LLM Observability & AI Evaluation Tools
LLM observability and evaluation tools show what AI applications and agents actually do in production. They trace each request through prompts, retrieval steps, model calls and tool calls, so teams can see why an answer was produced and where it went wrong.
Evaluation features test quality before and after release: measuring accuracy, relevance and hallucination rates, comparing prompt and model versions, and catching regressions. Monitoring features track latency, errors, token usage and cost, and alert when behaviour drifts.
As more teams move generative AI and agents from pilots into production, these tools become the way to prove an application is reliable, keep costs under control, and give security and compliance teams a record of what the AI did.
Compare 34 llm observability & ai evaluation tools below, or open the full directory to filter by your whole stack.
Alyx
Arize AIAn AI teammate designed to help debug, shorten the knowledge loop, and build AI applications with greater confidence and speed.
Arize AX
Arize AIEnterprise AI engineering platform for ML, computer vision, and generative AI applications, offering deep visibility and evaluation capabilities.
Arize Phoenix
Arize AIOpen-source platform for LLM tracing and evaluation, built on OpenTelemetry for transparent, framework-agnostic, and vendor-lock-in-free AI application development.
Arize Platform
Arize AIA comprehensive platform for monitoring, troubleshooting, and improving AI and machine learning models in production environments.
Velvet
Arize AIAn AI gateway designed to help engineers analyze, evaluate, and monitor AI-powered features in production environments by acting as a proxy.
Arthur AI Monitoring Platform
Arthur AIArthur AI provides a platform for monitoring and managing the performance of AI models in production, ensuring fairness and transparency. ServiceNow's AI Platform enables businesses to automate workflows and improve deci
Langfuse Cloud
ClickHouseLLM observability and evaluations for reliable AI applications and agents, now part of ClickHouse.
Coder AI Bridge
CoderA gateway for AI requests, handling authentication, observability, and policy enforcement for AI agents.
LLM Mesh
DataikuAbstracts and switches LLM providers with centralized routing, quotas, monitoring, and safety controls for managing cost and risk at scale.
Agentic Observability
Fiddler AIProvides end-to-end visibility across the agentic hierarchy, making AI agents interpretable to understand system behaviors, dependencies, and outcomes.
Control Plane for Agents
Fiddler AIOffers visibility, context, and control across the agentic lifecycle with observability, guardrails, and governance for autonomous AI systems.
Fiddler AI Observability and Security platform
Fiddler AIProvides unified visibility, context, and control for AI applications, monitoring performance, behavior, and risks throughout their lifecycle.
Fiddler Trust Service
Fiddler AIStrengthens AI guardrails and LLM monitoring, mitigating LLM security risks by scoring extensive metrics for advanced use cases and compliance.
Guardrails
Fiddler AIEnsures AI safety and compliance by protecting and enforcing AI applications with guardrails that prevent costly incidents in real-time.
Google Cloud Vertex AI
Google CloudGoogle Cloud Vertex AI is a unified platform for building, deploying, and managing machine learning models. It offers a range of tools and services for data preparation, model training, and model deployment. By integrati
Jasper IQ
JasperA rich context hub designed to maintain quality and authenticity in AI-generated content, ensuring brand voice and factual accuracy.
Generative AI Solutions
LabelboxTailored tools for working with large language models and generative systems, including instruction tuning, output evaluation, and human feedback.
LangSmith Evaluation
LangChainTools to score and improve the performance of AI agents.
LangSmith Observability
LangChainProvides deep visibility into AI agent behavior through tracing execution paths and runtime metrics.
LangSmith Platform
LangChainA platform for observing, evaluating, and deploying AI agents throughout the development lifecycle.
Okahu AI Observability
OkahuA platform for observing AI workloads, offering insights into performance, reliability, and cost-effectiveness for generative AI applications.
AI Evaluation Platform
Patronus AIComprehensive platform for evaluating, monitoring, and improving AI models throughout their lifecycle.
AI Observability
Patronus AIProvides deep insights into AI model behavior and operational performance for proactive issue resolution.
LLM Testing and Monitoring
Patronus AISpecialized tools for testing and continuously monitoring the performance and safety of Large Language Models.
Sapient Bodhi
Publicis SapientBuilds and orchestrates enterprise-ready AI agents with built-in context, controls, and observability for secure production deployment.
Snorkel AI Data Development Platform
Snorkel AIA comprehensive platform for building, evaluating, and improving AI models and agents through data-centric development and programmatic labeling.
Snorkel Evaluate
Snorkel AIA component of the platform for building deep, targeted evaluators to assess and improve the performance of AI models and agentic systems.
Agent Cost Observability
TensorcostProvides per-agent and per-workflow attribution for AI agent costs, with anomaly detection and alerts.
UiPath AI Center
UiPathUiPath AI Center provides a platform to deploy, manage, and govern AI models within the UiPath RPA platform. Integrating with Microsoft Azure Cognitive Services gives UiPath users access to Azure's robust suite of pre-tr
W&B Reports
Weights & BiasesGenerates interactive reports and dashboards to visualize ML experiments and model performance.
What to look for in llm observability & ai evaluation tools
- End-to-end tracing across prompts, retrieval, model calls, tool calls and agent steps.
- Evaluation: automated and human scoring for accuracy, relevance and hallucinations.
- Experimentation: compare prompts, models and versions on the same test sets.
- Production monitoring: latency, errors, token usage and cost, with alerts.
- Feedback capture: collect user ratings and turn them into test cases.
- Privacy controls: redaction of sensitive data in logged prompts and responses.
- Integrations with your LLM providers, agent frameworks and existing monitoring stack.
Frequently asked questions
What is LLM observability?
LLM observability is the ability to see and understand how an AI application behaves in production: tracing every request through its prompts, retrieval and model calls, and monitoring quality, latency, errors and cost.
What is AI evaluation?
AI evaluation measures how well a model or AI application performs on representative tasks — for example accuracy, relevance and hallucination rate — so teams can compare versions and catch regressions before users do.
How is LLM observability different from traditional APM?
Application performance monitoring tracks speed and errors. LLM observability also tracks what the model said and why: the prompts, retrieved context, outputs and their quality, plus token usage and cost.
When do teams need these tools?
As soon as an AI application or agent serves real users. Without tracing and evaluation it is hard to explain wrong answers, prove quality to stakeholders, or keep model costs predictable as usage grows.