Alternatives to Deepchecks
Compare Deepchecks alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Deepchecks in 2026. Compare features, ratings, user reviews, pricing, and more from Deepchecks competitors and alternatives in order to make an informed decision for your business.
-
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
BigPanda
BigPanda
Aggregate data from all observability, monitoring, change and topology tools. BigPanda’s Open Box Machine Learning will correlate the data into a small number of actionable insights so incidents are detected in real-time, as they form, before they escalate into outages. Accelerate incident and outage resolution by automatically identifying the probable root cause of problems. BigPanda identifies both root cause changes and infrastructure-related root causes. Resolve incidents and outages faster. BigPanda automates and streamlines the incident response lifecycle across incident triage, ticketing, notifications, and war room creation. Accelerate remediation by integrating BigPanda with enterprise runbook automation tools. Applications and cloud services are the lifeblood of every company. When there’s an outage, everyone is impacted. BigPanda cements AIOps market leadership with $190M in funding, $1.2B valuation. -
5
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. -
6
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 -
7
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 -
8
BenchLLM
BenchLLM
Use BenchLLM to evaluate your code on the fly. Build test suites for your models and generate quality reports. Choose between automated, interactive or custom evaluation strategies. We are a team of engineers who love building AI products. We don't want to compromise between the power and flexibility of AI and predictable results. We have built the open and flexible LLM evaluation tool that we have always wished we had. Run and evaluate models with simple and elegant CLI commands. Use the CLI as a testing tool for your CI/CD pipeline. Monitor models performance and detect regressions in production. Test your code on the fly. BenchLLM supports OpenAI, Langchain, and any other API out of the box. Use multiple evaluation strategies and visualize insightful reports. -
9
Vellum
Vellum AI
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. -
10
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. -
11
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 -
12
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. -
13
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 -
14
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 -
15
Respan
Respan
Respan is a self-driving observability and evaluation platform built specifically for AI agents. It enables teams to trace full execution flows, including messages, tool calls, routing decisions, memory usage, and outcomes. The platform connects observability, evaluations, and optimization into a continuous improvement loop. Metric-first evaluations allow teams to define performance standards such as accuracy, cost, reliability, and safety. Respan also includes capability and regression testing to protect stable behaviors while improving new ones. An AI-powered evaluation agent analyzes failures, identifies root causes, and recommends next steps automatically. With compliance certifications including ISO 27001, SOC 2, GDPR, and HIPAA, Respan supports secure, large-scale AI deployments across industries.Starting Price: $0/month -
16
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 -
17
TorqCloud
IntelliBridge
TorqCloud is designed to help users source, move, enrich, visualize, secure, and interact with data via AI agents. As a comprehensive AIOps solution, TorqCloud allows users to build or integrate end-to-end custom LLM applications using a low-code interface. Built to handle vast amounts of data to deliver actionable insights as a critical tool for any organization looking to stay competitive in today’s digital landscape. Our approach combines seamless integration across disciplines, an intense focus on user needs, test-and-learn methodologies that enable us to get the right product to market fast, and a close working relationship with your teams, including skills transfer and training. Starting with empathy interviews we perform stakeholder mapping exercises where we dive into the customer journey, needed behavioral changes, problem sizing, and linear unpacking. -
18
Guardrails AI
Guardrails AI
With our dashboard, you are able to go deeper into analytics that will enable you to verify all the necessary information related to entering requests into Guardrails AI. Unlock efficiency with our ready-to-use library of pre-built validators. Optimize your workflow with robust validation for diverse use cases. Empower your projects with a dynamic framework for creating, managing, and reusing custom validators. Where versatility meets ease, catering to a spectrum of innovative applications easily. By verifying and indicating where the error is, you can quickly generate a second output option. Ensures that outcomes are in line with expectations, precision, correctness, and reliability in interactions with LLMs. -
19
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 -
20
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. -
21
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 -
22
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 -
23
RagMetrics
RagMetrics
RagMetrics is a production-grade evaluation and trust platform for conversational GenAI, designed to assess AI chatbots, agents, and RAG systems before and after they go live. The platform continuously evaluates AI responses for accuracy, groundedness, hallucinations, reasoning quality, and tool-calling behavior across real conversations. RagMetrics integrates directly with existing AI stacks and monitors live interactions without disrupting user experience. It provides automated scoring, configurable metrics, and detailed diagnostics that explain when an AI response fails, why it failed, and how to fix it. Teams can run offline evaluations, A/B tests, and regression tests, as well as track performance trends in production through dashboards and alerts. The platform is model-agnostic and deployment-agnostic, supporting multiple LLMs, retrieval systems, and agent frameworks.Starting Price: $20/month -
24
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 -
25
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 -
26
Selene 1
atla
Atla's Selene 1 API offers state-of-the-art AI evaluation models, enabling developers to define custom evaluation criteria and obtain precise judgments on their AI applications' performance. Selene outperforms frontier models on commonly used evaluation benchmarks, ensuring accurate and reliable assessments. Users can customize evaluations to their specific use cases through the Alignment Platform, allowing for fine-grained analysis and tailored scoring formats. The API provides actionable critiques alongside accurate evaluation scores, facilitating seamless integration into existing workflows. Pre-built metrics, such as relevance, correctness, helpfulness, faithfulness, logical coherence, and conciseness, are available to address common evaluation scenarios, including detecting hallucinations in retrieval-augmented generation applications or comparing outputs to ground truth data. -
27
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. -
28
Seerene
Seerene
Seerene’s Digital Engineering Platform is a software analytics and process mining technology that analyzes and visualizes the software development processes in your company. It reveals weaknesses and turns your organization into a well-oiled machine, delivering software efficiently, cost-effectively, quickly, and with the highest quality. Seerene provides decision-makers with the information needed to actively drive their organization towards 360° software excellence. Reveal code that frequently contains defects and kills developer productivity. Reveal lighthouse teams and transfer their best-practice processes across the entire workforce. Reveal defect risks in release candidates with a holistic X-ray of code, development hotspots and tests. Reveal features with a mismatch between invested developer time und created user value. Reveal code that is never executed by end-users and produces unnecessary maintenance costs. -
29
Chatbot Arena
Chatbot Arena
Ask any question to two anonymous AI chatbots (ChatGPT, Gemini, Claude, Llama, and more). Choose the best response, you can keep chatting until you find a winner. If AI identity is revealed, your vote won't count. Upload an image and chat, or use text-to-image models like DALL-E 3, Flux, and Ideogram to generate images, Use RepoChat tab to chat with Github repos. Backed by over 1,000,000+ community votes, our platform ranks the best LLM and AI chatbots. Chatbot Arena is an open platform for crowdsourced AI benchmarking, hosted by researchers at UC Berkeley SkyLab and LMArena. We open source the FastChat project on GitHub and release open datasets.Starting Price: Free -
30
LLM Council
LLM Council
LLM Council is a lightweight multi-model orchestration tool that enables users to query several large language models simultaneously and synthesize their outputs into a single, higher-confidence response. Instead of relying on one AI system, it routes a prompt to a panel of models, each of which produces an independent answer before anonymously reviewing and ranking the others’ work. A designated “Chairman” model then combines the strongest insights into a unified final output, mimicking the dynamics of a panel of experts reaching consensus. It typically runs as a simple local web interface with a Python backend and React frontend and connects through aggregation services to access models from providers such as OpenAI, Google, and Anthropic. This structured peer-review workflow is designed to surface blind spots, reduce hallucinations, and improve answer reliability by introducing multiple perspectives and cross-model critique.Starting Price: $25 per month -
31
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 -
32
HumanSignal
HumanSignal
HumanSignal's Label Studio Enterprise is a comprehensive platform designed for creating high-quality labeled data and evaluating model outputs with human supervision. It supports labeling and evaluating multi-modal data, image, video, audio, text, and time series, all in one place. It offers customizable labeling interfaces with pre-built templates and powerful plugins, allowing users to tailor the UI and workflows to specific use cases. Label Studio Enterprise integrates seamlessly with popular cloud storage providers and ML/AI models, facilitating pre-annotation, AI-assisted labeling, and prediction generation for model evaluation. The Prompts feature enables users to leverage LLMs to swiftly generate accurate predictions, enabling instant labeling of thousands of tasks. It supports various labeling use cases, including text classification, named entity recognition, sentiment analysis, summarization, and image captioning.Starting Price: $99 per month -
33
UpTrain
UpTrain
Get scores for factual accuracy, context retrieval quality, guideline adherence, tonality, and many more. You can’t improve what you can’t measure. UpTrain continuously monitors your application's performance on multiple evaluation criterions and alerts you in case of any regressions with automatic root cause analysis. UpTrain enables fast and robust experimentation across multiple prompts, model providers, and custom configurations, by calculating quantitative scores for direct comparison and optimal prompt selection. Hallucinations have plagued LLMs since their inception. By quantifying degree of hallucination and quality of retrieved context, UpTrain helps to detect responses with low factual accuracy and prevent them before serving to the end-users. -
34
Amazon Bedrock Guardrails
Amazon
Amazon Bedrock Guardrails is a configurable safeguard system designed to enhance the safety and compliance of generative AI applications built on Amazon Bedrock. It enables developers to implement customized safety, privacy, and truthfulness controls across various foundation models, including those hosted within Amazon Bedrock, fine-tuned models, and self-hosted models. Guardrails provide a consistent approach to enforcing responsible AI policies by evaluating both user inputs and model responses based on defined policies. These policies include content filters for harmful text and image content, denial of specific topics, word filters for undesirable terms, sensitive information filters to redact personally identifiable information, and contextual grounding checks to detect and filter hallucinations in model responses. -
35
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. -
36
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 -
37
StackState
StackState
StackState's Topology and Relationship-Based Observability platform lets you manage your dynamic IT environment more effectively by unifying performance data from your existing monitoring tools into a single topology. Enabling you to: 1. 80% Decreased MTTR: by identifying the root cause and alerting the right teams with the correct information. 2. 65% Fewer Outages: through real-time unified observability and more planful planning. 3. 3x Faster Releases: by giving time back to developers to increase implementations. Get started today with our free guided demo: https://www.stackstate.com/schedule-a-demo -
38
Snorkel AI
Snorkel AI
AI today is blocked by lack of labeled data, not models. Unblock AI with the first data-centric AI development platform powered by a programmatic approach. Snorkel AI is leading the shift from model-centric to data-centric AI development with its unique programmatic approach. Save time and costs by replacing manual labeling with rapid, programmatic labeling. Adapt to changing data or business goals by quickly changing code, not manually re-labeling entire datasets. Develop and deploy high-quality AI models via rapid, guided iteration on the part that matters–the training data. Version and audit data like code, leading to more responsive and ethical deployments. Incorporate subject matter experts' knowledge by collaborating around a common interface, the data needed to train models. Reduce risk and meet compliance by labeling programmatically and keeping data in-house, not shipping to external annotators. -
39
BMC Helix Operations Management
BMC Software
BMC Helix Operations Management is a fully integrated, cloud-native, observability and AIOps solution designed to tackle challenging hybrid-cloud environments. Take a service-centric approach to observability data for truly effective AIOps. Combine 3rd party observability data such as metrics, events, logs, incidents, changes and topologies into a central IT data store. See service health and enable best-in-class root cause isolation via auto-generated dynamic business service models. Improve signal-to-noise ratio with AI event suppression, de-duplication, and correlation to create actionable situations. Gain immediate root cause isolation through AI probability assignments to causal nodes using data and service models. Prevent issues before they occur with Business Service Health monitoring and AI outage prediction. Troubleshoot rapidly with log enrichment and analytics. Easily request and execute automations from BMC or 3rd party tools. -
40
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. -
41
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 -
42
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. -
43
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. -
44
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. -
45
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. -
46
JFrog Insight
JFrog
JFrog has acquired CloudMunch and we are focused on integrating our solutions to empower your experience with DevOps BI and analytics. We can’t do it without your feedback and welcome you to be among the first customers to try JFrog Insight. Managing and monitoring DevOps values will now be an easy task. JFrog Insight is enterprising DevOps with the first continuous intelligence and configuration solution. This universal tool will provide a complete picture of your DevOps ecosystem and process, collect key metrics, correlate them across the diverse systems, giving actionable insights to development managers, operations teams, and compliance officers. Our R&D team is currently working on a perfect merge of CloudMunch product into JFrog’s tools stack to give you JFrog Insight – the next gen DevOps bringing BI analytics to your organization. -
47
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. -
48
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 -
49
OpsWorker
OpsWorker AI
Resolve production incidents and development issues with AI that understands your code, infrastructure, and telemetry — reducing MTTR by up to 80% and boosting engineering productivity by 50%. OpsWorker helps Software Developers, SREs, and DevOps Engineers reduce MTTR, resolve complex development issues, and manage high-incident environments. Through intelligent incident correlation, code-aware troubleshooting, and deep integration into your technical ecosystem, OpsWorker delivers actionable insights and autonomous remediation — ensuring resilient, high-performance operations across Kubernetes and Cloud workloads. Built as an AI SRE platform for modern AIOps, OpsWorker leverages AI Observability to analyze incidents across distributed systems, correlate signals from metrics, logs, traces, and deployments, and surface the most probable root cause within minutes. Designed with an EU-first approach, OpsWorker prioritizes data sovereignty and enterprise-grade security while enabling -
50
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.
