Best Observability, Prompts & Evals Tools

Arize AI provides an ML and LLM observability platform for monitoring model performance in production. For LLM applications, Arize offers trace visualization, prompt analysis, embedding drift detection, and retrieval evaluation. Their open-source Phoenix library provides local tracing and evaluation. Arize helps teams identify quality issues, debug failures, and continuously improve AI system performance.

The OpenTelemetry standard for AI. Pipes LLM telemetry into Datadog, Splunk, or any OTel backend.

Datadog's LLM Observability extends its industry-leading APM platform to AI applications. It provides end-to-end tracing from LLM calls to infrastructure metrics, prompt and completion tracking, cost analysis, and quality evaluation—all integrated with Datadog's existing monitoring, logging, and alerting stack. Ideal for enterprises already using Datadog who want unified observability across traditional and AI workloads.

Helicone is an open-source LLM observability and proxy platform. By adding a single line of code, developers get request logging, cost tracking, caching, rate limiting, and analytics for their LLM applications. Helicone supports all major LLM providers and offers both proxy and async logging modes. Popular with startups for its generous free tier and simple integration.

Braintrust is an end-to-end AI product platform trusted by companies like Notion, Stripe, and Vercel. It combines logging, evaluation datasets, prompt management, and an AI proxy with automatic caching and fallback. Braintrust's evaluation framework helps teams measure quality across prompt iterations with customizable scoring functions.

Prompt management + regression evals in one platform. SOC 2 compliant with annotation queues.

Phoenix is an open-source LLM observability and evaluation platform from Arize AI. It supports OpenTelemetry-based tracing across LLM and agent applications, with built-in evaluators, dataset management, and prompt playgrounds. Phoenix can be self-hosted with Docker or run via the Arize-hosted cloud version.

Patronus AI provides automated evaluation and testing for LLM applications. The platform detects hallucinations, toxicity, data leakage, and other failure modes using specialized evaluator models. Patronus offers pre-built evaluators for common use cases and supports custom evaluation criteria, helping enterprises ensure AI safety and quality before and after deployment.

Promptfoo is an open-source tool for testing and evaluating LLM prompts. It lets developers define test cases, run them against multiple models, compare outputs side-by-side, and catch regressions before deployment. Supports custom scoring functions, red-teaming, and CI/CD integration for automated prompt testing.

Portkey provides LLM observability alongside its gateway capabilities, offering detailed logging, metrics, and tracing for LLM API calls. Teams can monitor latency, costs, token usage, and error rates across providers, with request-level debugging and analytics dashboards for production AI applications.

Humanloop is a prompt engineering and evaluation platform that helps teams manage, version, and optimize LLM prompts. It provides prompt playgrounds, A/B testing, human feedback collection, and evaluation pipelines. Teams can track prompt performance across models and deploy optimized prompts to production.

Ragas is an open-source evaluation framework specifically designed for RAG (Retrieval-Augmented Generation) pipelines. It provides metrics for context precision, context recall, faithfulness, and answer relevancy, helping teams measure and improve the quality of their RAG systems. Ragas has become the standard evaluation toolkit for teams building production RAG applications.

Sentry provides runtime error monitoring and performance observability for AI applications. Its LLM monitoring capabilities track model calls, token usage, and latency alongside traditional error tracking. Sentry helps teams catch and debug issues in production AI pipelines with detailed stack traces and context.

DeepEval is an open-source LLM evaluation framework built for unit testing AI outputs. It provides 14+ evaluation metrics including hallucination detection, answer relevancy, and contextual recall. Integrates with pytest, supports custom metrics, and works with any LLM provider for automated quality assurance in CI/CD pipelines.

Galileo is a data intelligence platform for AI that helps teams evaluate, debug, and improve LLM applications. It provides metrics for hallucination detection, context adherence, chunk quality, and response completeness. Galileo's guardrails can be deployed in production to catch quality issues in real-time.

Open-source LLMOps platform for testing, evaluating, and monitoring AI agents. Differentiated by multi-turn agent simulation testing.

PromptLayer is a prompt management platform that provides version control, monitoring, and collaboration tools for LLM prompts. It logs every LLM request, tracks prompt templates, enables A/B testing across prompt versions, and provides a visual dashboard for prompt performance analytics.

Confident AI develops DeepEval, the most popular open-source LLM evaluation framework. DeepEval provides 14+ evaluation metrics including faithfulness, answer relevancy, contextual recall, and hallucination detection. The Confident AI platform adds collaboration features, regression testing, and continuous evaluation in CI/CD pipelines.

Maxim AI is an end-to-end LLM evaluation and observability platform aimed at engineering teams building and shipping AI agents and copilots. It combines tracing, evaluators, a prompt playground, and human-in-the-loop review workflows, and offers both managed cloud and self-hosted deployment.

Opik by Comet is an open-source LLM evaluation and observability platform. It provides tracing, evaluation scoring, dataset management, and experiment tracking for LLM applications. Opik supports automated LLM-as-judge evaluations and integrates with popular frameworks like LangChain and LlamaIndex.

Agenta is an open-source platform for prompt engineering, evaluation, and experimentation. It provides a prompt playground, version control for prompts, A/B testing, and evaluation pipelines. Teams can iterate on prompts collaboratively, track experiments, and deploy optimized prompts to production.

Lunary is an open-source LLM observability platform for monitoring AI applications in production. It provides request tracing, cost tracking, user analytics, and prompt management with a clean dashboard. Lunary can be self-hosted for data privacy and offers a managed cloud option.

Multimodal AI evaluation and observability platform with automated quality scoring across text, image, audio, and video outputs. Open-source TraceAI tracing built on OpenTelemetry.

Parea AI provides evaluation, testing, and observability for LLM applications.

Athina AI is a monitoring and evaluation platform for production LLM applications.

Generates synthetic multi-modal user simulations to stress-test AI agents before production, catching errors manual testing misses.

Datadog for agent reliability — monitors AI agents in production, surfacing root causes when agents fail, pick wrong tools, or exceed cost budgets.

AIOps agent for ML teams — automates ML infrastructure operations, debugging, and optimization.

Turns agent conversations and tool traces into actionable signals — detects hallucinations, tool-call failures, prompt injections, and user frustration.