common LLM/GenAI use cases, including: LLM connectors, vector storage and retrieval, prompt templates, observability, and evaluation/testing tooling.Starting Price: $97 -
26
Ollama
Ollama
Ollama is an innovative platform that focuses on providing AI-powered tools and services, designed to make it easier for users to interact with and build AI-driven applications. Run AI models locally. By offering a range of solutions, including natural language processing models and customizable AI features, Ollama empowers developers, businesses, and organizations to integrate advanced machine learning technologies into their workflows. With an emphasis on usability and accessibility, Ollama strives to simplify the process of working with AI, making it an appealing option for those looking to harness the potential of artificial intelligence in their projects.Starting Price: Free -
27
LLM Spark
LLM Spark
Whether you're building AI chatbots, virtual assistants, or other intelligent applications, set up your workspace effortlessly by integrating GPT-powered language models with your provider keys for unparalleled performance. Accelerate the creation of your diverse AI applications using LLM Spark's GPT-driven templates or craft unique projects from the ground up. Test & compare multiple models simultaneously for optimal performance across multiple scenarios. Save prompt versions and history effortlessly while streamlining development. Invite members to your workspace and collaborate on projects with ease. Semantic search for powerful search capabilities to find documents based on meaning, not just keywords. Deploy trained prompts effortlessly, making AI applications accessible across platforms.Starting Price: $29 per month -
28
Evidently AI
Evidently AI
The open-source ML observability platform. Evaluate, test, and monitor ML models from validation to production. From tabular data to NLP and LLM. Built for data scientists and ML engineers. All you need to reliably run ML systems in production. Start with simple ad hoc checks. Scale to the complete monitoring platform. All within one tool, with consistent API and metrics. Useful, beautiful, and shareable. Get a comprehensive view of data and ML model quality to explore and debug. Takes a minute to start. Test before you ship, validate in production and run checks at every model update. Skip the manual setup by generating test conditions from a reference dataset. Monitor every aspect of your data, models, and test results. Proactively catch and resolve production model issues, ensure optimal performance, and continuously improve it.Starting Price: $500 per month -
29
Lilac
Lilac
Lilac is an open source tool that enables data and AI practitioners to improve their products by improving their data. Understand your data with powerful search and filtering. Collaborate with your team on a single, centralized dataset. Apply best practices for data curation, like removing duplicates and PII to reduce dataset size and lower training cost and time. See how your pipeline impacts your data using our diff viewer. Clustering is a technique that automatically assigns categories to each document by analyzing the text content and putting similar documents in the same category. This reveals the overarching structure of your dataset. Lilac uses state-of-the-art algorithms and LLMs to cluster the dataset and assign informative, descriptive titles. Before we do advanced searching, like concept or semantic search, we can immediately use keyword search by typing a keyword in the search box.Starting Price: Free -
30
Athina AI
Athina AI
Athina is a collaborative AI development platform that enables teams to build, test, and monitor AI applications efficiently. It offers features such as prompt management, evaluation tools, dataset handling, and observability, all designed to streamline the development of reliable AI systems. Athina supports integration with various models and services, including custom models, and ensures data privacy through fine-grained access controls and self-hosted deployment options. The platform is SOC-2 Type 2 compliant, providing a secure environment for AI development. Athina's user-friendly interface allows both technical and non-technical team members to collaborate effectively, accelerating the deployment of AI features.Starting Price: Free -
31
OpenPipe
OpenPipe
OpenPipe provides fine-tuning for developers. Keep your datasets, models, and evaluations all in one place. Train new models with the click of a button. Automatically record LLM requests and responses. Create datasets from your captured data. Train multiple base models on the same dataset. We serve your model on our managed endpoints that scale to millions of requests. Write evaluations and compare model outputs side by side. Change a couple of lines of code, and you're good to go. Simply replace your Python or Javascript OpenAI SDK and add an OpenPipe API key. Make your data searchable with custom tags. Small specialized models cost much less to run than large multipurpose LLMs. Replace prompts with models in minutes, not weeks. Fine-tuned Mistral and Llama 2 models consistently outperform GPT-4-1106-Turbo, at a fraction of the cost. We're open-source, and so are many of the base models we use. Own your own weights when you fine-tune Mistral and Llama 2, and download them at any time.Starting Price: $1.20 per 1M tokens -
32
PlugBear
Runbear
PlugBear is a no/low-code solution for connecting communication channels with LLM (Large Language Model) applications. For example, it enables the creation of a Slack bot from an LLM app in just a few clicks. When a trigger event occurs in the integrated channels, PlugBear receives this event. It then transforms the messages to be suitable for LLM applications and initiates generation. Once the apps complete the generation, PlugBear transforms the results to be compatible with each channel. This process allows users of different channels to interact seamlessly with LLM applications.Starting Price: $31 per month -
33
Unify AI
Unify AI
Explore the power of choosing the right LLM for your needs and how to optimize for quality, speed, and cost-efficiency. Access all LLMs across all providers with a single API key and a standard API. Setup your own cost, latency, and output speed constraints. Define a custom quality metric. Personalize your router for your requirements. Systematically send your queries to the fastest provider, based on the very latest benchmark data for your region of the world, refreshed every 10 minutes. Get started with Unify with our dedicated walkthrough. Discover the features you already have access to and our upcoming roadmap. Just create a Unify account to access all models from all supported providers with a single API key. Our router balances output quality, speed, and cost based on user-specific preferences. The quality is predicted ahead of time using a neural scoring function, which predicts how good each model would be at responding to a given prompt.Starting Price: $1 per credit -
34
Trustwise
Trustwise
Trustwise is a single API that safely unlocks the power of generative AI at work. Modern AI systems are powerful yet often grapple with compliance, bias, data breaches, and cost management challenges. Trustwise delivers a seamless, industry-optimized API for AI trust, ensuring business alignment, cost-efficiency, and ethical integrity across all AI models and tools. Trustwise helps you innovate confidently with AI. Perfected over two years in partnership with leading industry players, our software guarantees the safety, alignment, and cost optimization of your AI initiatives. Actively mitigates harmful hallucinations and prevents leakage of sensitive information. Audit records for learning, and improvement; ensure interaction traceability and accountability. Ensures human oversight of AI decisions and aids learning continuous system adaptation. Built-in benchmarking and certification, NIST AI RMF, ISO 42001 aligned.Starting Price: $799 per month -
35
Deepchecks
Deepchecks
Release high-quality LLM apps quickly without compromising on testing. Never be held back by the complex and subjective nature of LLM interactions. Generative AI produces subjective results. Knowing whether a generated text is good usually requires manual labor by a subject matter expert. If you’re working on an LLM app, you probably know that you can’t release it without addressing countless constraints and edge-cases. Hallucinations, incorrect answers, bias, deviation from policy, harmful content, and more need to be detected, explored, and mitigated before and after your app is live. Deepchecks’ solution enables you to automate the evaluation process, getting “estimated annotations” that you only override when you have to. Used by 1000+ companies, and integrated into 300+ open source projects, the core behind our LLM product is widely tested and robust. Validate machine learning models and data with minimal effort, in both the research and the production phases.Starting Price: $1,000 per month -
36
Spark NLP
John Snow Labs
Experience the power of large language models like never before, unleashing the full potential of Natural Language Processing (NLP) with Spark NLP, the open source library that delivers scalable LLMs. The full code base is open under the Apache 2.0 license, including pre-trained models and pipelines. The only NLP library built natively on Apache Spark. The most widely used NLP library in the enterprise. Spark ML provides a set of machine learning applications that can be built using two main components, estimators and transformers. The estimators have a method that secures and trains a piece of data to such an application. The transformer is generally the result of a fitting process and applies changes to the target dataset. These components have been embedded to be applicable to Spark NLP. Pipelines are a mechanism for combining multiple estimators and transformers in a single workflow. They allow multiple chained transformations along a machine-learning task.Starting Price: Free -
37
Langtrace
Langtrace
Langtrace is an open source observability tool that collects and analyzes traces and metrics to help you improve your LLM apps. Langtrace ensures the highest level of security. Our cloud platform is SOC 2 Type II certified, ensuring top-tier protection for your data. Supports popular LLMs, frameworks, and vector databases. Langtrace can be self-hosted and supports OpenTelemetry standard traces, which can be ingested by any observability tool of your choice, resulting in no vendor lock-in. Get visibility and insights into your entire ML pipeline, whether it is a RAG or a fine-tuned model with traces and logs that cut across the framework, vectorDB, and LLM requests. Annotate and create golden datasets with traced LLM interactions, and use them to continuously test and enhance your AI applications. Langtrace includes built-in heuristic, statistical, and model-based evaluations to support this process.Starting Price: Free -
38
LLMWare.ai
LLMWare.ai
Our open source research efforts are focused both on the new "ware" ("middleware" and "software" that will wrap and integrate LLMs), as well as building high-quality, automation-focused enterprise models available in Hugging Face. LLMWare also provides a coherent, high-quality, integrated, and organized framework for development in an open system that provides the foundation for building LLM-applications for AI Agent workflows, Retrieval Augmented Generation (RAG), and other use cases, which include many of the core objects for developers to get started instantly. Our LLM framework is built from the ground up to handle the complex needs of data-sensitive enterprise use cases. Use our pre-built specialized LLMs for your industry or we can customize and fine-tune an LLM for specific use cases and domains. From a robust, integrated AI framework to specialized models and implementation, we provide an end-to-end solution.Starting Price: Free -
39
Laminar
Laminar
Laminar is an open source all-in-one platform for engineering best-in-class LLM products. Data governs the quality of your LLM application. Laminar helps you collect it, understand it, and use it. When you trace your LLM application, you get a clear picture of every step of execution and simultaneously collect invaluable data. You can use it to set up better evaluations, as dynamic few-shot examples, and for fine-tuning. All traces are sent in the background via gRPC with minimal overhead. Tracing of text and image models is supported, audio models are coming soon. You can set up LLM-as-a-judge or Python script evaluators to run on each received span. Evaluators label spans, which is more scalable than human labeling, and especially helpful for smaller teams. Laminar lets you go beyond a single prompt. You can build and host complex chains, including mixtures of agents or self-reflecting LLM pipelines.Starting Price: $25 per month -
40
Fetch Hive
Fetch Hive
Fetch Hive is a versatile Generative AI Collaboration Platform packed with features and values that enhance user experience and productivity: Custom RAG Chat Agents: Users can create chat agents with retrieval-augmented generation, which improves response quality and relevance. Centralized Data Storage: It provides a system for easily accessing and managing all necessary data for AI model training and deployment. Real-Time Data Integration: By incorporating real-time data from Google Search, Fetch Hive enhances workflows with up-to-date information, boosting decision-making and productivity. Generative AI Prompt Management: The platform helps in building and managing AI prompts, enabling users to refine and achieve desired outputs efficiently. Fetch Hive is a comprehensive solution for those looking to develop and manage generative AI projects effectively, optimizing interactions with advanced features and streamlined workflows.Starting Price: $49/month -
41
BentoML
BentoML
Serve your ML model in any cloud in minutes. Unified model packaging format enabling both online and offline serving on any platform. 100x the throughput of your regular flask-based model server, thanks to our advanced micro-batching mechanism. Deliver high-quality prediction services that speak the DevOps language and integrate perfectly with common infrastructure tools. Unified format for deployment. High-performance model serving. DevOps best practices baked in. The service uses the BERT model trained with the TensorFlow framework to predict movie reviews' sentiment. DevOps-free BentoML workflow, from prediction service registry, deployment automation, to endpoint monitoring, all configured automatically for your team. A solid foundation for running serious ML workloads in production. Keep all your team's models, deployments, and changes highly visible and control access via SSO, RBAC, client authentication, and auditing logs.Starting Price: Free -
42
Anyscale
Anyscale
A fully-managed platform for Ray, from the creators of Ray. The best way to develop, scale, and deploy AI apps on Ray. Accelerate development and deployment for any AI application, at any scale. Everything you love about Ray, minus the DevOps load. Let us run Ray for you, hosted on cloud infrastructure fully managed by us so that you can focus on what you do best, and ship great products. Anyscale automatically scales your infrastructure and clusters up or down to meet the dynamic demands of your workloads. Whether it’s executing a production workflow on a schedule (for eg. retraining and updating a model with fresh data every week) or running a highly scalable and low-latency production service (for eg. serving a machine learning model), Anyscale makes it easy to create, deploy, and monitor machine learning workflows in production. Anyscale will automatically create a cluster, run the job on it, and monitor the job until it succeeds. -
43
Pinecone
Pinecone
The AI Knowledge Platform. The Pinecone Database, Inference, and Assistant make building high-performance vector search apps easy. Developer-friendly, fully managed, and easily scalable without infrastructure hassles. Once you have vector embeddings, manage and search through them in Pinecone to power semantic search, recommenders, and other applications that rely on relevant information retrieval. Ultra-low query latency, even with billions of items. Give users a great experience. Live index updates when you add, edit, or delete data. Your data is ready right away. Combine vector search with metadata filters for more relevant and faster results. Launch, use, and scale your vector search service with our easy API, without worrying about infrastructure or algorithms. We'll keep it running smoothly and securely. -
44
Supervised
Supervised
Utilize the efficiency of OpenAI’s GPT engine to build supervised large language models which are backed by your very own data. Enterprises looking to integrate AI into their current business can use Supervised to build scalable AI apps. Building your own LLM can be tough. That’s why we let you build and sell your own AI apps with Supervised. Supervised AI provides you an environment to build custom LLM & AI Apps that are powerful and scalable. Using our custom models and data sources, you can build high-accuracy AI at a fast pace. Businesses are utilizing AI in a very layman's way right now, where most of its potential is yet to unlock. At Supervised, we let you harness your data to build a completely new AI model from scratch. Build custom AI apps on data sources and models built by other developers.Starting Price: $19 per month -
45
Usage Panda
Usage Panda
Layer enterprise-level security features over your OpenAI usage. OpenAI LLM APIs are incredibly powerful, but they lack the granular control and visibility that enterprises expect. Usage Panda fixes that. Usage Panda evaluates security policies for requests before they're sent to OpenAI. Avoid surprise bills by only allowing requests that fall below a cost threshold. Opt-in to log the complete request, parameters, and response for every request made to OpenAI. Create an unlimited number of connections, each with its own custom policies and limits. Monitor, redact, and block malicious attempts to alter or reveal system prompts. Explore usage in granular detail using Usage Panda's visualization tools and custom charts. Get notified via email or Slack before reaching a usage limit or billing threshold. Associate costs and policy violations back to end application users and implement per-user rate limits. -
46
Taylor AI
Taylor AI
Training open source language models requires time and specialized knowledge. Taylor AI empowers your engineering team to focus on generating real business value, rather than deciphering complex libraries and setting up training infrastructure. Working with third-party LLM providers requires exposing your company's sensitive data. Most providers reserve the right to re-train models with your data. With Taylor AI, you own and control your models. Break away from the pay-per-token pricing structure. With Taylor AI, you only pay to train the model. You have the freedom to deploy and interact with your AI models as much as you like. New open source models emerge every month. Taylor AI stays current on the best open source language models, so you don't have to. Stay ahead, and train with the latest open source models. You own your model, so you can deploy it on your terms according to your unique compliance and security standards. -
47
Portkey
Portkey.ai
Launch production-ready apps with the LMOps stack for monitoring, model management, and more. Replace your OpenAI or other provider APIs with the Portkey endpoint. Manage prompts, engines, parameters, and versions in Portkey. Switch, test, and upgrade models with confidence! View your app performance & user level aggregate metics to optimise usage and API costs Keep your user data secure from attacks and inadvertent exposure. Get proactive alerts when things go bad. A/B test your models in the real world and deploy the best performers. We built apps on top of LLM APIs for the past 2 and a half years and realised that while building a PoC took a weekend, taking it to production & managing it was a pain! We're building Portkey to help you succeed in deploying large language models APIs in your applications. Regardless of you trying Portkey, we're always happy to help!Starting Price: $49 per month -
48
Pezzo
Pezzo
Pezzo is the open-source LLMOps platform built for developers and teams. In just two lines of code, you can seamlessly troubleshoot and monitor your AI operations, collaborate and manage your prompts in one place, and instantly deploy changes to any environment.Starting Price: $0 -
49
Gradient
Gradient
Fine-tune and get completions on private LLMs with a simple web API. No infrastructure is needed. Build private, SOC2-compliant AI applications instantly. Personalize models to your use case easily with our developer platform. Simply define the data you want to teach it and pick the base model - we take care of the rest. Put private LLMs into applications with a single API call, no more dealing with deployment, orchestration, or infrastructure hassles. The most powerful OSS model available—highly generalized capabilities with amazing narrative and reasoning capabilities. Harness a fully unlocked LLM to build the highest quality internal automation systems for your company.Starting Price: $0.0005 per 1,000 tokens -
50
PromptIDE
xAI
The xAI PromptIDE is an integrated development environment for prompt engineering and interpretability research. It accelerates prompt engineering through an SDK that allows implementing complex prompting techniques and rich analytics that visualize the network's outputs. We use it heavily in our continuous development of Grok. We developed the PromptIDE to give transparent access to Grok-1, the model that powers Grok, to engineers and researchers in the community. The IDE is designed to empower users and help them explore the capabilities of our large language models (LLMs) at pace. At the heart of the IDE is a Python code editor that - combined with a new SDK - allows implementing complex prompting techniques. While executing prompts in the IDE, users see helpful analytics such as the precise tokenization, sampling probabilities, alternative tokens, and aggregated attention masks. The IDE also offers quality of life features. It automatically saves all prompts.Starting Price: Free -
51
RagaAI
RagaAI
RagaAI is the #1 AI testing platform that helps enterprises mitigate AI risks and make their models secure and reliable. Reduce AI risk exposure across cloud or edge deployments and optimize MLOps costs with intelligent recommendations. A foundation model specifically designed to revolutionize AI testing. Easily identify the next steps to fix dataset and model issues. The AI-testing methods used by most today increase the time commitment and reduce productivity while building models. Also, they leave unforeseen risks, so they perform poorly post-deployment and thus waste both time and money for the business. We have built an end-to-end AI testing platform that helps enterprises drastically improve their AI development pipeline and prevent inefficiencies and risks post-deployment. 300+ tests to identify and fix every model, data, and operational issue, and accelerate AI development with comprehensive testing. -
52
Airtrain
Airtrain
Query and compare a large selection of open-source and proprietary models at once. Replace costly APIs with cheap custom AI models. Customize foundational models on your private data to adapt them to your particular use case. Small fine-tuned models can perform on par with GPT-4 and are up to 90% cheaper. Airtrain’s LLM-assisted scoring simplifies model grading using your task descriptions. Serve your custom models from the Airtrain API in the cloud or within your secure infrastructure. Evaluate and compare open-source and proprietary models across your entire dataset with custom properties. Airtrain’s powerful AI evaluators let you score models along arbitrary properties for a fully customized evaluation. Find out what model generates outputs compliant with the JSON schema required by your agents and applications. Your dataset gets scored across models with standalone metrics such as length, compression, coverage.Starting Price: Free -
53
Entry Point AI
Entry Point AI
Entry Point AI is the modern AI optimization platform for proprietary and open source language models. Manage prompts, fine-tunes, and evals all in one place. When you reach the limits of prompt engineering, it’s time to fine-tune a model, and we make it easy. Fine-tuning is showing a model how to behave, not telling. It works together with prompt engineering and retrieval-augmented generation (RAG) to leverage the full potential of AI models. Fine-tuning can help you to get better quality from your prompts. Think of it like an upgrade to few-shot learning that bakes the examples into the model itself. For simpler tasks, you can train a lighter model to perform at or above the level of a higher-quality model, greatly reducing latency and cost. Train your model not to respond in certain ways to users, for safety, to protect your brand, and to get the formatting right. Cover edge cases and steer model behavior by adding examples to your dataset.Starting Price: $49 per month -
54
NLP Lab
John Snow Labs
John Snow Labs' Generative AI Lab is a cutting-edge platform designed to empower enterprises with the ability to develop, customize, and deploy state-of-the-art generative AI models. The lab provides a robust, end-to-end solution that simplifies the integration of generative AI into business operations, making it accessible to organizations of all sizes and industries. The Generative AI Lab offers a no-code environment, allowing users to create sophisticated AI models without needing extensive programming expertise. This democratizes AI development, enabling business professionals, data scientists, and developers to collaboratively build and deploy models that can transform data into actionable insights. The platform is built on top of a rich ecosystem of pre-trained models, advanced NLP capabilities, and a comprehensive suite of tools that streamline the process of customizing AI for specific business needs. -
55
Maitai
Maitai
Maitai detects faults in AI output in real time, autocorrects bad output, and then builds more reliable, higher-performance models just for you. We build and fully manage your AI model stack, custom to your application. Reliable, fast, and cost-effective inference without all the headaches. Maitai detects faults in AI output and then takes corrective action before damage is done. Sleep well at night knowing your AI output follows your expectations. Never have a bad request. Maitai preemptively falls back to a secondary model when we detect issues (outages, degraded performance) with your primary model. We built Maitai to easily swap in over your existing provider. Start using Maitai on day 1 without disruptions. Bring your own keys or use ours. Maitai makes sure your model output matches your expectations. At the same time, we ensure requests never fail, and response times are consistent.Starting Price: $50 per month -
56
Composio
Composio
Composio is an integration platform designed to enhance AI agents and Large Language Models (LLMs) by providing seamless connections to over 150 tools with minimal code. It supports a wide array of agentic frameworks and LLM providers, facilitating function calling for efficient task execution. Composio offers a comprehensive repository of tools, including GitHub, Salesforce, file management systems, and code execution environments, enabling AI agents to perform diverse actions and subscribe to various triggers. The platform features managed authentication, allowing users to oversee authentication processes for all users and agents from a centralized dashboard. Composio's core capabilities include a developer-first integration approach, built-in authentication management, an expanding catalog of over 90 ready-to-connect tools, a 30% increase in reliability through simplified JSON structures and improved error handling, SOC Type II compliance ensuring maximum data security.Starting Price: $49 per month -
57
DagsHub
DagsHub
DagsHub is a collaborative platform designed for data scientists and machine learning engineers to manage and streamline their projects. It integrates code, data, experiments, and models into a unified environment, facilitating efficient project management and team collaboration. Key features include dataset management, experiment tracking, model registry, and data and model lineage, all accessible through a user-friendly interface. DagsHub supports seamless integration with popular MLOps tools, allowing users to leverage their existing workflows. By providing a centralized hub for all project components, DagsHub enhances transparency, reproducibility, and efficiency in machine learning development. DagsHub is a platform for AI and ML developers that lets you manage and collaborate on your data, models, and experiments, alongside your code. DagsHub was particularly designed for unstructured data for example text, images, audio, medical imaging, and binary files.Starting Price: $9 per month -
58
Databricks Data Intelligence Platform
Databricks
The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data. The winners in every industry will be data and AI companies. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals. Databricks combines generative AI with the unification benefits of a lakehouse to power a Data Intelligence Engine that understands the unique semantics of your data. This allows the Databricks Platform to automatically optimize performance and manage infrastructure in ways unique to your business. The Data Intelligence Engine understands your organization’s language, so search and discovery of new data is as easy as asking a question like you would to a coworker. -
59
Weights & Biases
Weights & Biases
Experiment tracking, hyperparameter optimization, model and dataset versioning with Weights & Biases (WandB). Track, compare, and visualize ML experiments with 5 lines of code. Add a few lines to your script, and each time you train a new version of your model, you'll see a new experiment stream live to your dashboard. Optimize models with our massively scalable hyperparameter search tool. Sweeps are lightweight, fast to set up, and plug in to your existing infrastructure for running models. Save every detail of your end-to-end machine learning pipeline — data preparation, data versioning, training, and evaluation. It's never been easier to share project updates. Quickly and easily implement experiment logging by adding just a few lines to your script and start logging results. Our lightweight integration works with any Python script. W&B Weave is here to help developers build and iterate on their AI applications with confidence. -
60
Polyaxon
Polyaxon
A Platform for reproducible and scalable Machine Learning and Deep Learning applications. Learn more about the suite of features and products that underpin today's most innovative platform for managing data science workflows. Polyaxon provides an interactive workspace with notebooks, tensorboards, visualizations,and dashboards. Collaborate with the rest of your team, share and compare experiments and results. Reproducible results with a built-in version control for code and experiments. Deploy Polyaxon in the cloud, on-premises or in hybrid environments, including single laptop, container management platforms, or on Kubernetes. Spin up or down, add more nodes, add more GPUs, and expand storage. -
61
Metaflow
Metaflow
Successful data science projects are delivered by data scientists who can build, improve, and operate end-to-end workflows independently, focusing more on data science, less on engineering. Use Metaflow with your favorite data science libraries, such as Tensorflow or SciKit Learn, and write your models in idiomatic Python code with not much new to learn. Metaflow also supports the R language. Metaflow helps you design your workflow, run it at scale, and deploy it to production. It versions and tracks all your experiments and data automatically. It allows you to inspect results easily in notebooks. Metaflow comes packaged with the tutorials, so getting started is easy. You can make copies of all the tutorials in your current directory using the metaflow command line interface. -
62
Arthur AI
Arthur
Track model performance to detect and react to data drift, improving model accuracy for better business outcomes. Build trust, ensure compliance, and drive more actionable ML outcomes with Arthur’s explainability and transparency APIs. Proactively monitor for bias, track model outcomes against custom bias metrics, and improve the fairness of your models. See how each model treats different population groups, proactively identify bias, and use Arthur's proprietary bias mitigation techniques. Arthur scales up and down to ingest up to 1MM transactions per second and deliver insights quickly. Actions can only be performed by authorized users. Individual teams/departments can have isolated environments with specific access control policies. Data is immutable once ingested, which prevents manipulation of metrics/insights. -
63
Jina AI
Jina AI
Empower businesses and developers to create cutting-edge neural search, generative AI, and multimodal services using state-of-the-art LMOps, MLOps and cloud-native technologies. Multimodal data is everywhere: from simple tweets to photos on Instagram, short videos on TikTok, audio snippets, Zoom meeting records, PDFs with figures, 3D meshes in games. It is rich and powerful, but that power often hides behind different modalities and incompatible data formats. To enable high-level AI applications, one needs to solve search and create first. Neural Search uses AI to find what you need. A description of a sunrise can match a picture, or a photo of a rose can match a song. Generative AI/Creative AI uses AI to make what you need. It can create an image from a description, or write poems from a picture. -
64
Qdrant
Qdrant
Qdrant is a vector similarity engine & vector database. It deploys as an API service providing search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more! Provides the OpenAPI v3 specification to generate a client library in almost any programming language. Alternatively utilise ready-made client for Python or other programming languages with additional functionality. Implement a unique custom modification of the HNSW algorithm for Approximate Nearest Neighbor Search. Search with a State-of-the-Art speed and apply search filters without compromising on results. Support additional payload associated with vectors. Not only stores payload but also allows filter results based on payload values. -
65
Dify
Dify
Dify is an open-source platform designed to streamline the development and operation of generative AI applications. It offers a comprehensive suite of tools, including an intuitive orchestration studio for visual workflow design, a Prompt IDE for prompt testing and refinement, and enterprise-level LLMOps capabilities for monitoring and optimizing large language models. Dify supports integration with various LLMs, such as OpenAI's GPT series and open-source models like Llama, providing flexibility for developers to select models that best fit their needs. Additionally, its Backend-as-a-Service (BaaS) features enable seamless incorporation of AI functionalities into existing enterprise systems, facilitating the creation of AI-powered chatbots, document summarization tools, and virtual assistants. -
66
Bruinen
Bruinen
Bruinen enables your platform to validate and connect your users’ profiles from across the internet. We offer simple integration with a variety of data sources, including Google, GitHub, and many more. Connect to the data you need and take action on one platform. Our API takes care of the auth, permissions, and rate limits - reducing complexity and increasing efficiency, allowing you to iterate quickly and stay focused on your core product. Allow users to confirm an action via email, SMS, or a magic-link before the action occurs. Let your users customize the actions they want to confirm, all with a pre-built permissions UI. Bruinen offers an easy-to-use, consistent interface to access your users’ profiles. You can connect, authenticate, and pull data from those accounts all from Bruinen’s platform. -
67
dstack
dstack
It streamlines development and deployment, reduces cloud costs, and frees users from vendor lock-in. Configure the hardware resources, such as GPU, and memory, and specify your preference for using spot instances. dstack automatically provisions cloud resources, fetches your code, and forwards ports for secure access. Access the cloud dev environment conveniently using your local desktop IDE. Configure the hardware resources you need (GPU, memory, etc.) and indicate whether you want to use spot or on-demand instances. dstack will automatically provision cloud resources and forward ports for secure and convenient access. Pre-train and finetune your own state-of-the-art models easily and cost-effectively in any cloud. Have cloud resources automatically provisioned based on your configuration? Access your data and store output artifacts using declarative configuration or the Python SDK. -
68
LangSmith
LangChain
Unexpected results happen all the time. With full visibility into the entire chain sequence of calls, you can spot the source of errors and surprises in real time with surgical precision. Software engineering relies on unit testing to build performant, production-ready applications. LangSmith provides that same functionality for LLM applications. Spin up test datasets, run your applications over them, and inspect results without having to leave LangSmith. LangSmith enables mission-critical observability with only a few lines of code. LangSmith is designed to help developers harness the power–and wrangle the complexity–of LLMs. We’re not only building tools. We’re establishing best practices you can rely on. Build and deploy LLM applications with confidence. Application-level usage stats. Feedback collection. Filter traces, cost and performance measurement. Dataset curation, compare chain performance, AI-assisted evaluation, and embrace best practices. -
69
Vellum AI
Vellum
Bring LLM-powered features to production with tools for prompt engineering, semantic search, version control, quantitative testing, and performance monitoring. Compatible across all major LLM providers. Quickly develop an MVP by experimenting with different prompts, parameters, and even LLM providers to quickly arrive at the best configuration for your use case. Vellum acts as a low-latency, highly reliable proxy to LLM providers, allowing you to make version-controlled changes to your prompts – no code changes needed. Vellum collects model inputs, outputs, and user feedback. This data is used to build up valuable testing datasets that can be used to validate future changes before they go live. Dynamically include company-specific context in your prompts without managing your own semantic search infra. -
70
Neum AI
Neum AI
No one wants their AI to respond with out-of-date information to a customer. Neum AI helps companies have accurate and up-to-date context in their AI applications. Use built-in connectors for data sources like Amazon S3 and Azure Blob Storage, vector stores like Pinecone and Weaviate to set up your data pipelines in minutes. Supercharge your data pipeline by transforming and embedding your data with built-in connectors for embedding models like OpenAI and Replicate, and serverless functions like Azure Functions and AWS Lambda. Leverage role-based access controls to make sure only the right people can access specific vectors. Bring your own embedding models, vector stores and sources. Ask us about how you can even run Neum AI in your own cloud. -
71
baioniq
Quantiphi
Generative AI and Large Language Models (LLMs) present a promising solution to unlock the untapped value of unstructured data, providing enterprises with instant access to valuable insights. This has opened up new possibilities for businesses to reimagine customer experience, products, and services, and increase productivity for their teams. baioniq is Quantiphi's enterprise-ready Generative AI Platform on AWS is designed to help organizations rapidly onboard generative AI capabilities and apply them to domain-specific tasks. For AWS customers, baioniq is containerized and deployed on AWS. It provides a modular solution that allows modern enterprises to fine-tune LLMs to incorporate domain-specific data and perform enterprise-specific tasks in four simple steps. -
72
Lakera
Lakera
Lakera Guard empowers organizations to build GenAI applications without worrying about prompt injections, data loss, harmful content, and other LLM risks. Powered by the world's most advanced AI threat intelligence. Lakera’s threat intelligence database contains tens of millions of attack data points and is growing by 100k+ entries every day. With Lakera guard, your defense continuously strengthens. Lakera guard embeds industry-leading security intelligence at the heart of your LLM applications so that you can build and deploy secure AI systems at scale. We observe tens of millions of attacks to detect and protect you from undesired behavior and data loss caused by prompt injection. Continuously assess, track, report, and responsibly manage your AI systems across the organization to ensure they are secure at all times. -
73
Deasie
Deasie
You can't build good models with bad data. More than 80% of today’s data is unstructured (e.g., documents, reports, text, images). For language models, it is critical to understand what parts of this data are relevant, outdated, inconsistent, and safe to use. Failure to do so leads to unsafe and unreliable adoption of AI. -
74
Second State
Second State
Fast, lightweight, portable, rust-powered, and OpenAI compatible. We work with cloud providers, especially edge cloud/CDN compute providers, to support microservices for web apps. Use cases include AI inference, database access, CRM, ecommerce, workflow management, and server-side rendering. We work with streaming frameworks and databases to support embedded serverless functions for data filtering and analytics. The serverless functions could be database UDFs. They could also be embedded in data ingest or query result streams. Take full advantage of the GPUs, write once, and run anywhere. Get started with the Llama 2 series of models on your own device in 5 minutes. Retrieval-argumented generation (RAG) is a very popular approach to building AI agents with external knowledge bases. Create an HTTP microservice for image classification. It runs YOLO and Mediapipe models at native GPU speed. -
75
Lasso Security
Lasso Security
But it’s pretty wild out there, with new cyber threats evolving as we speak. Lasso Security enables you to safely harness AI Large Language Model (LLM) technology and embrace progress, without compromising security. We’re focused exclusively on LLM security issues. This technology is in our DNA, right down to our code. Our solution lassos external threats, and internal errors that lead to exposure, going beyond traditional methods. A majority of organizations are now dedicating resources to LLM adoption. But very few are taking the time to address vulnerabilities and risks - either the ones we know about, or the ones coming over the horizon. -
76
Gantry
Gantry
Get the full picture of your model's performance. Log inputs and outputs and seamlessly enrich them with metadata and user feedback. Figure out how your model is really working, and where you can improve. Monitor for errors and discover underperforming cohorts and use cases. The best models are built on user data. Programmatically gather unusual or underperforming examples to retrain your model. Stop manually reviewing thousands of outputs when changing your prompt or model. Evaluate your LLM-powered apps programmatically. Detect and fix degradations quickly. Monitor new deployments in real-time and seamlessly edit the version of your app your users interact with. Connect your self-hosted or third-party model and your existing data sources. Process enterprise-scale data with our serverless streaming dataflow engine. Gantry is SOC-2 compliant and built with enterprise-grade authentication. -
77
UpTrain
UpTrain
Get scores for factual accuracy, context retrieval quality, guideline adherence, tonality, and many more. You can’t improve what you can’t measure. UpTrain continuously monitors your application's performance on multiple evaluation criterions and alerts you in case of any regressions with automatic root cause analysis. UpTrain enables fast and robust experimentation across multiple prompts, model providers, and custom configurations, by calculating quantitative scores for direct comparison and optimal prompt selection. Hallucinations have plagued LLMs since their inception. By quantifying degree of hallucination and quality of retrieved context, UpTrain helps to detect responses with low factual accuracy and prevent them before serving to the end-users. -
78
WhyLabs
WhyLabs
Enable observability to detect data and ML issues faster, deliver continuous improvements, and avoid costly incidents. Start with reliable data. Continuously monitor any data-in-motion for data quality issues. Pinpoint data and model drift. Identify training-serving skew and proactively retrain. Detect model accuracy degradation by continuously monitoring key performance metrics. Identify risky behavior in generative AI applications and prevent data leakage. Protect your generative AI applications are safe from malicious actions. Improve AI applications through user feedback, monitoring, and cross-team collaboration. Integrate in minutes with purpose-built agents that analyze raw data without moving or duplicating it, ensuring privacy and security. Onboard the WhyLabs SaaS Platform for any use cases using the proprietary privacy-preserving integration. Security approved for healthcare and banks. -
79
Martian
Martian
By using the best-performing model for each request, we can achieve higher performance than any single model. Martian outperforms GPT-4 across OpenAI's evals (open/evals). We turn opaque black boxes into interpretable representations. Our router is the first tool built on top of our model mapping method. We are developing many other applications of model mapping including turning transformers from indecipherable matrices into human-readable programs. If a company experiences an outage or high latency period, automatically reroute to other providers so your customers never experience any issues. Determine how much you could save by using the Martian Model Router with our interactive cost calculator. Input your number of users, tokens per session, and sessions per month, and specify your cost/quality tradeoff. -
80
Arcee AI
Arcee AI
Optimizing continual pre-training for model enrichment with proprietary data. Ensuring that domain-specific models offer a smooth experience. Creating a production-friendly RAG pipeline that offers ongoing support. With Arcee's SLM Adaptation system, you do not have to worry about fine-tuning, infrastructure set-up, and all the other complexities involved in stitching together solutions using a plethora of not-built-for-purpose tools. Thanks to the domain adaptability of our product, you can efficiently train and deploy your own SLMs across a plethora of use cases, whether it is for internal tooling, or for your customers. By training and deploying your SLMs with Arcee’s end-to-end VPC service, you can rest assured that what is yours, stays yours. -
81
Freeplay
Freeplay
Freeplay gives product teams the power to prototype faster, test with confidence, and optimize features for customers, take control of how you build with LLMs. A better way to build with LLMs. Bridge the gap between domain experts & developers. Prompt engineering, testing & evaluation tools for your whole team. -
82
Keywords AI
Keywords AI
Keywords AI is the leading LLM monitoring platform for AI startups. Thousands of engineers use Keywords AI to get complete LLM observability and user analytics. With 1 line of code change, you can easily integrate 200+ LLMs into your codebase. Keywords AI allows you to monitor, test, and improve your AI apps with minimal effort.Starting Price: $0/month -
83
Seekr
Seekr
Boost your productivity and create more inspired content with generative AI that is bounded and grounded by the highest industry standards and intelligence. Rate content for reliability, reveal political lean, and align with your brand’s safety themes. Our AI models are rigorously tested and reviewed by leading experts and data scientists to train our dataset exclusively with the web’s most trustworthy content. Leverage the industry’s most trustworthy large language model (LLM) to create new content fast, accurately, and at low cost. Speed up processes and drive better business outcomes with a suite of AI tools built to reduce costs and skyrocket results. -
84
LM Studio
LM Studio
Use models through the in-app Chat UI or an OpenAI-compatible local server. Minimum requirements: M1/M2/M3 Mac, or a Windows PC with a processor that supports AVX2. Linux is available in beta. One of the main reasons for using a local LLM is privacy, and LM Studio is designed for that. Your data remains private and local to your machine. You can use LLMs you load within LM Studio via an API server running on localhost. -
85
EvalsOne
EvalsOne
An intuitive yet comprehensive evaluation platform to iteratively optimize your AI-driven products. Streamline LLMOps workflow, build confidence, and gain a competitive edge. EvalsOne is your all-in-one toolbox for optimizing your application evaluation process. Imagine a Swiss Army knife for AI, equipped to tackle any evaluation scenario you throw its way. Suitable for crafting LLM prompts, fine-tuning RAG processes, and evaluating AI agents. Choose from rule-based or LLM-based approaches to automate the evaluation process. Integrate human evaluation seamlessly, leveraging the power of expert judgment. Applicable to all LLMOps stages from development to production environments. EvalsOne provides an intuitive process and interface, that empowers teams across the AI lifecycle, from developers to researchers and domain experts. Easily create evaluation runs and organize them in levels. Quickly iterate and perform in-depth analysis through forked runs. -
86
Contextual.ai
Contextual AI
Customize contextual language models for your enterprise use case. Unlock your team’s full potential with RAG 2.0, the most accurate, reliable, and auditable way to build production-grade AI systems. We pre-train, fine-tune, and align all components as a single integrated system to achieve production-level performance so you can build and customize specialized enterprise AI applications for your use cases. The contextual language model system is end-to-end optimized. Our models are optimized end-to-end for both retrieval and generation so your users get the accurate answers they need. Our cutting-edge fine-tuning techniques customize our models to your data and guidelines, increasing the value of your business. Our platform has lightweight built-in mechanisms for quickly incorporating user feedback. Our research focuses on developing highly accurate and reliable models that deeply understand context. -
87
Ottic
Ottic
Empower tech and non-technical teams to test your LLM apps and ship reliable products faster. Accelerate the LLM app development cycle in up to 45 days. Empower tech and non-technical teams through a collaborative and friendly UI. Gain full visibility into your LLM application's behavior with comprehensive test coverage. Ottic connects with the tools your QA and engineers use every day, right out of the box. Cover any real-world scenario and build a comprehensive test suite. Break down test cases into granular test steps and detect regressions in your LLM product. Get rid of hardcoded prompts. Create, manage, and track prompts effortlessly. Bridge the gap between technical and non-technical team members, ensuring seamless collaboration in prompt engineering. Run tests by sampling and optimize your budget. Drill down on what went wrong to produce more reliable LLM apps. Gain direct visibility into how users interact with your app in real-time. -
88
Simplismart
Simplismart
Fine-tune and deploy AI models with Simplismart's fastest inference engine. Integrate with AWS/Azure/GCP and many more cloud providers for simple, scalable, cost-effective deployment. Import open source models from popular online repositories or deploy your own custom model. Leverage your own cloud resources or let Simplismart host your model. With Simplismart, you can go far beyond AI model deployment. You can train, deploy, and observe any ML model and realize increased inference speeds at lower costs. Import any dataset and fine-tune open-source or custom models rapidly. Run multiple training experiments in parallel efficiently to speed up your workflow. Deploy any model on our endpoints or your own VPC/premise and see greater performance at lower costs. Streamlined and intuitive deployment is now a reality. Monitor GPU utilization and all your node clusters in one dashboard. Detect any resource constraints and model inefficiencies on the go. -
89
Byne
Byne
Retrieval-augmented generation, agents, and more start building in the cloud and deploying on your server. We charge a flat fee per request. There are two types of requests: document indexation and generation. Document indexation is the addition of a document to your knowledge base. Document indexation, which is the addition of a document to your knowledge base and generation, which creates LLM writing based on your knowledge base RAG. Build a RAG workflow by deploying off-the-shelf components and prototype a system that works for your case. We support many auxiliary features, including reverse tracing of output to documents, and ingestion for many file formats. Enable the LLM to use tools by leveraging Agents. An Agent-powered system can decide which data it needs and search for it. Our implementation of agents provides a simple hosting for execution layers and pre-build agents for many use cases.Starting Price: 2¢ per generation request -
90
Mirascope
Mirascope
Mirascope is an open-source library built on Pydantic 2.0 for the most clean, and extensible prompt management and LLM application building experience. Mirascope is a powerful, flexible, and user-friendly library that simplifies the process of working with LLMs through a unified interface that works across various supported providers, including OpenAI, Anthropic, Mistral, Gemini, Groq, Cohere, LiteLLM, Azure AI, Vertex AI, and Bedrock. Whether you're generating text, extracting structured information, or developing complex AI-driven agent systems, Mirascope provides the tools you need to streamline your development process and create powerful, robust applications. Response models in Mirascope allow you to structure and validate the output from LLMs. This feature is particularly useful when you need to ensure that the LLM's response adheres to a specific format or contains certain fields. -
91
Snorkel AI
Snorkel AI
AI today is blocked by lack of labeled data, not models. Unblock AI with the first data-centric AI development platform powered by a programmatic approach. Snorkel AI is leading the shift from model-centric to data-centric AI development with its unique programmatic approach. Save time and costs by replacing manual labeling with rapid, programmatic labeling. Adapt to changing data or business goals by quickly changing code, not manually re-labeling entire datasets. Develop and deploy high-quality AI models via rapid, guided iteration on the part that matters–the training data. Version and audit data like code, leading to more responsive and ethical deployments. Incorporate subject matter experts' knowledge by collaborating around a common interface, the data needed to train models. Reduce risk and meet compliance by labeling programmatically and keeping data in-house, not shipping to external annotators. -
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Omni AI
Omni AI
Omni is a powerful AI framework allowing you to connect Prompts, Tools and customized logic to LLM Agents. Agents are built upon the ReAct paradigm (Reason + Act) and allow LLM models to engage with a multitude of tools and custom components to accomplish a task. Automate customer support, document processing, lead qualification, and more. You can seamlessly switch between prompts and LLM architectures to optimize performance. We host your workflows as APIs so that you can access AI instantly. -
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CalypsoAI
CalypsoAI
Customizable content scanners ensure any confidential and sensitive data or intellectual property included in a prompt never leaves your organization. Responses from LLMs are scanned for code written in a wide variety of languages and responses containing it are prevented from gaining access to your system. Scanners deploy a wide array of techniques to identify and stop prompts that attempt to circumvent systematic and organizational parameters for LLM activity. in-house subject matter experts ensures your teams use information provided by LLMs with confidence. Don't let fear of falling victim to the vulnerabilities inherent in large language models hinder your organization's ability to gain a competitive advantage. -
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LLMCurator
LLMCurator
Teams use LLMCurator to annotate data, interact with LLM, and share results. Edit the model's response when needed to create higher-quality data. Annotate your text dataset by giving prompts and then export and process the response. -
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impaction.ai
Coxwave
Discover. Analyze. Optimize. Use [impaction.ai]'s intuitive semantic search to effortlessly sift through conversational data. Just type 'find me conversations where...' and let our engine do the rest. Meet Columbus, your intelligent data co-pilot. Columbus analyzes conversations, highlights key trends, and even recommends which dialogues deserve your attention. Armed with these insights, take data-driven actions to enhance user engagement and build a smarter, more responsive AI product. Columbus not only tells you what's happening but also suggests how to make it better. -
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TorqCloud
IntelliBridge
TorqCloud is designed to help users source, move, enrich, visualize, secure, and interact with data via AI agents. As a comprehensive AIOps solution, TorqCloud allows users to build or integrate end-to-end custom LLM applications using a low-code interface. Built to handle vast amounts of data to deliver actionable insights as a critical tool for any organization looking to stay competitive in today’s digital landscape. Our approach combines seamless integration across disciplines, an intense focus on user needs, test-and-learn methodologies that enable us to get the right product to market fast, and a close working relationship with your teams, including skills transfer and training. Starting with empathy interviews we perform stakeholder mapping exercises where we dive into the customer journey, needed behavioral changes, problem sizing, and linear unpacking. -
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FinetuneDB
FinetuneDB
Capture production data, evaluate outputs collaboratively, and fine-tune your LLM's performance. Know exactly what goes on in production with an in-depth log overview. Collaborate with product managers, domain experts and engineers to build reliable model outputs. Track AI metrics such as speed, quality scores, and token usage. Copilot automates evaluations and model improvements for your use case. Create, manage, and optimize prompts to achieve precise and relevant interactions between users and AI models. Compare foundation models, and fine-tuned versions to improve prompt performance and save tokens. Collaborate with your team to build a proprietary fine-tuning dataset for your AI models. Build custom fine-tuning datasets to optimize model performance for specific use cases. -
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Astra Platform
Astra Platform
A single line of code to supercharge your LLM with integrations and without complex JSON schemas. Spend minutes, not days adding integrations to your LLM. With only a few lines of code, the LLM can perform any action in any target app on behalf of the user. 2,200 out-of-the-box integrations. Connect with Google Calendar, Gmail, Hubspot, Salesforce or more. Manage authentication profiles so your LLM can perform actions on behalf of your users. Build REST integrations or easily import from a OpenAPI spec. Function calling requires the foundation model to be fine-tuned which can be expensive and diminish the quality of your output. Enable function calling with any LLM, even if it's not natively supported. With Astra, you can build a seamless layer of integrations and function execution on top of your LLM, extending its capabilities without altering its core structure. Automatically generate LLM-optimized field descriptions. -
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ConfidentialMind
ConfidentialMind
We've done the work of bundling and pre-configuring all the components you need for building solutions and integrating LLMs directly into your business processes. With ConfidentialMind you can jump right into action. Deploys an endpoint for the most powerful open source LLMs like Llama-2, turning it into an internal LLM API. Imagine ChatGPT in your very own cloud. This is the most secure solution possible. Connects the rest of the stack with the APIs of the largest hosted LLM providers like Azure OpenAI, AWS Bedrock, or IBM. ConfidentialMind deploys a playground UI based on Streamlit with a selection of LLM-powered productivity tools for your company such as writing assistants and document analysts. Includes a vector database, critical components for the most common LLM applications for shifting through massive knowledge bases with thousands of documents efficiently. Allows you to control the access to the solutions your team builds and what data the LLMs have access to. -
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Adaline
Adaline
Iterate quickly and ship confidently. Confidently ship by evaluating your prompts with a suite of evals like context recall, llm-rubric (LLM as a judge), latency, and more. Let us handle intelligent caching and complex implementations to save you time and money. Quickly iterate on your prompts in a collaborative playground that supports all the major providers, variables, automatic versioning, and more. Easily build datasets from real data using Logs, upload your own as a CSV, or collaboratively build and edit within your Adaline workspace. Track usage, latency, and other metrics to monitor the health of your LLMs and the performance of your prompts using our APIs. Continuously evaluate your completions in production, see how your users are using your prompts, and create datasets by sending logs using our APIs. The single platform to iterate, evaluate, and monitor LLMs. Easily rollbacks if your performance regresses in production, and see how your team iterated the prompt. -
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Chainlit
Chainlit
Chainlit is an open-source Python package designed to expedite the development of production-ready conversational AI applications. With Chainlit, developers can build and deploy chat-based interfaces in minutes, not weeks. The platform offers seamless integration with popular AI tools and frameworks, including OpenAI, LangChain, and LlamaIndex, allowing for versatile application development. Key features of Chainlit include multimodal capabilities, enabling the processing of images, PDFs, and other media types to enhance productivity. It also provides robust authentication options, supporting integration with providers like Okta, Azure AD, and Google. The Prompt Playground feature allows developers to iterate on prompts in context, adjusting templates, variables, and LLM settings for optimal results. For observability, Chainlit offers real-time visualization of prompts, completions, and usage metrics, ensuring efficient and trustworthy LLM operations.
LLMOps Guide
LLMOps, an abbreviation for Large Language Model Operations, represents a specialized domain within MLOps that concentrates on the operational aspects and infrastructure required for refining and deploying existing foundational models.
LLMs, or Large Language Models, are deep learning models capable of generating human-like language outputs. With billions of parameters and training on vast amounts of textual data, they possess immense power but also present complex management challenges.
The scope of LLMOps encompasses various areas, including:
- Data Governance: The management of data plays a crucial role in training and fine-tuning LLMs. It necessitates meticulous handling to ensure data quality and accessibility for the models whenever required.
- Model Advancement: LLMs can undergo fine-tuning for diverse tasks. Consequently, a systematic process is essential to develop and evaluate different models, aiming to identify the most optimal one for specific tasks.
- Scalable Deployment: Effectively deploying LLMs demands a scalable and reliable infrastructure capable of accommodating the resource-intensive nature of large language models.
- Performance Monitoring: Continuous monitoring of LLMs is necessary to ensure their adherence to expected performance standards. This encompasses aspects such as accuracy, latency, and bias detection.
LLMOps is a rapidly evolving field due to the increasing power and prevalence of LLMs. The growing adoption of these models further emphasizes the need for expertise in LLMOps.
Outlined below are some of the challenges encountered in LLMOps:
- Data Governance: Managing the abundance of training and fine-tuning data for LLMs while upholding quality standards and accessibility remains a significant challenge.
- Model Advancement: The process of developing and evaluating various LLMs for specific tasks can be intricate and demanding.
- Scalable Deployment: Establishing a deployment infrastructure that efficiently accommodates the demanding nature of large language models while ensuring scalability and reliability poses a notable challenge.
- Performance Monitoring: Consistently monitoring LLMs is vital to confirm their performance aligns with expectations. This involves evaluating accuracy, latency, and mitigating bias.
Furthermore, LLMOps offers several benefits:
- Enhanced Accuracy: By ensuring high-quality data for training and deploying LLMs in a scalable and reliable manner, LLMOps contributes to improving the accuracy of these models.
- Reduced Latency: Efficient deployment techniques facilitated by LLMOps can lead to reduced latency in LLMs, enabling them to quickly access the required data.
- Increased Fairness: LLMOps endeavors to mitigate bias within LLMs, ensuring fair treatment and preventing discrimination against specific groups.
As the power and adoption of LLMs continue to surge, the significance of LLMOps expertise will grow in parallel. This dynamic field remains in a constant state of evolution.