Best LLM Observability Platforms (September 2026)

1

Braintrust

Top pick

braintrust.dev

Best for Engineering teams building LLM-powered applications

Braintrust is a platform for evaluating, testing, and monitoring applications built on large language models. It provides tools for logging production traces, running automated evaluations against datasets, comparing prompt and model versions, and tracking quality regressions over time. A prompt playground allows side-by-side experimentation across different models and configurations. Braintrust is aimed at engineering and product teams building LLM-powered features who need visibility into output quality, latency, and cost as they iterate and ship changes.

• Prompt playground
• Automated evaluations
• Production trace logging

2

Fiddler AI

fiddler.ai

Best for ML teams monitoring production models and LLMs

Fiddler AI is an ML and LLM observability platform that helps teams monitor model performance, data drift, and output quality in production. It provides explainability tools to trace how models generate predictions or responses, along with dashboards for tracking accuracy, bias, and safety metrics over time. For LLM applications specifically, it offers monitoring for hallucinations, toxicity, and prompt-response quality. The platform suits ML engineering and data science teams at mid-size to large organizations that need to maintain compliance, transparency, and reliability across deployed models and generative AI applications.

• Model explainability
• Drift and performance monitoring
• Hallucination and toxicity detection

3

Galileo

rungalileo.io

Best for ML teams building and monitoring GenAI applications

Galileo is an evaluation and observability platform for large language model applications. It helps teams measure output quality, detect hallucinations, and monitor RAG pipelines and agent workflows in development and production. The platform offers metrics for factuality, relevance, and context adherence, along with tracing tools to debug prompts, retrieval steps, and model chains. It suits ML engineering and data science teams building and maintaining generative AI features who need structured evaluation and runtime monitoring to catch quality regressions before and after deployment.

• Hallucination detection
• RAG pipeline evaluation
• Prompt and chain tracing

4

WhyLabs

whylabs.ai

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

WhyLabs provides an AI observability platform for monitoring machine learning models and large language model 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 storing raw inputs, which helps address privacy concerns. The platform suits data science and ML engineering teams responsible for maintaining reliability and safety of models and generative AI applications deployed at scale.

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

5

LangSmith

Free plan

smith.langchain.com

Best for Teams building LLM apps with LangChain/LangGraph

LangSmith is a platform from LangChain for debugging, testing, evaluating, and monitoring applications built with large language models. It provides tracing of chains and agent runs, dataset creation for evaluation, prompt versioning, and dashboards to track latency, cost, and output quality in production. It integrates natively with the LangChain and LangGraph frameworks but can also be used with other LLM stacks via its SDK and API. It suits developers and teams building LLM-powered applications who need visibility into model behavior across development and production.

• Run tracing and debugging
• Dataset-based evaluation
• Production monitoring dashboards

6

TruEra

Free plan

truera.com

Best for ML and data science teams evaluating LLM apps

TruEra provides observability and evaluation tools for machine learning and LLM applications, including its open-source TruLens library for testing and tracking large language model outputs. The platform helps teams assess quality, detect hallucinations, and monitor model performance through structured evaluation metrics and feedback functions. It is aimed at data science and ML engineering teams building or deploying LLM-based applications who need visibility into model behavior before and after production release. TruEra was acquired by Snowflake in 2024, and its capabilities continue to be integrated into Snowflake's data and AI offerings.

• LLM output evaluation
• Feedback functions
• Model performance tracking

7

Weights & Biases

Free plan

wandb.ai

Best for ML teams building and evaluating LLM applications

Weights & Biases is a machine learning platform that provides tools for experiment tracking, model management, and evaluation, extended to large language models through its Weave product. It enables teams to log prompts, trace multi-step LLM calls, compare outputs, and monitor evaluation metrics across model versions. The platform integrates with common ML and LLM frameworks and supports collaborative dashboards for tracking experiments over time. It suits ML engineering and research teams already using W&B for model training who want to extend observability practices to LLM-based applications.

• Prompt and trace logging
• Experiment comparison dashboards
• Evaluation metric tracking

8

Portkey

portkey.ai

Best for Engineering teams running production LLM applications·Free trial

Portkey is an AI gateway and observability platform for teams building applications with large language models. It provides logging, tracing, and analytics across multiple LLM providers through a unified API, along with features like caching, load balancing, and fallback routing to improve reliability. Portkey also supports prompt management and guardrails for controlling model outputs. It is aimed at engineering teams running LLM-powered applications in production who need visibility into usage, costs, latency, and errors across different models and vendors.

• Request logging and tracing
• Multi-provider AI gateway
• Caching and fallback routing

9

Langfuse

Free plan

langfuse.com

Best for Teams building and debugging LLM applications

Langfuse is an open-source LLM engineering platform used to trace, monitor, and evaluate applications built on large language models. It provides tools for tracking prompts, model calls, and chains, along with debugging traces, evaluation scores, and dataset management for testing prompt changes. Langfuse can be self-hosted or used as a managed cloud service, and integrates with common LLM frameworks and SDKs. It suits developers and teams building LLM-powered applications who need visibility into production behavior, cost tracking, and prompt version management throughout the development lifecycle.

• Trace and prompt monitoring
• Evaluation and scoring tools
• Dataset and prompt versioning