Best LLM Observability Tools (September 2026)

1

Portkey

Top pick

portkey.ai

Best for Engineering teams running production LLM applications·Free trial

Portkey is an AI gateway and observability platform for teams building applications on large language models. It provides logging, tracing, and monitoring across multiple LLM providers through a unified API, along with features like caching, load balancing, fallbacks, and cost tracking. Portkey also supports prompt management and versioning, allowing teams to test and iterate on prompts. It is suited for engineering teams running LLM-powered applications in production who need visibility into latency, errors, usage, and spend across different models and vendors.

• Multi-provider request logging
• Cost and latency tracking
• Prompt management and versioning

2

Humanloop

humanloop.com

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

Humanloop is a development platform for teams building applications with large language models. It provides tools for prompt management, versioning, and evaluation, alongside observability features that let engineers log model inputs and outputs, monitor performance over time, and collect human or automated feedback. The platform supports collaboration between technical and non-technical stakeholders when iterating on prompts. It is aimed at product and engineering teams building LLM-powered features who need visibility into model behavior and a structured workflow for testing changes before deployment.

• Prompt management & versioning
• Logging and monitoring
• Evaluation and feedback collection

3

Lunary

lunary.ai

Best for Teams building production LLM applications·Free trial

Lunary is an observability and analytics platform for applications built on large language models. It provides tracing, logging, and monitoring of LLM calls, allowing teams to track conversations, debug errors, and evaluate model outputs over time. The platform includes features for prompt management, cost and usage tracking, and user feedback collection, helping developers identify issues and refine prompts. Lunary supports integration with popular LLM frameworks and APIs, making it suited for engineering teams building and maintaining production LLM applications who need visibility into performance, reliability, and costs.

• Request tracing and logging
• Prompt management
• Cost and usage tracking

4

Datadog LLM Observability

datadoghq.com

Best for Engineering teams already using Datadog for APM·Free trial

Datadog LLM Observability is a module within the Datadog platform that monitors large language model applications in production. It traces prompts and responses across chains and agents, surfaces latency, token usage, and cost metrics, and flags quality issues such as hallucinations, toxicity, or failed tool calls. Because it sits inside Datadog's broader observability suite, teams can correlate LLM traces with infrastructure, APM, and log data already collected. It suits engineering and platform teams running generative AI features who already rely on Datadog for application monitoring and want unified visibility.

• Prompt and chain tracing
• Cost and token tracking
• Quality and safety evaluations

5

Helicone

Free plan

helicone.ai

Best for Engineering teams building LLM-powered applications

Helicone is an open-source observability platform for applications built on large language models. It logs API requests and responses, tracking metrics such as latency, token usage, and cost across providers like OpenAI and Anthropic. The platform offers dashboards for monitoring usage patterns, tools for debugging prompts, caching to reduce redundant calls, and integrations that require minimal code changes to implement. It suits engineering teams building LLM-powered products who need visibility into performance, spend, and reliability of their model calls, from early-stage prototypes through production deployments.

• Request logging and tracing
• Cost and token tracking
• Prompt caching

6

LangSmith

smith.langchain.com

Best for Teams building LLM apps with LangChain·Free trial

LangSmith is a platform from LangChain for debugging, testing, evaluating, and monitoring applications built on large language models. It provides tracing of chains and agent steps, allowing developers to inspect prompts, outputs, and latency at each stage of execution. The platform supports dataset creation and evaluation runs to compare model or prompt versions, along with monitoring dashboards for production usage. It integrates closely with the LangChain framework but can also be used independently with other LLM stacks. It suits engineering teams building and maintaining LLM-powered applications who need visibility into application behavior.

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

7

WhyLabs

whylabs.ai

Best for ML and data science teams monitoring models in production·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, toxicity, and prompt injection attempts. Built on the open-source whylogs library, it profiles data without requiring raw data to leave a customer's environment, which suits organizations with privacy constraints. The platform provides dashboards and alerting to help data science and ML engineering teams detect anomalies and degradation across models and pipelines at scale.

• Data and model drift detection
• LLM safety and hallucination monitoring
• Privacy-preserving data profiling

8

Galileo

rungalileo.io

Best for ML teams building and monitoring LLM applications

Galileo is an LLM observability and evaluation platform aimed at teams building generative AI applications. It provides tools for detecting hallucinations, monitoring model outputs in production, and evaluating prompts and retrieval-augmented generation pipelines. The platform offers metrics and guardrails to help identify quality issues before and after deployment, along with dashboards for tracing and debugging model behavior. It is designed for machine learning engineers and data science teams working with large language models who need visibility into output accuracy, reliability, and safety across development and production environments.

• Hallucination detection
• Prompt and RAG evaluation
• Production monitoring dashboards