Compare the Top LLMOps Tools using the curated list below to find the Best LLMOps Tools for your needs.
-
1
Vertex AI
Google
382 RatingsFully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case. Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection. -
2
OpenAI's mission, which is to ensure artificial general intelligence (AGI), benefits all people. This refers to highly autonomous systems that outperform humans in most economically valuable work. While we will try to build safe and useful AGI, we will also consider our mission accomplished if others are able to do the same. Our API can be used to perform any language task, including summarization, sentiment analysis and content generation. You can specify your task in English or use a few examples. Our constantly improving AI technology is available to you with a simple integration. These sample completions will show you how to integrate with the API.
-
3
Langfuse is a free and open-source LLM engineering platform that helps teams to debug, analyze, and iterate their LLM Applications. Observability: Incorporate Langfuse into your app to start ingesting traces. Langfuse UI : inspect and debug complex logs, user sessions and user sessions Langfuse Prompts: Manage versions, deploy prompts and manage prompts within Langfuse Analytics: Track metrics such as cost, latency and quality (LLM) to gain insights through dashboards & data exports Evals: Calculate and collect scores for your LLM completions Experiments: Track app behavior and test it before deploying new versions Why Langfuse? - Open source - Models and frameworks are agnostic - Built for production - Incrementally adaptable - Start with a single LLM or integration call, then expand to the full tracing for complex chains/agents - Use GET to create downstream use cases and export the data
-
4
BenchLLM allows you to evaluate your code in real-time. Create test suites and quality reports for your models. Choose from automated, interactive, or custom evaluation strategies. We are a group of engineers who enjoy building AI products. We don't want a compromise between the power, flexibility and predictability of AI. We have created the open and flexible LLM tool that we always wanted. CLI commands are simple and elegant. Use the CLI to test your CI/CD pipeline. Monitor model performance and detect regressions during production. Test your code in real-time. BenchLLM supports OpenAI (Langchain), and any other APIs out of the box. Visualize insightful reports and use multiple evaluation strategies.
-
5
With just a few lines, you can integrate natural language understanding and generation into the product. The Cohere API allows you to access models that can read billions upon billions of pages and learn the meaning, sentiment, intent, and intent of every word we use. You can use the Cohere API for human-like text. Simply fill in a prompt or complete blanks. You can create code, write copy, summarize text, and much more. Calculate the likelihood of text, and retrieve representations from your model. You can filter text using the likelihood API based on selected criteria or categories. You can create your own downstream models for a variety of domain-specific natural languages tasks by using representations. The Cohere API is able to compute the similarity of pieces of text and make categorical predictions based on the likelihood of different text options. The model can see ideas through multiple lenses so it can identify abstract similarities between concepts as distinct from DNA and computers.
-
6
ClearML
ClearML
$15ClearML is an open-source MLOps platform that enables data scientists, ML engineers, and DevOps to easily create, orchestrate and automate ML processes at scale. Our frictionless and unified end-to-end MLOps Suite allows users and customers to concentrate on developing ML code and automating their workflows. ClearML is used to develop a highly reproducible process for end-to-end AI models lifecycles by more than 1,300 enterprises, from product feature discovery to model deployment and production monitoring. You can use all of our modules to create a complete ecosystem, or you can plug in your existing tools and start using them. ClearML is trusted worldwide by more than 150,000 Data Scientists, Data Engineers and ML Engineers at Fortune 500 companies, enterprises and innovative start-ups. -
7
Lyzr
Lyzr AI
$0 per monthLyzr is an enterprise Generative AI company that offers private and secure AI Agent SDKs and an AI Management System. Lyzr helps enterprises build, launch and manage secure GenAI applications, in their AWS cloud or on-prem infra. No more sharing sensitive data with SaaS platforms or GenAI wrappers. And no more reliability and integration issues of open-source tools. Differentiating from competitors such as Cohere, Langchain, and LlamaIndex, Lyzr.ai follows a use-case-focused approach, building full-service yet highly customizable SDKs, simplifying the addition of LLM capabilities to enterprise applications. Featuring low-code LLM SDKs, Lyzr empowers users to customize nearly 100 parameters with minimal coding, significantly reducing deployment time. Lyzr's extensive partner network, including alliances with AWS, Snowflake, and collaborations with emerging LLM companies like Weaviate, BrevDev solidifies our position in the enterprise Generative AI arena. The Lyzr Enterprise Hub further enhances our offering, providing a centralized platform for managing SDKs, LLM requests and GenAI applications, complete with detailed analytics and monitoring tools. -
8
Valohai
Valohai
$560 per monthPipelines are permanent, models are temporary. Train, Evaluate, Deploy, Repeat. Valohai is the only MLOps platform to automate everything, from data extraction to model deployment. Automate everything, from data extraction to model installation. Automatically store every model, experiment, and artifact. Monitor and deploy models in a Kubernetes cluster. Just point to your code and hit "run". Valohai launches workers and runs your experiments. Then, Valohai shuts down the instances. You can create notebooks, scripts, or shared git projects using any language or framework. Our API allows you to expand endlessly. Track each experiment and trace back to the original training data. All data can be audited and shared. -
9
Amazon SageMaker
Amazon
Amazon SageMaker, a fully managed service, provides data scientists and developers with the ability to quickly build, train, deploy, and deploy machine-learning (ML) models. SageMaker takes the hard work out of each step in the machine learning process, making it easier to create high-quality models. Traditional ML development can be complex, costly, and iterative. This is made worse by the lack of integrated tools to support the entire machine learning workflow. It is tedious and error-prone to combine tools and workflows. SageMaker solves the problem by combining all components needed for machine learning into a single toolset. This allows models to be produced faster and with less effort. Amazon SageMaker Studio is a web-based visual interface that allows you to perform all ML development tasks. SageMaker Studio allows you to have complete control over each step and gives you visibility. -
10
Qwak
Qwak
Qwak build system allows data scientists to create an immutable, tested production-grade artifact by adding "traditional" build processes. Qwak build system standardizes a ML project structure that automatically versions code, data, and parameters for each model build. Different configurations can be used to build different builds. It is possible to compare builds and query build data. You can create a model version using remote elastic resources. Each build can be run with different parameters, different data sources, and different resources. Builds create deployable artifacts. Artifacts built can be reused and deployed at any time. Sometimes, however, it is not enough to deploy the artifact. Qwak allows data scientists and engineers to see how a build was made and then reproduce it when necessary. Models can contain multiple variables. The data models were trained using the hyper parameter and different source code. -
11
Hugging Face
Hugging Face
$9 per monthAutoTrain is a new way to automatically evaluate, deploy and train state-of-the art Machine Learning models. AutoTrain, seamlessly integrated into the Hugging Face ecosystem, is an automated way to develop and deploy state of-the-art Machine Learning model. Your account is protected from all data, including your training data. All data transfers are encrypted. Today's options include text classification, text scoring and entity recognition. Files in CSV, TSV, or JSON can be hosted anywhere. After training is completed, we delete all training data. Hugging Face also has an AI-generated content detection tool. -
12
Comet
Comet
$179 per user per monthManage and optimize models throughout the entire ML lifecycle. This includes experiment tracking, monitoring production models, and more. The platform was designed to meet the demands of large enterprise teams that deploy ML at scale. It supports any deployment strategy, whether it is private cloud, hybrid, or on-premise servers. Add two lines of code into your notebook or script to start tracking your experiments. It works with any machine-learning library and for any task. To understand differences in model performance, you can easily compare code, hyperparameters and metrics. Monitor your models from training to production. You can get alerts when something is wrong and debug your model to fix it. You can increase productivity, collaboration, visibility, and visibility among data scientists, data science groups, and even business stakeholders. -
13
Confident AI
Confident AI
$39/month Confident AI is used by companies of all sizes to prove that their LLM is worth being in production. On a single, central platform, you can evaluate your LLM workflow. Deploy LLM with confidence to ensure substantial benefits, and address any weaknesses within your LLM implementation. Provide ground truths to serve as benchmarks for evaluating your LLM stack. Ensure alignment with predefined output expectation, while identifying areas that need immediate refinement and adjustments. Define ground facts to ensure that your LLM behaves as expected. Advanced diff tracking for iterating towards the optimal LLM stack. We guide you through the process of selecting the right knowledge bases, altering the prompt templates and selecting the best configurations for your use case. Comprehensive analytics to identify focus areas. Use out-of-the box observability to identify use cases that will bring the greatest ROI for your organization. Use metric insights to reduce LLM costs and delays over time. -
14
Klu
Klu
$97Klu.ai, a Generative AI Platform, simplifies the design, deployment, and optimization of AI applications. Klu integrates your Large Language Models and incorporates data from diverse sources to give your applications unique context. Klu accelerates the building of applications using language models such as Anthropic Claude (Azure OpenAI), GPT-4 (Google's GPT-4), and over 15 others. It allows rapid prompt/model experiments, data collection and user feedback and model fine tuning while cost-effectively optimising performance. Ship prompt generation, chat experiences and workflows in minutes. Klu offers SDKs for all capabilities and an API-first strategy to enable developer productivity. Klu automatically provides abstractions to common LLM/GenAI usage cases, such as: LLM connectors and vector storage, prompt templates, observability and evaluation/testing tools. -
15
Athina AI
Athina AI
$50 per monthMonitor your LLMs during production and discover and correct hallucinations and errors related to accuracy and quality with LLM outputs. Check your outputs to see if they contain hallucinations, misinformation or other issues. Configurable for any LLM application. Segment data to analyze in depth your cost, accuracy and response times. To debug generation, you can search, sort and filter your inference calls and trace your queries, retrievals and responses. Explore your conversations to learn what your users feel and what they are saying. You can also find out which conversations were unsuccessful. Compare your performance metrics between different models and prompts. Our insights will guide you to the best model for each use case. Our evaluators analyze and improve the outputs by using your data, configurations and feedback. -
16
BentoML
BentoML
FreeYour ML model can be served in minutes in any cloud. Unified model packaging format that allows online and offline delivery on any platform. Our micro-batching technology allows for 100x more throughput than a regular flask-based server model server. High-quality prediction services that can speak the DevOps language, and seamlessly integrate with common infrastructure tools. Unified format for deployment. High-performance model serving. Best practices in DevOps are incorporated. The service uses the TensorFlow framework and the BERT model to predict the sentiment of movie reviews. DevOps-free BentoML workflow. This includes deployment automation, prediction service registry, and endpoint monitoring. All this is done automatically for your team. This is a solid foundation for serious ML workloads in production. Keep your team's models, deployments and changes visible. You can also control access via SSO and RBAC, client authentication and auditing logs. -
17
Anyscale
Anyscale
Ray's creators have created a fully-managed platform. The best way to create, scale, deploy, and maintain AI apps on Ray. You can accelerate development and deployment of any AI app, at any scale. Ray has everything you love, but without the DevOps burden. Let us manage Ray for you. Ray is hosted on our cloud infrastructure. This allows you to focus on what you do best: creating great products. Anyscale automatically scales your infrastructure to meet the dynamic demands from your workloads. It doesn't matter if you need to execute a production workflow according to a schedule (e.g. Retraining and updating a model with new data every week or running a highly scalable, low-latency production service (for example. Anyscale makes it easy for machine learning models to be served in production. Anyscale will automatically create a job cluster and run it until it succeeds. -
18
Vald
Vald
FreeVald is a distributed, fast, dense and highly scalable vector search engine that approximates nearest neighbors. Vald was designed and implemented using the Cloud-Native architecture. It uses the fastest ANN Algorithm NGT for searching neighbors. Vald supports automatic vector indexing, index backup, horizontal scaling, which allows you to search from billions upon billions of feature vector data. Vald is simple to use, rich in features, and highly customizable. Usually, the graph must be locked during indexing. This can cause stop-the world. Vald uses distributed index graphs so that it continues to work while indexing. Vald has its own highly customizable Ingress/Egress filter. This can be configured to work with the gRPC interface. Horizontal scaling is available on memory and cpu according to your needs. Vald supports disaster recovery by enabling auto backup using Persistent Volume or Object Storage. -
19
Stack AI
Stack AI
$199/month AI agents that interact and answer questions with users and complete tasks using your data and APIs. AI that can answer questions, summarize and extract insights from any long document. Transfer styles and formats, as well as tags and summaries between documents and data sources. Stack AI is used by developer teams to automate customer service, process documents, qualify leads, and search libraries of data. With a single button, you can try multiple LLM architectures and prompts. Collect data, run fine-tuning tasks and build the optimal LLM to fit your product. We host your workflows in APIs, so that your users have access to AI instantly. Compare the fine-tuning services of different LLM providers. -
20
Langdock
Langdock
FreeNative support for ChatGPT, LangChain and more. Bing, HuggingFace, and more to come. Add your API documentation by hand or import an OpenAPI specification. Access the request prompt and parameters, headers, bodies, and more. View detailed live metrics on how your plugin performs, including latencies and errors. Create your own dashboards to track funnels and aggregate metrics. -
21
ZenML
ZenML
FreeSimplify your MLOps pipelines. ZenML allows you to manage, deploy and scale any infrastructure. ZenML is open-source and free. Two simple commands will show you the magic. ZenML can be set up in minutes and you can use all your existing tools. ZenML interfaces ensure your tools work seamlessly together. Scale up your MLOps stack gradually by changing components when your training or deployment needs change. Keep up to date with the latest developments in the MLOps industry and integrate them easily. Define simple, clear ML workflows and save time by avoiding boilerplate code or infrastructure tooling. Write portable ML codes and switch from experiments to production in seconds. ZenML's plug and play integrations allow you to manage all your favorite MLOps software in one place. Prevent vendor lock-in by writing extensible, tooling-agnostic, and infrastructure-agnostic code. -
22
Deep Lake
activeloop
$995 per monthWe've been working on Generative AI for 5 years. Deep Lake combines the power and flexibility of vector databases and data lakes to create enterprise-grade LLM-based solutions and refine them over time. Vector search does NOT resolve retrieval. You need a serverless search for multi-modal data including embeddings and metadata to solve this problem. You can filter, search, and more using the cloud, or your laptop. Visualize your data and embeddings to better understand them. Track and compare versions to improve your data and your model. OpenAI APIs are not the foundation of competitive businesses. Your data can be used to fine-tune LLMs. As models are being trained, data can be efficiently streamed from remote storage to GPUs. Deep Lake datasets can be visualized in your browser or Jupyter Notebook. Instantly retrieve different versions and materialize new datasets on the fly via queries. Stream them to PyTorch, TensorFlow, or Jupyter Notebook. -
23
Flowise
Flowise
FreeFlowise is open source and will always be free to use for commercial and private purposes. Build LLMs apps easily with Flowise, an open source UI visual tool to build your customized LLM flow using LangchainJS, written in Node Typescript/Javascript. Open source MIT License, see your LLM applications running live, and manage component integrations. GitHub Q&A using conversational retrieval QA chains. Language translation using LLM chains with a chat model and chat prompt template. Conversational agent for chat model that uses chat-specific prompts. -
24
Portkey
Portkey.ai
$49 per monthLMOps is a stack that allows you to launch production-ready applications for monitoring, model management and more. Portkey is a replacement for OpenAI or any other provider APIs. Portkey allows you to manage engines, parameters and versions. Switch, upgrade, and test models with confidence. View aggregate metrics for your app and users to optimize usage and API costs Protect your user data from malicious attacks and accidental exposure. Receive proactive alerts if things go wrong. Test your models in real-world conditions and deploy the best performers. We have been building apps on top of LLM's APIs for over 2 1/2 years. While building a PoC only took a weekend, bringing it to production and managing it was a hassle! We built Portkey to help you successfully deploy large language models APIs into your applications. We're happy to help you, regardless of whether or not you try Portkey! -
25
Gradient
Gradient
$0.0005 per 1,000 tokensA simple web API allows you to fine-tune your LLMs and receive completions. No infrastructure is required. Instantly create private AI applications that comply with SOC2-standards. Our developer platform makes it easy to customize models for your specific use case. Select the base model and define the data that you want to teach. We will take care of everything else. With a single API, you can integrate private LLMs with your applications. No more deployment, orchestration or infrastructure headaches. The most powerful OSS available -- highly generalized capabilities with amazing storytelling and reasoning capabilities. Use a fully unlocked LLM for the best internal automation systems in your company. -
26
Ollama
Ollama
FreeStart using large language models in your locality. -
27
LLM Spark
LLM Spark
$29 per monthSet up your workspace easily by integrating GPT language models with your provider key for unparalleled performance. LLM Spark's GPT templates can be used to create AI applications quickly. Or, you can start from scratch and create unique projects. Test and compare multiple models at the same time to ensure optimal performance in multiple scenarios. Save versions and history with ease while streamlining development. Invite others to your workspace so they can collaborate on projects. Semantic search is a powerful search tool that allows you to find documents by meaning and not just keywords. AI applications can be made accessible across platforms by deploying trained prompts. -
28
Evidently AI
Evidently AI
$500 per monthThe open-source ML observability Platform. From validation to production, evaluate, test, and track ML models. From tabular data up to NLP and LLM. Built for data scientists and ML Engineers. All you need to run ML systems reliably in production. Start with simple ad-hoc checks. Scale up to the full monitoring platform. All in one tool with consistent APIs and metrics. Useful, beautiful and shareable. Explore and debug a comprehensive view on data and ML models. Start in a matter of seconds. Test before shipping, validate in production, and run checks with every model update. By generating test conditions based on a reference dataset, you can skip the manual setup. Monitor all aspects of your data, models and test results. Proactively identify and resolve production model problems, ensure optimal performance and continually improve it. -
29
Lilac
Lilac
FreeLilac is a free open-source tool that allows data and AI practitioners improve their products through better data. Understanding your data is easy with powerful filtering and search. Work together with your team to create a single dataset. Use best practices for data curation to reduce the size of your dataset and training costs and time. Our diff viewer allows you to see how your pipeline affects your data. Clustering is an automatic technique that assigns categories to documents by analyzing their text content. Similar documents are then placed in the same category. This reveals your dataset's overall structure. Lilac uses LLMs and state-of-the art algorithms to cluster the data and assign descriptive, informative titles. We can use keyword search before we do advanced searches, such as concept or semantic searching. -
30
OpenPipe
OpenPipe
$1.20 per 1M tokensOpenPipe provides fine-tuning for developers. Keep all your models, datasets, and evaluations in one place. New models can be trained with a click of a mouse. Automatically record LLM responses and requests. Create datasets using your captured data. Train multiple base models using the same dataset. We can scale your model to millions of requests on our managed endpoints. Write evaluations and compare outputs of models side by side. You only need to change a few lines of code. OpenPipe API Key can be added to your Python or Javascript OpenAI SDK. Custom tags make your data searchable. Small, specialized models are much cheaper to run than large, multipurpose LLMs. Replace prompts in minutes instead of weeks. Mistral and Llama 2 models that are fine-tuned consistently outperform GPT-4-1106 Turbo, at a fraction the cost. Many of the base models that we use are open-source. You can download your own weights at any time when you fine-tune Mistral or Llama 2. -
31
Airtrain
Airtrain
FreeQuery and compare multiple proprietary and open-source models simultaneously. Replace expensive APIs with custom AI models. Customize foundational AI models using your private data and adapt them to fit your specific use case. Small, fine-tuned models perform at the same level as GPT-4 while being up to 90% less expensive. Airtrain's LLM-assisted scoring simplifies model grading using your task descriptions. Airtrain's API allows you to serve your custom models in the cloud, or on your own secure infrastructure. Evaluate and compare proprietary and open-source models across your entire dataset using custom properties. Airtrain's powerful AI evaluation tools let you score models based on arbitrary properties to create a fully customized assessment. Find out which model produces outputs that are compliant with the JSON Schema required by your agents or applications. Your dataset is scored by models using metrics such as length and compression. -
32
PlugBear
Runbear
$31 per monthPlugBear provides a low-code/no-code solution to connect communication channels with LLM applications (Large Language Model). It allows, for example, the creation of a Slack Bot from an LLM application in just a few simple clicks. PlugBear is notified when a trigger event occurs on the integrated channels. It then transforms messages into LLM applications, and initiates generation. PlugBear then transforms the generated results so that they are compatible with each channel. This allows users to interact with LLM applications seamlessly across different channels. -
33
Polyaxon
Polyaxon
A platform for machine learning and deep learning applications that is reproducible and scaleable. Learn more about the products and features that make up today's most innovative platform to manage data science workflows. Polyaxon offers an interactive workspace that includes notebooks, tensorboards and visualizations. You can collaborate with your team and share and compare results. Reproducible results are possible with the built-in version control system for code and experiments. Polyaxon can be deployed on-premises, in the cloud, or in hybrid environments. This includes single laptops, container management platforms, and Kubernetes. You can spin up or down, add nodes, increase storage, and add more GPUs. -
34
Metaflow
Metaflow
Data scientists are able to build, improve, or operate end-to–end workflows independently. This allows them to deliver data science projects that are successful. Metaflow can be used with your favorite data science libraries such as SciKit Learn or Tensorflow. You can write your models in idiomatic Python codes with little to no learning. Metaflow also supports R language. Metaflow allows you to design your workflow, scale it, and then deploy it to production. It automatically tracks and versions all your data and experiments. It allows you to easily inspect the results in notebooks. Metaflow comes pre-installed with the tutorials so it's easy to get started. Metaflow allows you to make duplicates of all tutorials in your current directory by using the command line interface. -
35
Arthur AI
Arthur
To detect and respond to data drift, track model performance for better business outcomes. Arthur's transparency and explainability APIs help to build trust and ensure compliance. Monitor for bias and track model outcomes against custom bias metrics to 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's proprietary techniques for reducing bias can be used to identify bias in models and help you to see how they treat different populations.} {Arthur scales up and down to ingest up to 1MM transactions per second and deliver insights quickly.|Arthur can scale up and down to ingest as many transactions per second as possible and delivers insights quickly.} Only authorized users can perform actions. Each team/department can have their own environments with different access controls. Once data is ingested, it cannot be modified. This prevents manipulation of metrics/insights. -
36
Qdrant
Qdrant
Qdrant is a vector database and similarity engine. It is an API service that allows you to search for the closest high-dimensional vectors. Qdrant allows embeddings and neural network encoders to be transformed into full-fledged apps for matching, searching, recommending, etc. This specification provides the OpenAPI version 3 specification to create a client library for almost any programming language. You can also use a ready-made client for Python, or other programming languages that has additional functionality. For Approximate Nearest Neighbor Search, you can make a custom modification to the HNSW algorithm. Search at a State of the Art speed and use search filters to maximize results. Additional payload can be associated with vectors. Allows you to store payload and filter results based upon payload values. -
37
Dify
Dify
Your team can develop AI applications using models such as GPT-4, and operate them visually. You can deploy your application within 5 minutes, whether it is for internal team use or an external release. Using documents/webpages/Notion content as the context for AI, automatically complete text preprocessing, vectorization and segmentation. No need to learn embedding methods anymore. This will save you weeks of development. Dify offers a smooth user experience for model access and context embedding. It also provides cost control, data annotation, and cost control. You can easily create AI apps for internal team use, or product development. Start with a prompt but go beyond its limitations. Dify offers rich functionality in many scenarios. -
38
Supervised
Supervised
$19 per monthOpenAI's GPT Engine can be used to build supervised large-language models backed by your own data. Supervised is a tool that allows enterprises to build AI apps with scalability. It can be difficult to build your own LLM. We let you create and sell your AI apps using Supervised. Supervised AI gives you the tools to create powerful and scalable AI & LLM Apps. You can quickly build high-accuracy AI using our custom models and data. AI is being used by businesses in a very basic way, and the full potential of AI has yet to be unlocked. We let you use your data to create a new AI model. Build custom AI applications using data sources and models created by other developers. -
39
Usage Panda
Usage Panda
Add enterprise-level security to your OpenAI usage. OpenAI LLM APIs may be powerful, but lack the visibility and control that enterprises require. Usage Panda fixes this. Usage Panda checks the security policies of requests before they are sent to OpenAI. Avoid unexpected bills by only allowing those requests that are below a certain cost threshold. Opt-in for a log of the entire request, parameters and response to every OpenAI request. Create an unlimited number connections, each with their own custom policies and limitations. Monitor, redact and block malicious attempts at altering or revealing system prompts. Usage Panda's visualizations and custom charts allow you to explore usage in great detail. Receive notifications via email or Slack when you reach a usage threshold or billing limit. Assign costs and policy violations to the end application users, and implement rate limits per user. -
40
Bruinen
Bruinen
Bruinen allows your platform to validate your users' profiles across the Internet. We offer easy integration with a wide range of data sources including Google, GitHub and many others. Connect to the data that you need and take actions on one platform. Our API handles auth, permissions and rate limits, reducing complexity and increasing productivity. This allows you to iterate faster and focus on your core product. Allow users to confirm a specific action via SMS, email, or magic-link prior to the action taking place. Allow your users to customize the actions that they want to confirm with a pre-built permissions interface. Bruinen provides a consistent, easy-to-use interface for accessing your users' profiles. Bruinen allows you to connect, authenticate and pull data from these accounts. -
41
dstack
dstack
It reduces cloud costs and frees users from vendor-lock-in. Configure your hardware resources such as GPU and memory and specify whether you prefer to use spot instances. dstack provision cloud resources, fetches code and forwards ports to secure access. You can access the cloud dev environment using your desktop IDE. Configure your hardware resources (GPU, RAM, etc.). Indicate whether you would like to use spot instances or on-demand instances. dstack automatically provision cloud resources, forward ports and secure access. Pre-train your own models and fine-tune them in any cloud, easily and cost-effectively. Do you want cloud resources to be provisioned automatically based on your configurations? You can access your data and store outputs artifacts by using declarative configurations or the Python SDK. -
42
Taylor AI
Taylor AI
Open source language models require time and specialized expertise. Taylor AI allows your engineering team focus on creating real business value rather than deciphering complicated libraries and setting up a training infrastructure. Working with third-party LLM vendors requires that your sensitive company data be exposed. Most providers reserve their right to retrain models using your data. Taylor AI allows you to own and control all of your models. Break free from the pay-per token pricing structure. Taylor AI only charges you for the training of the model. You can deploy and interact as much as you want with your AI models. Every month, new open source models are released. Taylor AI keeps up to date with the latest open source language models so that you don't need to. Train with the latest open-source models to stay ahead. You own the model so you can deploy according to your unique compliance standards and security standards. -
43
Pezzo
Pezzo
$0Pezzo is an open-source LLMOps tool for developers and teams. With just two lines of code you can monitor and troubleshoot your AI operations. You can also collaborate and manage all your prompts from one place. -
44
PromptIDE
xAI
FreeThe xAI PromptIDE integrates development for prompt engineering, interpretability research and other related tasks. It accelerates prompting engineering through an SDK which allows complex prompting techniques to be implemented and rich analytics to visualize the network's results. We use it heavily for our Grok continuous development. We developed PromptIDE in order to provide engineers and researchers with transparent access to Grok-1 - the model that powers Grok - to the community. The IDE was designed to empower users, and allow them to explore the capabilities of large language models at their own pace. The IDE's core is a Python editor that, when combined with a new SDK, allows for complex prompting techniques. Users can see useful analytics while executing prompts within the IDE. These include the precise tokenization of the prompt, sampling probabilities and alternative tokens. The IDE offers a number of features that enhance the quality of life. It automatically saves prompts. -
45
Lasso Security
Lasso Security
It's a wild world out there. New cyber threats are emerging as we speak. Lasso Security allows you to harness AI large-language model (LLM), embrace progress without compromising security. We are focused solely on LLM security. This technology is embedded in our DNA and code. Our solution goes beyond traditional methods to lasso external threats and internal errors which lead to exposure. Most organizations now devote resources to LLM adoption. Few organizations are addressing vulnerabilities and risks, whether they are known or not. -
46
RagaAI
RagaAI
RagaAI is a leading AI testing platform which helps enterprises to mitigate AI risks, and make their models reliable and secure. Intelligent recommendations will reduce AI risk across cloud or edge deployments, and optimize MLOps cost. A foundation model designed specifically to revolutionize AI testing. You can easily identify the next steps for fixing dataset and model problems. AI-testing methods are used by many today, and they increase time commitments and reduce productivity when building models. They also leave unforeseen risks and perform poorly after deployment, wasting both time and money. We have created an end-toend AI testing platform to help enterprises improve their AI pipeline and prevent inefficiencies. 300+ tests to identify, fix, and accelerate AI development by identifying and fixing every model, data and operational issue. -
47
Weights & Biases
Weights & Biases
Experiment tracking, hyperparameter optimization and model and dataset versioning. With just 5 lines of code, you can track, compare, and visualize ML experiment results. You can add a few lines of code to your script and every time you train a new model, a new stream of experiments will be available to your dashboard. Our hyperparameter search tool is scalable and can optimize models. Sweeps can be easily set up and plugged into your existing infrastructure to run models. All details of your machine learning pipeline, including data preparation, data versioning and training, can be saved. It's now easier than ever to share project updates. Describe how your model works, show graphs showing how models have improved, discuss bugs, and show progress towards milestones. This central platform will allow you to track all machine learning models in your organization, from production to experimentation. -
48
Snorkel AI
Snorkel AI
AI is today blocked by a lack of labeled data. Not models. The first data-centric AI platform powered by a programmatic approach will unblock AI. With its unique programmatic approach, Snorkel AI is leading a shift from model-centric AI development to data-centric AI. By replacing manual labeling with programmatic labeling, you can save time and money. You can quickly adapt to changing data and business goals by changing code rather than manually re-labeling entire datasets. Rapid, guided iteration of the training data is required to develop and deploy AI models of high quality. Versioning and auditing data like code leads to faster and more ethical deployments. By collaborating on a common interface, which provides the data necessary to train models, subject matter experts can be integrated. Reduce risk and ensure compliance by labeling programmatically, and not sending data to external annotators. -
49
Jina AI
Jina AI
Businesses and developers can now create cutting-edge neural searches, generative AI and multimodal services using state of the art LMOps, LLOps, and cloud-native technology. Multimodal data is everywhere. From tweets to short videos on TikTok to audio snippets, Zoom meeting records, PDFs containing figures, 3D meshes and photos in games, there's no shortage of it. It is powerful and rich, but it often hides behind incompatible data formats and modalities. High-level AI applications require that one solve search first and create second. Neural Search uses AI for finding what you need. A description of a sunrise may match a photograph, or a photo showing a rose can match the lyrics to a song. Generative AI/Creative AI use AI to create what you need. It can create images from a description or write poems from a photograph. -
50
Pinecone
Pinecone
Artificial intelligence long-term memory The Pinecone vector database makes building high-performance vector search apps easy. Fully managed and developer-friendly, the database is easily scalable without any infrastructure problems. Once you have vector embeddings created, you can search and manage them in Pinecone to power semantic searches, recommenders, or other applications that rely upon relevant information retrieval. Even with billions of items, ultra-low query latency Provide a great user experience. You can add, edit, and delete data via live index updates. Your data is available immediately. For more relevant and quicker results, combine vector search with metadata filters. Our API makes it easy to launch, use, scale, and scale your vector searching service without worrying about infrastructure. It will run smoothly and securely. -
51
LangChain
LangChain
We believe that the most effective and differentiated applications won't only call out via an API to a language model. LangChain supports several modules. We provide examples, how-to guides and reference docs for each module. Memory is the concept that a chain/agent calls can persist in its state. LangChain provides a standard interface to memory, a collection memory implementations and examples of agents/chains that use it. This module outlines best practices for combining language models with your own text data. Language models can often be more powerful than they are alone. -
52
Omni AI
Omni AI
Omni is an AI framework that allows you to connect Prompts and Tools to LLM Agents. Agents are built on the ReAct paradigm, which is Reason + Act. They allow LLM models and tools to interact to complete a task. Automate customer service, document processing, qualification of leads, and more. You can easily switch between LLM architectures and prompts to optimize performance. Your workflows are hosted as APIs, so you can instantly access AI. -
53
CalypsoAI
CalypsoAI
Content scanners can be customized to ensure that any sensitive or confidential data, intellectual property or confidential information included in an inquiry never leaves your organisation. LLM responses are scanned to detect code written in many different languages. Responses containing this code are blocked from accessing your system. Scanners use a variety of techniques to identify prompts that try to circumvent the system and organizational parameters for LLM activities. Subject matter experts in-house ensure that your teams can use the information provided by LLMs confidently. Don't let the fear of being a victim of the vulnerabilities in large language models prevent your organization from gaining a competitive edge. -
54
LangSmith
LangChain
Unexpected outcomes happen all the time. You can pinpoint the source of errors or surprises in real-time with surgical precision when you have full visibility of the entire chain of calls. Unit testing is a key component of software engineering to create production-ready, performant applications. LangSmith offers the same functionality for LLM apps. LangSmith allows you to create test datasets, execute your applications on them, and view results without leaving the application. LangSmith allows mission-critical observability in just a few lines. LangSmith was designed to help developers harness LLMs' power and manage their complexity. We don't just build tools. We are establishing best practices that you can rely upon. Build and deploy LLM apps with confidence. Stats on application-level usage. Feedback collection. Filter traces and cost measurement. Dataset curation - compare chain performance - AI-assisted assessment & embrace best practices. -
55
Vellum AI
Vellum
Use tools to bring LLM-powered features into production, including tools for rapid engineering, semantic searching, version control, quantitative testing, and performance monitoring. Compatible with all major LLM providers. Develop an MVP quickly by experimenting with various prompts, parameters and even LLM providers. Vellum is a low-latency and highly reliable proxy for LLM providers. This allows you to make version controlled changes to your prompts without needing to change any code. Vellum collects inputs, outputs and user feedback. These data are used to build valuable testing datasets which can be used to verify future changes before going live. Include dynamically company-specific context to your prompts, without managing your own semantic searching infrastructure. -
56
Neum AI
Neum AI
No one wants to have their AI respond to a client with outdated information. Neum AI provides accurate and current context for AI applications. Set up your data pipelines quickly by using built-in connectors. These include data sources such as Amazon S3 and Azure Blob Storage and vector stores such as Pinecone and Weaviate. Transform and embed your data using built-in connectors to embed models like OpenAI, Replicate and serverless functions such as Azure Functions and AWS Lambda. Use role-based controls to ensure that only the right people have access to specific vectors. Bring your own embedding model, vector stores, and sources. Ask us how you can run Neum AI on your own cloud. -
57
baioniq
Quantiphi
Generative AI (GAI) and Large Language Models, or LLMs, are promising solutions to unlock the value of unstructured information. They provide enterprises with instant insights. This has given businesses the opportunity to reimagine their customer experience, products and services, as well as increase productivity within their teams. baioniq, Quantiphi’s enterprise-ready Generative AI Platform for AWS, is designed to help organizations quickly adopt generative AI capabilities. AWS customers can deploy baioniq on AWS using a containerized version. It is a modular solution which allows modern enterprises to fine tune LLMs in four simple steps to incorporate domain-specific information and perform enterprise-specific functions. -
58
Carbon
Carbon
Carbon is a cost-effective alternative to expensive pipelines. You only pay monthly for usage. Utilise less and spend less with our usage-based pricing; use more and save more. Use our ready-made components for file uploading, web scraping, and 3rd party verification. A rich library of APIs designed for developers that import AI-focused data. Create and retrieve chunks, embeddings and data from all sources. Unstructured data can be searched using enterprise-grade keyword and semantic search. Carbon manages OAuth flows from 10+ sources. It transforms source data to vector store-optimized files and handles data synchronization automatically. -
59
Lakera
Lakera
Lakera Guard enables organizations to build GenAI apps without worrying about prompt injections. Data loss, harmful content and other LLM risks are eliminated. Powered by world's most advanced AI-based threat intelligence. Lakera's threat database contains tens millions of attack datapoints and is growing daily by more than 100k entries. Your defense is constantly strengthened with Lakera guard. Lakera guard embeds the latest security intelligence into your LLM applications, allowing you to build and deploy secure AI at scale. We monitor tens or millions of attacks in order to detect and protect against unwanted behavior and data loss due to prompt injection. Assess, track, report and manage AI systems in your organization responsibly to ensure their security at all times. -
60
Deasie
Deasie
You can't build a good model with bad data. More than 80% (documents, reports, texts, images) of the data we have today is unstructured. It is important to know which parts of the data are relevant, old, inconsistent and safe to use for language models. Inaction leads to unreliable and unsafe AI adoption. -
61
Second State
Second State
OpenAI compatible, fast, lightweight, portable and powered by rust. We work with cloud providers to support microservices in web apps, especially edge cloud/CDN computing providers. Use cases include AI inferences, database accesses, CRM, ecommerce and workflow management. We work with streaming frameworks, databases and data to support embedded functions for data filtering. The serverless functions may be database UDFs. They could be embedded into data ingest streams or query results. Write once and run anywhere. Take full advantage of GPUs. In just 5 minutes, you can get started with the Llama 2 models on your device. Retrieval argument fmgeneration is a popular way to build AI agents using external knowledge bases. Create an HTTP microservice to classify images. It runs YOLO models and Mediapipe models natively at GPU speed. -
62
Gantry
Gantry
Get a complete picture of the performance of your model. Log inputs and out-puts, and enrich them with metadata. Find out what your model is doing and where it can be improved. Monitor for errors, and identify underperforming cohorts or use cases. The best models are based on user data. To retrain your model, you can programmatically gather examples that are unusual or underperforming. When changing your model or prompt, stop manually reviewing thousands outputs. Apps powered by LLM can be evaluated programmatically. Detect and fix degradations fast. Monitor new deployments and edit your app in real-time. Connect your data sources to your self-hosted model or third-party model. Our serverless streaming dataflow engines can handle large amounts of data. Gantry is SOC-2-compliant and built using enterprise-grade authentication. -
63
UpTrain
UpTrain
Scores are available for factual accuracy and context retrieval, as well as guideline adherence and tonality. You can't improve if you don't measure. UpTrain continuously monitors the performance of your application on multiple evaluation criteria and alerts you if there are any regressions. UpTrain allows for rapid and robust experimentation with multiple prompts and model providers. Since their inception, LLMs have been plagued by hallucinations. UpTrain quantifies the degree of hallucination, and the quality of context retrieved. This helps detect responses that are not factually accurate and prevents them from being served to end users. -
64
WhyLabs
WhyLabs
Observability allows you to detect data issues and ML problems faster, to deliver continuous improvements and to avoid costly incidents. Start with reliable data. Monitor data in motion for quality issues. Pinpoint data and models drift. Identify the training-serving skew, and proactively retrain. Monitor key performance metrics continuously to detect model accuracy degradation. Identify and prevent data leakage in generative AI applications. Protect your generative AI apps from malicious actions. Improve AI applications by using user feedback, monitoring and cross-team collaboration. Integrate in just minutes with agents that analyze raw data, without moving or replicating it. This ensures privacy and security. Use the proprietary privacy-preserving technology to integrate the WhyLabs SaaS Platform with any use case. Security approved by healthcare and banks. -
65
Martian
Martian
Martian outperforms GPT-4 across OpenAI's evals (open/evals). Martian outperforms GPT-4 in all OpenAI's evaluations (open/evals). We transform opaque black boxes into interpretable visual representations. Our router is our first tool built using our model mapping method. Model mapping is being used in many other applications, including transforming transformers from unintelligible matrices to human-readable programs. Automatically reroute your customers to other providers if a company has an outage or a high latency period. Calculate how much money you could save using the Martian Model Router by using our interactive cost calculator. Enter the number of users and tokens per session. Also, specify how you want to trade off between cost and quality. -
66
Arcee AI
Arcee AI
Optimizing continuous pre-training to enrich models with proprietary data. Assuring domain-specific models provide a smooth user experience. Create 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. Our product's domain adaptability allows you to train and deploy SLMs for a variety of use cases. Arcee's VPC service allows you to train and deploy your SLMs while ensuring that what belongs to you, stays yours. -
67
FinetuneDB
FinetuneDB
Capture production data. Evaluate outputs together and fine-tune the performance of your LLM. A detailed log overview will help you understand what is happening in production. Work with domain experts, product managers and engineers to create reliable model outputs. Track AI metrics, such as speed, token usage, and quality scores. Copilot automates model evaluations and improvements for your use cases. Create, manage, or optimize prompts for precise and relevant interactions between AI models and users. Compare fine-tuned models and foundation models to improve prompt performance. Build a fine-tuning dataset with your team. Create custom fine-tuning data to optimize model performance. -
68
Freeplay
Freeplay
Take control of your LLMs with Freeplay. It gives product teams the ability to prototype faster, test confidently, and optimize features. A better way to build using LLMs. Bridge the gap between domain specialists & developers. Engineering, testing & evaluation toolkits for your entire team. -
69
Keywords AI
Keywords AI
$0/month A unified platform for LLM applications. Use all the best-in class LLMs. Integration is dead simple. You can easily trace user sessions, debug and trace user sessions. -
70
Seekr
Seekr
Generative AI can boost your productivity and inspire you to create more content. It is bound and grounded by industry standards and intelligence. Content can be rated for reliability, political leaning, and alignment with your brand safety themes. Our AI models are rigorously reviewed and tested by leading experts and scientists to train our dataset with only the most trustworthy content on the web. Use the most reliable large language model (LLM), which is used by the industry, to create new content quickly, accurately, and for a low cost. AI tools can help you speed up processes and improve business outcomes. They are designed to reduce costs while delivering astronomical results. -
71
LM Studio
LM Studio
Use models via the Chat UI in-app or an OpenAI compatible local server. Minimum requirements: Mac M1/M2/M3 or Windows PC with AVX2 processor. Linux is currently in beta. Privacy is a major reason to use a local LLM, and LM Studio was designed with that in mind. Your data is kept private and on your local machine. You can use LLMs that you load in LM Studio through an API server running locally. -
72
LLMCurator
LLMCurator
Teams can use LLMCurator for annotating data, interacting with LLM and sharing results. Edit the model response to create better data. You can annotate your text dataset with prompts, and then export and process it. -
73
impaction.ai
Coxwave
Discover. Analyze. Optimize. Use [impaction.ai]’s intuitive semantic search to easily sift conversational data. Type 'find me conversation where ...', and let our engine handle the rest. Meet Columbus, your intelligent data co-pilot. Columbus analyzes conversations and highlights key trends. It can even recommend which dialogues you should pay attention to. Take data-driven action to improve user engagement and create a smarter and more responsive AI product. Columbus is not only a great source of information, but also offers suggestions on how to improve the situation. -
74
TorqCloud
IntelliBridge
TorqCloud was designed to help users source data, move it, enrich it, visualize, secure and interact with that data using AI agents. TorqCloud is a comprehensive AIOps tool that allows users to create or integrate custom LLM applications end-to-end using a low code interface. Built to handle massive amounts of data and deliver actionable insights, TorqCloud is a vital tool for any organization that wants to stay competitive in the digital landscape. Our approach combines seamless interdisciplinarity, a focus on user needs, test and learn methodologies that allow us to get the product to market quickly, and a close relationship with your team, including skills transfers and training. We begin with empathy interviews and then perform stakeholder mapping exercises. This is where we explore the customer journey, behavioral changes needed, problem sizing and linear unpacking.
Overview of LLMOps Tools
LLMOps stands for Large Language Model Operations, a unique subset of MLOps that delves into the operational complexities and infrastructural requirements necessary for fine-tuning and deploying large foundational models.
Large Language Models, often abbreviated as LLMs, are advanced deep learning constructs that can mimic human-like linguistic patterns. They're designed with billions of parameters and trained on extensive text data sets, leading to impressive capabilities, but also bringing about unique managerial hurdles.
Key Components of LLMOps
- Data Administration: In the world of LLMs, data management is paramount. It involves careful organization and control to ensure the quality and availability of data for the models as and when required.
- Model Progression: LLMs are often fine-tuned for different tasks. This necessitates a well-structured methodology to create and test various models, with the ultimate goal being to identify the most suitable one for specific tasks.
- Scalable Implementation: The deployment of LLMs requires an infrastructure that is not only reliable but also scalable, given the resource-heavy nature of these models.
- Performance Supervision: Continuous oversight of LLMs is crucial to maintain compliance with performance benchmarks, including accuracy, response time, and bias detection.
LLMOps is a fast-growing field, propelled by the increasing capabilities and widespread use of LLMs. The broader acceptance of these models underscores the importance and demand for LLMOps expertise.
LLMOps Challenges
- Data Administration: Maintaining quality standards and accessibility while managing vast amounts of data for LLM training and fine-tuning can be quite daunting.
- Model Progression: The process involved in developing and evaluating different LLMs for specific tasks can be intricate and demanding.
- Scalable Implementation: Establishing a reliable and scalable deployment infrastructure that can efficiently handle the requirements of large language models is a significant challenge.
- Performance Supervision: Consistent monitoring of LLMs is vital to ensure their performance meets the set standards. This involves examining accuracy, response time, and bias mitigation.
Benefits of LLMOps
LLMOps provides several significant advantages:
- Increased Accuracy: By ensuring the use of high-quality data for training and enabling reliable and scalable deployment of models, LLMOps contributes to enhancing the accuracy of these models.
- Reduced Latency: LLMOps enables efficient deployment strategies, leading to reduced latency in LLMs and faster data retrieval.
- Promotion of Fairness: By striving to eliminate bias in LLMs, LLMOps ensures more impartial outputs, preventing discrimination against specific groups.
With the continued growth in the power and application of LLMs, the significance of expertise in LLMOps will only increase. This dynamic field is continually evolving, staying abreast of new developments and challenges.