Alternatives to Traceloop
Compare Traceloop alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Traceloop in 2026. Compare features, ratings, user reviews, pricing, and more from Traceloop competitors and alternatives in order to make an informed decision for your business.
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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. -
2
Arize AI
Arize AI
Automatically discover issues, diagnose problems, and improve models with Arize’s machine learning observability platform. Machine learning systems address mission critical needs for businesses and their customers every day, yet often fail to perform in the real world. Arize is an end-to-end observability platform to accelerate detecting and resolving issues for your AI models at large. Seamlessly enable observability for any model, from any platform, in any environment. Lightweight SDKs to send training, validation, and production datasets. Link real-time or delayed ground truth to predictions. Gain foresight and confidence that your models will perform as expected once deployed. Proactively catch any performance degradation, data/prediction drift, and quality issues before they spiral. Reduce the time to resolution (MTTR) for even the most complex models with flexible, easy-to-use tools for root cause analysis.Starting Price: $50/month -
3
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. -
4
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. -
5
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. -
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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. -
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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 -
8
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 -
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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 -
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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 -
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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 -
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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 -
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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. -
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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 -
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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 -
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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 -
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Instructor
Instructor
Instructor is a tool that enables developers to extract structured data from natural language using Large Language Models (LLMs). Integrating with Python's Pydantic library allows users to define desired output structures through type hints, facilitating schema validation and seamless integration with IDEs. Instructor supports various LLM providers, including OpenAI, Anthropic, Litellm, and Cohere, offering flexibility in implementation. Its customizable nature permits the definition of validators and custom error messages, enhancing data validation processes. Instructor is trusted by engineers from platforms like Langflow, underscoring its reliability and effectiveness in managing structured outputs powered by LLMs. Instructor is powered by Pydantic, which is powered by type hints. Schema validation and prompting are controlled by type annotations; less to learn, and less code to write, and it integrates with your IDE.Starting Price: Free -
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Beaconcure
Beaconcure
Intelligent clinical data analysis. Accelerate and de-risk regulatory approvals with our automated analytics and validation platform. The clinical data analytics software unlike no other improve data quality & de-risk your submission. Automate your manual quality process, Avoid QC rerun, Eliminate data duplication or inconsistencies, Ensure data traceability, Transparent quality process. Accelerate time to market & generate revenue earlier. reduce data validation time. free up resources, reduce cost. Accelerate regulatory approval. The need for accurate clinical data validation and high-quality data output has never been more urgent. Verify manages and analyzes clinical data to mitigate risk and expedite approval of a new drug and vaccine. Ensure quality, speed & success. -
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Sup AI
Sup AI
Sup AI is a multi-LLM platform that merges outputs from several top large language models, such as GPT, Claude, Llama, and more, to generate richer, more accurate, and better-validated answers than any single model could provide. It applies real-time “logprob confidence scoring,” analyzing each token’s probability to detect uncertainty or hallucination; when a model’s confidence falls below a threshold, the response is halted, helping ensure that delivered answers remain high-quality and trustworthy. Sup’s “multi-model fusion” then compares, contrasts, and consolidates outputs from different models, cross-verifying and synthesizing the best parts into a final result. Sup also supports “multimodal RAG” (retrieval-augmented generation) to incorporate external data (text, PDFs, images) into context-aware responses, giving the AI access to factual sources and helping it “never forget” relevant information.Starting Price: $20 per month -
20
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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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 -
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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. -
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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. -
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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 -
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Codeanywhere
Codeanywhere
Our Cloud IDE saves you time by deploying a development environment in seconds, enabling you to code, learn, build, and collaborate on your projects. With our amazing web-based code editor in Codeanywhere, you will forget you ever used any other code editor. All major programming languages fully supported, including JavaScript/TypeScript, PHP, Python, Ruby, Go, Java, C/C++, C# any many other. Intelligent editing features such as auto-complete, code refactor, go to definition, rename symbol and many others. Debug code with break points, call stacks and interactive console. Fully featured Git client. Expandable with vast amount of existing extensions. You can also spin up powerful containers in seconds, that can be fully preconfigured for the programming environment of your choice. Develop and run your code on our infrastructure with full sudo access. Prebuilt development environments for all major programming languages, packed with tools and database preinstalled.Starting Price: $2.50 per user per month -
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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 -
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PydanticAI
Pydantic
PydanticAI is a Python-based agent framework designed to simplify the development of production-grade applications using generative AI. Built by the team behind Pydantic, the framework integrates seamlessly with popular AI models such as OpenAI, Anthropic, Gemini, and others. It offers type-safe design, real-time debugging, and performance monitoring through Pydantic Logfire. PydanticAI also provides structured responses by leveraging Pydantic to validate model outputs, ensuring consistency. The framework includes a dependency injection system to support iterative development and testing, as well as the ability to stream LLM outputs for rapid validation. It is ideal for AI-driven projects that require flexible and efficient agent composition using standard Python best practices. We built PydanticAI with one simple aim: to bring that FastAPI feeling to GenAI app development.Starting Price: Free -
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Sift
Sift
Sift is a unified observability platform purpose-built for modern, mission-critical hardware systems that provides engineers with infrastructure and tooling to ingest, store, normalize, and explore high-frequency, high-cardinality telemetry and event data from design, validation, manufacturing, and operations in a single source of truth rather than fragmented dashboards and scripts; it centralizes diverse data types, aligns signals across subsystems, and structures information for fast search, visual review, and traceability so teams can detect anomalies, perform root-cause analysis, automate verification and validation, and debug hardware with real-time precision. It supports automated data review, no-code visualization and querying of massive datasets, continuous anomaly detection, and integration with engineering workflows, including CI/CD pipelines and tooling, while enabling telemetry governance, collaboration, reporting, and knowledge capture across siloed teams. -
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Scribens
Scribens
Scribens checks the grammar of your texts and finds spelling mistakes. Avoid copy-pasting and keep the formatting of your original texts. Correct your texts on Gmail, Hotmail, Yahoo, Facebook, Twitter, LinkedIn, forums, blogs, etc. Use Scribens as a seamless extension of Microsoft Word, Outlook, PowerPoint, Excel, OpenOffice, or LibreOffice. Scribens corrects over 250 types of common grammar and spelling mistakes, including verbs, nouns, pronouns, prepositions, homonyms, punctuation, typography, and more. Online corrections are included with explanations in order to help the user progress his or her English writing skills. Scribens employs a sophisticated syntactical recognition algorithm that detects even the most subtle errors in a text. In offering you advanced correction software, Scribens allows you to significantly improve the quality of your writing. Scribens detects stylistic elements such as repetitions, run-on sentences, redundancies, and more.Starting Price: €9.90 per month -
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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. -
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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. -
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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. -
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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. -
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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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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. -
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NuExtract
NuExtract
NuExtract is a large language model specialized in extracting structured information from documents of any format, including raw text, scanned images, PDFs, PowerPoints, spreadsheets, and more, supporting over a dozen languages and mixed‑language inputs. It delivers JSON‑formatted output that faithfully follows user‑defined templates, with built‑in verification and null‑value handling to minimize hallucinations. Users define extraction tasks by creating a template, either by describing the desired fields or importing existing schemas—and can improve accuracy by adding document, output examples in the example set. The NuExtract Platform provides an intuitive workspace for designing templates, testing extractions in a playground, managing teaching examples, and fine‑tuning settings such as model temperature and document rasterization DPI. Once validated, projects can be deployed via a RESTful API endpoint that processes documents in real time.Starting Price: $5 per 1M tokens -
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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. -
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Apidog
Apidog
Apidog is a complete set of tools that connects the entire API lifecycle, helping R&D teams implement best practices for API Design-first development. Design and debug APIs in a powerful visual editor. Describe and debug easily with JSON Schema support. Automate API lifecycle with Apidog's test generation from API specs, visual assertion, built-in response validation, and CI/CD. Generate visually appealing API documentation, publish to custom domain or securely share with collaborative teams. Local and cloud mock engine generate reasonable mock data according to field names and specifications without writing scripts. Quality tools have the power to unite your entire team, while ensuring that no task is needlessly repeated. Effortlessly describe your API as you test it, and generate JSON/XML schemas with a simple click. Generate test cases from APIs, add assertions visually, and create test scenarios with branches and iterations easily.Starting Price: $9 per user per month -
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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. -
40
UHRS (Universal Human Relevance System)
Microsoft
When you need transcription, data validation, classification, sentiment analysis, or other related tasks, UHRS can give you what you need. We provide human intelligence to train machine learning models to help you solve some of your most challenging problems. We make it easy for judges to access UHRS anywhere, at any time. All that’s needed is an internet connection, and judges are good to go. Work on tasks like video annotation in just a few minutes. With UHRS, you can classify thousands of images quickly and easily. Train your products and tools with improved image detection, boundary recognition, and more with high quality annotated image data. Classify images, semantic segmentation, object detection. Validating audio to text, conversation, and relevance. Identify sentiment of a tweet, and document classification. Ad hoc data collection tasks, information correction/moderation, and survey. -
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Wardstone
JRL Software LTD
Wardstone is an LLM security API that sits between applications and language model providers, scanning inputs and outputs for threats across four categories in a single call: prompt attacks, content violations, data leakage, and unknown links. It detects jailbreaks, prompt injections, harmful content (hate, violence, self-harm), PII (SSNs, credit cards, emails, phone numbers), and suspicious URLs. Each response returns risk bands per category with sub-30ms latency. Works with any LLM provider. REST API with SDKs for TypeScript, Python, Go, Ruby, PHP, Java, and C#. Free tier at 10,000 calls/month, no credit card required. Includes a browser-based playground for testing.Starting Price: $0/month -
42
Dynamiq
Dynamiq
Dynamiq is a platform built for engineers and data scientists to build, deploy, test, monitor and fine-tune Large Language Models for any use case the enterprise wants to tackle. Key features: 🛠️ Workflows: Build GenAI workflows in a low-code interface to automate tasks at scale 🧠 Knowledge & RAG: Create custom RAG knowledge bases and deploy vector DBs in minutes 🤖 Agents Ops: Create custom LLM agents to solve complex task and connect them to your internal APIs 📈 Observability: Log all interactions, use large-scale LLM quality evaluations 🦺 Guardrails: Precise and reliable LLM outputs with pre-built validators, detection of sensitive content, and data leak prevention 📻 Fine-tuning: Fine-tune proprietary LLM models to make them your ownStarting Price: $125/month -
43
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 -
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3D Repo
3D Repo
Record 3D pins to identify issues and assign them to various parties on the project for streamlined project management with the Issue Tracker. Issues are highlighted with a colour, specific to the party that it has been assigned to. SafetiBase is the collaborative way to share and use Health and Safety information and project risks, associating them directly to the model. SafetiBase conforms to the newly published specification for ‘collaborative sharing and use of structured health and safety information using BIM’ (Publicly Available Specification PAS 1192-6). A simple way for users to validate data and group model elements together for easy progress tracking and more reliable data outputs for the client. With its ease of use, Smart Groups democratises the data validation process regardless of software knowledge. Detect changes in 3D models regardless of their file type or underlying data structure.Starting Price: $45.91 per user per month -
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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. -
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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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Bitdive
Bitdive
BitDive is a zero-code quality and test automation platform for Java, Kotlin, Spring Boot and other JVM-based applications that captures real executions and converts them into reusable, deterministic test scenarios you can replay in CI, staging or on a developer machine without writing or maintaining test code. BitDive runs as a lightweight library dependency and records full context from real traffic including HTTP/gRPC requests and responses, method calls, SQL queries with parameters and results, service interactions and timings, enabling deep method-level observability, distributed tracing, performance profiling and semantic drift detection. Its capture-replay-verify loop lets teams automatically generate regression suites and JUnit tests from actual executions, reproduce and debug production bugs locally with full execution chains, eliminate fragile mocks and flaky tests, and validate behavior changes before deployment. BitDive also visualizes service maps and heatmaps.Starting Price: Free -
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Emmett
Meerkat
Emmett is Meerkat's tecnnology for the detection and recognition of texts in images. Available as an API for easy integration with other software via HTTP calls. Features Quality Assessment: Assess the document quality to perform OCR, improving recognition results Structured information: Obtain categorized document data for Brazilian IDs, passports coming soon Extensibility: Extract information from ID and various other documents Data Validation: Look for information in unstructured documents such as proof of residence Public databases query: Check information against public personal information databases -
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SUPA
SUPA
Supercharge your AI with human expertise. SUPA is here to help you streamline your data at any stage: collection, curation, annotation, model validation and human feedback. Better data, better AI. SUPA is trusted by AI teams to solve their human data needs. Our lightning-fast machine-led labeling platform integrates with our diverse workforce to provide high-quality data at scale, making it the most cost-efficient solution for your AI. We do next-gen labeling for next-gen AI. Our use cases range from LLM generation, data curation, Segment Anything (SAM) output validation to sketch generation and semantic segmentation. -
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Guide Labs
Guide Labs
Guide Labs is developing a new class of interpretable AI systems and foundation models that humans can reliably debug, trust, and understand. Our models are engineered to produce human-understandable factors for any output, provide reliable context citations, and specify which training data influences the generated output. This approach addresses issues in current AI systems, which often produce explanations unrelated to their outputs, are difficult to debug, and are challenging to control and align. The Guide Labs team comprises experts with over 20 years of experience in interpretable machine learning. We have developed the first interpretable generative diffusion model and large language model. We are rethinking the model architecture, loss function, and entire pipeline to constrain the model training process such that the models we get are more easily understandable, their errors easier to identify and fix, and easy to align.
