Alternatives to BenchLLM
Compare BenchLLM alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to BenchLLM in 2025. Compare features, ratings, user reviews, pricing, and more from BenchLLM competitors and alternatives in order to make an informed decision for your business.
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1
Vertex AI
Google
Build, deploy, and scale machine learning (ML) models faster, with fully managed ML tools for any use case. Through Vertex AI Workbench, Vertex AI is natively integrated with BigQuery, Dataproc, and Spark. You can use BigQuery ML to create and execute machine learning models in BigQuery using standard SQL queries on existing business intelligence tools and spreadsheets, or you can export datasets from BigQuery directly into Vertex AI Workbench and run your models from there. Use Vertex Data Labeling to generate highly accurate labels for your data collection. Vertex AI Agent Builder enables developers to create and deploy enterprise-grade generative AI applications. It offers both no-code and code-first approaches, allowing users to build AI agents using natural language instructions or by leveraging frameworks like LangChain and LlamaIndex. -
2
Ango Hub
iMerit
Ango Hub is a quality-focused, enterprise-ready data annotation platform for AI teams, available on cloud and on-premise. It supports computer vision, medical imaging, NLP, audio, video, and 3D point cloud annotation, powering use cases from autonomous driving and robotics to healthcare AI. Built for AI fine-tuning, RLHF, LLM evaluation, and human-in-the-loop workflows, Ango Hub boosts throughput with automation, model-assisted pre-labeling, and customizable QA while maintaining accuracy. Features include centralized instructions, review pipelines, issue tracking, and consensus across up to 30 annotators. With nearly twenty labeling tools—such as rotated bounding boxes, label relations, nested conditional questions, and table-based labeling—it supports both simple and complex projects. It also enables annotation pipelines for chain-of-thought reasoning and next-gen LLM training and enterprise-grade security with HIPAA compliance, SOC 2 certification, and role-based access controls. -
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. -
4
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 -
5
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 -
6
Prompt flow
Microsoft
Prompt Flow is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, and evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality. With Prompt Flow, you can create flows that link LLMs, prompts, Python code, and other tools together in an executable workflow. It allows for debugging and iteration of flows, especially tracing interactions with LLMs with ease. You can evaluate your flows, calculate quality and performance metrics with larger datasets, and integrate the testing and evaluation into your CI/CD system to ensure quality. Deployment of flows to the serving platform of your choice or integration into your app’s code base is made easy. Additionally, collaboration with your team is facilitated by leveraging the cloud version of Prompt Flow in Azure AI. -
7
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 -
8
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. -
9
Literal AI
Literal AI
Literal AI is a collaborative platform designed to assist engineering and product teams in developing production-grade Large Language Model (LLM) applications. It offers a suite of tools for observability, evaluation, and analytics, enabling efficient tracking, optimization, and integration of prompt versions. Key features include multimodal logging, encompassing vision, audio, and video, prompt management with versioning and AB testing capabilities, and a prompt playground for testing multiple LLM providers and configurations. Literal AI integrates seamlessly with various LLM providers and AI frameworks, such as OpenAI, LangChain, and LlamaIndex, and provides SDKs in Python and TypeScript for easy instrumentation of code. The platform also supports the creation of experiments against datasets, facilitating continuous improvement and preventing regressions in LLM applications. -
10
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 -
11
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. -
12
HoneyHive
HoneyHive
AI engineering doesn't have to be a black box. Get full visibility with tools for tracing, evaluation, prompt management, and more. HoneyHive is an AI observability and evaluation platform designed to assist teams in building reliable generative AI applications. It offers tools for evaluating, testing, and monitoring AI models, enabling engineers, product managers, and domain experts to collaborate effectively. Measure quality over large test suites to identify improvements and regressions with each iteration. Track usage, feedback, and quality at scale, facilitating the identification of issues and driving continuous improvements. HoneyHive supports integration with various model providers and frameworks, offering flexibility and scalability to meet diverse organizational needs. It is suitable for teams aiming to ensure the quality and performance of their AI agents, providing a unified platform for evaluation, monitoring, and prompt management. -
13
promptfoo
promptfoo
Promptfoo discovers and eliminates major LLM risks before they are shipped to production. Its founders have experience launching and scaling AI to over 100 million users using automated red-teaming and testing to overcome security, legal, and compliance issues. Promptfoo's open source, developer-first approach has made it the most widely adopted tool in this space, with over 20,000 users. Custom probes for your application that identify failures you actually care about, not just generic jailbreaks and prompt injections. Move quickly with a command-line interface, live reloads, and caching. No SDKs, cloud dependencies, or logins. Used by teams serving millions of users and supported by an active open source community. Build reliable prompts, models, and RAGs with benchmarks specific to your use case. Secure your apps with automated red teaming and pentesting. Speed up evaluations with caching, concurrency, and live reloading.Starting Price: Free -
14
DeepEval
Confident AI
DeepEval is a simple-to-use, open source LLM evaluation framework, for evaluating and testing large-language model systems. It is similar to Pytest but specialized for unit testing LLM outputs. DeepEval incorporates the latest research to evaluate LLM outputs based on metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., which uses LLMs and various other NLP models that run locally on your machine for evaluation. Whether your application is implemented via RAG or fine-tuning, LangChain, or LlamaIndex, DeepEval has you covered. With it, you can easily determine the optimal hyperparameters to improve your RAG pipeline, prevent prompt drifting, or even transition from OpenAI to hosting your own Llama2 with confidence. The framework supports synthetic dataset generation with advanced evolution techniques and integrates seamlessly with popular frameworks, allowing for efficient benchmarking and optimization of LLM systems.Starting Price: Free -
15
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 -
16
Orq.ai
Orq.ai
Orq.ai is the #1 platform for software teams to operate agentic AI systems at scale. Optimize prompts, deploy use cases, and monitor performance, no blind spots, no vibe checks. Experiment with prompts and LLM configurations before moving to production. Evaluate agentic AI systems in offline environments. Roll out GenAI features to specific user groups with guardrails, data privacy safeguards, and advanced RAG pipelines. Visualize all events triggered by agents for fast debugging. Get granular control on cost, latency, and performance. Connect to your favorite AI models, or bring your own. Speed up your workflow with out-of-the-box components built for agentic AI systems. Manage core stages of the LLM app lifecycle in one central platform. Self-hosted or hybrid deployment with SOC 2 and GDPR compliance for enterprise security. -
17
Opik
Comet
Confidently evaluate, test, and ship LLM applications with a suite of observability tools to calibrate language model outputs across your dev and production lifecycle. Log traces and spans, define and compute evaluation metrics, score LLM outputs, compare performance across app versions, and more. Record, sort, search, and understand each step your LLM app takes to generate a response. Manually annotate, view, and compare LLM responses in a user-friendly table. Log traces during development and in production. Run experiments with different prompts and evaluate against a test set. Choose and run pre-configured evaluation metrics or define your own with our convenient SDK library. Consult built-in LLM judges for complex issues like hallucination detection, factuality, and moderation. Establish reliable performance baselines with Opik's LLM unit tests, built on PyTest. Build comprehensive test suites to evaluate your entire LLM pipeline on every deployment.Starting Price: $39 per month -
18
Maxim
Maxim
Maxim is an agent simulation, evaluation, and observability platform that empowers modern AI teams to deploy agents with quality, reliability, and speed. Maxim's end-to-end evaluation and data management stack covers every stage of the AI lifecycle, from prompt engineering to pre & post release testing and observability, data-set creation & management, and fine-tuning. Use Maxim to simulate and test your multi-turn workflows on a wide variety of scenarios and across different user personas before taking your application to production. Features: Agent Simulation Agent Evaluation Prompt Playground Logging/Tracing Workflows Custom Evaluators- AI, Programmatic and Statistical Dataset Curation Human-in-the-loop Use Case: Simulate and test AI agents Evals for agentic workflows: pre and post-release Tracing and debugging multi-agent workflows Real-time alerts on performance and quality Creating robust datasets for evals and fine-tuning Human-in-the-loop workflowsStarting Price: $29/seat/month -
19
Autoblocks AI
Autoblocks AI
Autoblocks is an AI-powered platform designed to help teams in high-stakes industries like healthcare, finance, and legal to rapidly prototype, test, and deploy reliable AI models. The platform focuses on reducing risk by simulating thousands of real-world scenarios, ensuring AI agents behave predictably and reliably before being deployed. Autoblocks enables seamless collaboration between developers and subject matter experts (SMEs), automatically capturing feedback and integrating it into the development process to continuously improve models and ensure compliance with industry standards. -
20
Teammately
Teammately
Teammately is an autonomous AI agent designed to revolutionize AI development by self-iterating AI products, models, and agents to meet your objectives beyond human capabilities. It employs a scientific approach, refining and selecting optimal combinations of prompts, foundation models, and knowledge chunking. To ensure reliability, Teammately synthesizes fair test datasets and constructs dynamic LLM-as-a-judge systems tailored to your project, quantifying AI capabilities and minimizing hallucinations. The platform aligns with your goals through Product Requirement Docs (PRD), enabling focused iteration towards desired outcomes. Key features include multi-step prompting, serverless vector search, and deep iteration processes that continuously refine AI until objectives are achieved. Teammately also emphasizes efficiency by identifying the smallest viable models, reducing costs, and enhancing performance.Starting Price: $25 per month -
21
Humanloop
Humanloop
Eye-balling a few examples isn't enough. Collect end-user feedback at scale to unlock actionable insights on how to improve your models. Easily A/B test models and prompts with the improvement engine built for GPT. Prompts only get your so far. Get higher quality results by fine-tuning on your best data – no coding or data science required. Integration in a single line of code. Experiment with Claude, ChatGPT and other language model providers without touching it again. You can build defensible and innovative products on top of powerful APIs – if you have the right tools to customize the models for your customers. Copy AI fine tune models on their best data, enabling cost savings and a competitive advantage. Enabling magical product experiences that delight over 2 million active users. -
22
Giskard
Giskard
Giskard provides interfaces for AI & Business teams to evaluate and test ML models through automated tests and collaborative feedback from all stakeholders. Giskard speeds up teamwork to validate ML models and gives you peace of mind to eliminate risks of regression, drift, and bias before deploying ML models to production.Starting Price: $0 -
23
ChainForge
ChainForge
ChainForge is an open-source visual programming environment designed for prompt engineering and large language model evaluation. It enables users to assess the robustness of prompts and text-generation models beyond anecdotal evidence. Simultaneously test prompt ideas and variations across multiple LLMs to identify the most effective combinations. Evaluate response quality across different prompts, models, and settings to select the optimal configuration for specific use cases. Set up evaluation metrics and visualize results across prompts, parameters, models, and settings, facilitating data-driven decision-making. Manage multiple conversations simultaneously, template follow-up messages, and inspect outputs at each turn to refine interactions. ChainForge supports various model providers, including OpenAI, HuggingFace, Anthropic, Google PaLM2, Azure OpenAI endpoints, and locally hosted models like Alpaca and Llama. Users can adjust model settings and utilize visualization nodes. -
24
Ragas
Ragas
Ragas is an open-source framework designed to test and evaluate Large Language Model (LLM) applications. It offers automatic metrics to assess performance and robustness, synthetic test data generation tailored to specific requirements, and workflows to ensure quality during development and production monitoring. Ragas integrates seamlessly with existing stacks, providing insights to enhance LLM applications. The platform is maintained by a team of passionate individuals leveraging cutting-edge research and pragmatic engineering practices to empower visionaries redefining LLM possibilities. Synthetically generate high-quality and diverse evaluation data customized for your requirements. Evaluate and ensure the quality of your LLM application in production. Use insights to improve your application. Automatic metrics that helps you understand the performance and robustness of your LLM application.Starting Price: Free -
25
Latitude
Latitude
Latitude is an open-source prompt engineering platform designed to help product teams build, evaluate, and deploy AI models efficiently. It allows users to import and manage prompts at scale, refine them with real or synthetic data, and track the performance of AI models using LLM-as-judge or human-in-the-loop evaluations. With powerful tools for dataset management and automatic logging, Latitude simplifies the process of fine-tuning models and improving AI performance, making it an essential platform for businesses focused on deploying high-quality AI applications.Starting Price: $0 -
26
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. -
27
Traceloop
Traceloop
Traceloop is a comprehensive observability platform designed to monitor, debug, and test the quality of outputs from Large Language Models (LLMs). It offers real-time alerts for unexpected output quality changes, execution tracing for every request, and the ability to gradually roll out changes to models and prompts. Developers can debug and re-run issues from production directly in their Integrated Development Environment (IDE). Traceloop integrates seamlessly with the OpenLLMetry SDK, supporting multiple programming languages including Python, JavaScript/TypeScript, Go, and Ruby. The platform provides a range of semantic, syntactic, safety, and structural metrics to assess LLM outputs, such as QA relevancy, faithfulness, text quality, grammar correctness, redundancy detection, focus assessment, text length, word count, PII detection, secret detection, toxicity detection, regex validation, SQL validation, JSON schema validation, and code validation.Starting Price: $59 per month -
28
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 -
29
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 -
30
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 -
31
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 -
32
Arize Phoenix
Arize AI
Phoenix is an open-source observability library designed for experimentation, evaluation, and troubleshooting. It allows AI engineers and data scientists to quickly visualize their data, evaluate performance, track down issues, and export data to improve. Phoenix is built by Arize AI, the company behind the industry-leading AI observability platform, and a set of core contributors. Phoenix works with OpenTelemetry and OpenInference instrumentation. The main Phoenix package is arize-phoenix. We offer several helper packages for specific use cases. Our semantic layer is to add LLM telemetry to OpenTelemetry. Automatically instrumenting popular packages. Phoenix's open-source library supports tracing for AI applications, via manual instrumentation or through integrations with LlamaIndex, Langchain, OpenAI, and others. LLM tracing records the paths taken by requests as they propagate through multiple steps or components of an LLM application.Starting Price: Free -
33
PromptLayer
PromptLayer
The first platform built for prompt engineers. Log OpenAI requests, search usage history, track performance, and visually manage prompt templates. manage Never forget that one good prompt. GPT in prod, done right. Trusted by over 1,000 engineers to version prompts and monitor API usage. Start using your prompts in production. To get started, create an account by clicking “log in” on PromptLayer. Once logged in, click the button to create an API key and save this in a secure location. After making your first few requests, you should be able to see them in the PromptLayer dashboard! You can use PromptLayer with LangChain. LangChain is a popular Python library aimed at assisting in the development of LLM applications. It provides a lot of helpful features like chains, agents, and memory. Right now, the primary way to access PromptLayer is through our Python wrapper library that can be installed with pip.Starting Price: Free -
34
Symflower
Symflower
Symflower enhances software development by integrating static, dynamic, and symbolic analyses with Large Language Models (LLMs). This combination leverages the precision of deterministic analyses and the creativity of LLMs, resulting in higher quality and faster software development. Symflower assists in identifying the most suitable LLM for specific projects by evaluating various models against real-world scenarios, ensuring alignment with specific environments, workflows, and requirements. The platform addresses common LLM challenges by implementing automatic pre-and post-processing, which improves code quality and functionality. By providing the appropriate context through Retrieval-Augmented Generation (RAG), Symflower reduces hallucinations and enhances LLM performance. Continuous benchmarking ensures that use cases remain effective and compatible with the latest models. Additionally, Symflower accelerates fine-tuning and training data curation, offering detailed reports. -
35
MLflow
MLflow
MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. MLflow currently offers four components. Record and query experiments: code, data, config, and results. Package data science code in a format to reproduce runs on any platform. Deploy machine learning models in diverse serving environments. Store, annotate, discover, and manage models in a central repository. The MLflow Tracking component is an API and UI for logging parameters, code versions, metrics, and output files when running your machine learning code and for later visualizing the results. MLflow Tracking lets you log and query experiments using Python, REST, R API, and Java API APIs. An MLflow Project is a format for packaging data science code in a reusable and reproducible way, based primarily on conventions. In addition, the Projects component includes an API and command-line tools for running projects. -
36
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 -
37
Label Studio
Label Studio
The most flexible data annotation tool. Quickly installable. Build custom UIs or use pre-built labeling templates. Configurable layouts and templates adapt to your dataset and workflow. Detect objects on images, boxes, polygons, circular, and key points supported. Partition the image into multiple segments. Use ML models to pre-label and optimize the process. Webhooks, Python SDK, and API allow you to authenticate, create projects, import tasks, manage model predictions, and more. Save time by using predictions to assist your labeling process with ML backend integration. Connect to cloud object storage and label data there directly with S3 and GCP. Prepare and manage your dataset in our Data Manager using advanced filters. Support multiple projects, use cases, and data types in one platform. Start typing in the config, and you can quickly preview the labeling interface. At the bottom of the page, you have live serialization updates of what Label Studio expects as an input. -
38
AgentBench
AgentBench
AgentBench is an evaluation framework specifically designed to assess the capabilities and performance of autonomous AI agents. It provides a standardized set of benchmarks that test various aspects of an agent's behavior, such as task-solving ability, decision-making, adaptability, and interaction with simulated environments. By evaluating agents on tasks across different domains, AgentBench helps developers identify strengths and weaknesses in the agents’ performance, such as their ability to plan, reason, and learn from feedback. The framework offers insights into how well an agent can handle complex, real-world-like scenarios, making it useful for both research and practical development. Overall, AgentBench supports the iterative improvement of autonomous agents, ensuring they meet reliability and efficiency standards before wider application. -
39
Basalt
Basalt
Basalt is an AI-building platform that helps teams quickly create, test, and launch better AI features. With Basalt, you can prototype quickly using our no-code playground, allowing you to draft prompts with co-pilot guidance and structured sections. Iterate efficiently by saving and switching between versions and models, leveraging multi-model support and versioning. Improve your prompts with recommendations from our co-pilot. Evaluate and iterate by testing with realistic cases, upload your dataset, or let Basalt generate it for you. Run your prompt at scale on multiple test cases and build confidence with evaluators and expert evaluation sessions. Deploy seamlessly with the Basalt SDK, abstracting and deploying prompts in your codebase. Monitor by capturing logs and monitoring usage in production, and optimize by staying informed of new errors and edge cases.Starting Price: Free -
40
Handit
Handit
Handit.ai is an open source engine that continuously auto-improves your AI agents by monitoring every model, prompt, and decision in production, tagging failures in real time, and generating optimized prompts and datasets. It evaluates output quality using custom metrics, business KPIs, and LLM-as-judge grading, then automatically AB-tests each fix and presents versioned pull-request-style diffs for you to approve. With one-click deployment, instant rollback, and dashboards tying every merge to business impact, such as saved costs or user gains, Handit removes manual tuning and ensures continuous improvement on autopilot. Plugging into any environment, it delivers real-time monitoring, automatic evaluation, self-optimization through AB testing, and proof-of-effectiveness reporting. Teams have seen accuracy increases exceeding 60 %, relevance boosts over 35 %, and thousands of evaluations within days of integration.Starting Price: Free -
41
Scale Evaluation
Scale
Scale Evaluation offers a comprehensive evaluation platform tailored for developers of large language models. This platform addresses current challenges in AI model assessment, such as the scarcity of high-quality, trustworthy evaluation datasets and the lack of consistent model comparisons. By providing proprietary evaluation sets across various domains and capabilities, Scale ensures accurate model assessments without overfitting. The platform features a user-friendly interface for analyzing and reporting model performance, enabling standardized evaluations for true apples-to-apples comparisons. Additionally, Scale's network of expert human raters delivers reliable evaluations, supported by transparent metrics and quality assurance mechanisms. The platform also offers targeted evaluations with custom sets focusing on specific model concerns, facilitating precise improvements through new training data. -
42
doteval
doteval
doteval is an AI-assisted evaluation workspace that simplifies the creation of high-signal evaluations, alignment of LLM judges, and definition of rewards for reinforcement learning, all within a single platform. It offers a Cursor-like experience to edit evaluations-as-code against a YAML schema, enabling users to version evaluations across checkpoints, replace manual effort with AI-generated diffs, and compare evaluation runs on tight execution loops to align them with proprietary data. doteval supports the specification of fine-grained rubrics and aligned graders, facilitating rapid iteration and high-quality evaluation datasets. Users can confidently determine model upgrades or prompt improvements and export specifications for reinforcement learning training. It is designed to accelerate the evaluation and reward creation process by 10 to 100 times, making it a valuable tool for frontier AI teams benchmarking complex model tasks. -
43
Benchable
Benchable
Benchable is a dynamic AI tool designed for businesses and tech enthusiasts to effectively compare the performance, cost, and quality of various AI models. It allows users to benchmark leading models like GPT-4, Claude, and Gemini through custom tests, providing real-time results to help make informed decisions. With its user-friendly interface and robust analytics, Benchable streamlines the evaluation process, ensuring you find the most suitable AI solution for your needs.Starting Price: $0 -
44
TruLens
TruLens
TruLens is an open-source Python library designed to systematically evaluate and track Large Language Model (LLM) applications. It provides fine-grained instrumentation, feedback functions, and a user interface to compare and iterate on app versions, facilitating rapid development and improvement of LLM-based applications. Programmatic tools that assess the quality of inputs, outputs, and intermediate results from LLM applications, enabling scalable evaluation. Fine-grained, stack-agnostic instrumentation and comprehensive evaluations help identify failure modes and systematically iterate to improve applications. An easy-to-use interface that allows developers to compare different versions of their applications, facilitating informed decision-making and optimization. TruLens supports various use cases, including question-answering, summarization, retrieval-augmented generation, and agent-based applications.Starting Price: Free -
45
SwarmOne
SwarmOne
SwarmOne is an autonomous infrastructure platform designed to streamline the entire AI lifecycle, from training to deployment, by automating and optimizing AI workloads across any environment. With just two lines of code and a one-click hardware installation, users can initiate instant AI training, evaluation, and deployment. It supports both code and no-code workflows, enabling seamless integration with any framework, IDE, or operating system, and is compatible with any GPU brand, quantity, or generation. SwarmOne's self-setting architecture autonomously manages resource allocation, workload orchestration, and infrastructure swarming, eliminating the need for Docker, MLOps, or DevOps. Its cognitive infrastructure layer and burst-to-cloud engine ensure optimal performance, whether on-premises or in the cloud. By automating tasks that typically hinder AI model development, SwarmOne allows data scientists to focus exclusively on scientific work, maximizing GPU utilization. -
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Galileo
Galileo
Models can be opaque in understanding what data they didn’t perform well on and why. Galileo provides a host of tools for ML teams to inspect and find ML data errors 10x faster. Galileo sifts through your unlabeled data to automatically identify error patterns and data gaps in your model. We get it - ML experimentation is messy. It needs a lot of data and model changes across many runs. Track and compare your runs in one place and quickly share reports with your team. Galileo has been built to integrate with your ML ecosystem. Send a fixed dataset to your data store to retrain, send mislabeled data to your labelers, share a collaborative report, and a lot more! Galileo is purpose-built for ML teams to build better quality models, faster. -
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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. -
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Tasq.ai
Tasq.ai
Tasq.ai delivers a powerful, no-code platform for building hybrid AI workflows that combine state-of-the-art machine learning with global, decentralized human guidance, ensuring unmatched scalability, control, and precision. It enables teams to configure AI pipelines visually, breaking tasks into micro-workflows that layer automated inference and quality-assured human review. This decoupled orchestration supports diverse use cases across text, computer vision, audio, video, and structured data, with rapid deployment, adaptive sampling, and consensus-based validation built in. Key capabilities include global deployment of highly screened contributors (“Tasqers”) for unbiased, high-accuracy annotations; granular task routing and judgment aggregation to meet confidence thresholds; and seamless integration into ML ops pipelines via drag-and-drop customization. -
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Okareo
Okareo
Okareo is an AI development platform designed to help teams build, test, and monitor AI agents with confidence. It offers automated simulations to uncover edge cases, system conflicts, and failure points before deployment, ensuring that AI features are robust and reliable. With real-time error tracking and intelligent safeguards, Okareo helps prevent hallucinations and maintains accuracy in production environments. Okareo continuously fine-tunes AI using domain-specific data and live performance insights, boosting relevance, effectiveness, and user satisfaction. By turning agent behaviors into actionable insights, Okareo enables teams to surface what's working, what's not, and where to focus next, driving business value beyond mere logs. Designed for seamless collaboration and scalability, Okareo supports both small and large-scale AI projects, making it an essential tool for AI teams aiming to deliver high-quality AI applications efficiently.Starting Price: $199 per month -
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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