Comet vs Langfuse

Comet is an AI developer platform covering two connected needs: traditional ML experiment tracking and management, and LLM and agent observability through its Opik product. On the MLOps side, it lets data scientists track and compare training runs, version models and datasets, and monitor production models, with support for frameworks like PyTorch, TensorFlow, Hugging Face, and scikit-learn. Opik, Comet's LLM observability and evaluation platform, adds tracing across 60+ integrations, automatic error detection with an AI assistant called Ollie that recommends fixes, test suites with LLM-as-a-judge evaluation, and production monitoring dashboards, including cost tracking for coding agents like Claude Code. Opik's core feature set is available as a free, self-hostable open-source download in addition to Comet's hosted cloud plans.

Langfuse is an open-source AI engineering platform that provides a comprehensive suite of tools for evaluating and observing large language models (LLMs). It offers features such as LLM evaluations, observability, metrics, prompt management, and a playground for testing and experimentation. Langfuse also integrates with popular tools and platforms like OpenTelemetry, LangChain, OpenAI SDK, and LiteLLM.

• Covers both classic ML experiment tracking and modern LLM and agent observability under one company.
• Opik's open-source option gives teams a genuinely free, self-hosted path with the full feature set.
• Broad framework support (PyTorch, TensorFlow, Hugging Face, scikit-learn) for the MLOps side.
• Cost intelligence for coding agents like Claude Code is a distinctive feature for teams managing AI spend.

• Comprehensive suite of tools for LLM evaluation and observability
• Highly customizable and extensible, using a modular architecture and a large community of developers
• Supports a wide range of LLMs and platforms, including LangChain, OpenAI SDK, and LiteLLM
• Open-source and free to use, with a large and active community of users and developers

• Having two related but distinct products, classic MLOps and Opik, can be confusing when first evaluating the platform.
• Free cloud tiers cap data volume, such as 25k spans/month, requiring a paid plan for production-scale usage.
• Enterprise features like SSO and compliance certifications are reserved for the custom-priced Enterprise tier.

• Steep learning curve, due to the complexity and technical nature of the platform
• Limited support for non-technical users, who may find the platform difficult to use and navigate
• Dependent on the quality and availability of LLMs and other third-party services