Best LLM Observability Tools (September 2026)

1

WhyLabs

Top pick

whylabs.ai

Best for ML and data science teams monitoring production models·Free trial

WhyLabs is an AI observability platform that monitors machine learning models and LLM applications in production. It tracks data quality, model performance, and drift, and offers LLM-specific monitoring for issues such as hallucinations, prompt injection, and sensitive data leakage through its LangKit toolkit. Built on an open-source data logging library, it profiles data statistically to reduce storage and privacy concerns while enabling scalable monitoring. It suits data science and ML engineering teams needing to detect anomalies and maintain reliability across traditional ML and generative AI systems in production environments.

• Data drift detection
• LLM security monitoring
• Statistical data profiling

2

Galileo

rungalileo.io

Best for ML teams building and monitoring LLM applications

Galileo is an evaluation and observability platform for teams building applications on large language models. It helps detect hallucinations, monitor output quality, and debug model behavior across the development lifecycle, from prompt engineering to production monitoring. The platform provides metrics and guardrails for assessing RAG pipelines and agentic workflows, along with dashboards for tracing requests and analyzing failure patterns. Galileo is aimed at machine learning engineers and data science teams working on generative AI applications who need visibility into model performance and reliability before and after deployment.

• Hallucination detection
• RAG evaluation metrics
• Production monitoring dashboards

3

Portkey

portkey.ai

Best for Engineering teams running production LLM apps·Free trial

Portkey is an AI gateway and observability platform for teams building applications with large language models. It provides logging, tracing, and monitoring across multiple LLM providers through a unified API, along with caching, request retries, and load balancing to improve reliability. Portkey tracks latency, cost, and usage metrics, and offers tools for prompt management and version control. It suits engineering teams running production LLM applications who need visibility into model performance, spend, and failures across different providers, as well as guardrails for governance and security.

• Multi-provider LLM tracing
• Cost and latency monitoring
• Prompt management and versioning

4

LangSmith

smith.langchain.com

Best for Developers building LLM applications with LangChain·Free trial

LangSmith is a platform from LangChain for debugging, testing, and monitoring applications built with large language models. It provides tracing of chains and agent calls, allowing developers to inspect intermediate steps, latency, and token usage. The platform supports dataset creation and evaluation workflows to test prompt and model changes before deployment, along with monitoring dashboards for production applications. LangSmith integrates closely with the LangChain framework but can also be used with other LLM application code. It suits development teams building and iterating on LLM-powered applications who need visibility into execution and quality.

• Execution tracing
• Prompt evaluation and testing
• Production monitoring dashboards

5

PromptLayer

promptlayer.com

Best for Teams building and iterating on LLM prompts·Free trial

PromptLayer is a platform for managing, tracking, and evaluating prompts used in large language model applications. It logs LLM requests and responses, allowing teams to search history, compare prompt versions, and monitor performance over time. The tool includes a visual prompt registry that lets non-engineers edit and test prompts without changing code, along with scoring and evaluation features to assess output quality. PromptLayer suits engineering and product teams building LLM-powered applications who need visibility into prompt behavior, version control, and collaboration between technical and non-technical stakeholders.

• Prompt version tracking
• Request logging and search
• Output evaluation and scoring

6

Humanloop

humanloop.com

Best for Engineering teams building LLM-powered applications·Free trial

Humanloop is a development platform for building and managing applications powered by large language models. It provides tools for prompt engineering, versioning, and evaluation, alongside observability features for logging and monitoring model requests and responses in production. Teams can compare prompt and model versions, collect human and automated feedback, and track performance over time to identify regressions or quality issues. The platform is aimed at engineering and product teams building LLM-based features who need visibility into how prompts and models behave across development and deployment stages.

• Prompt versioning
• Evaluation tools
• Production monitoring

7

Athina AI

athina.ai

Best for Teams building and monitoring LLM applications·Free trial

Athina AI is an observability and evaluation platform for teams building applications with large language models. It provides tools to log, trace, and monitor LLM requests in production, along with evaluation frameworks to test prompts and model outputs for accuracy, safety, and consistency. The platform supports dataset management, prompt experimentation, and analytics dashboards that help teams identify regressions or failures. It is suited to engineering and applied AI teams that need visibility into LLM application behavior across development and production environments.

• Request logging and tracing
• Prompt and output evaluation
• Analytics dashboards