Laminar vs Langfuse

Laminar is an open-source, OpenTelemetry-native observability and debugging platform for AI agents. It traces LLM calls, tools, custom functions, and sub-agents as readable transcripts. Signals turn plain-language outcomes or failures into structured events across traces. Its debugger replays runs from cache to a selected point, while evaluations run against datasets locally or in CI to catch regressions. Laminar provides realtime traces, full-text search, raw SQL through its UI, CLI, or MCP, and custom dashboards. Integrations include OpenAI, Anthropic, Gemini, Cohere, LangChain, Pydantic AI, Claude Code, and Playwright. Deployment is available through managed cloud or self-hosting, including AWS or GCP with Helm charts. The Free plan is free with 1 GB data and seven-day retention. Starter costs $30 a month; Pro costs $150 a month.

Langfuse is an open-source AI engineering platform for teams debugging, analyzing, and iterating on AI agent applications. Its tracing records prompts, model responses, token use, latency, tool calls, retrieval steps, and metadata. Prompt Management stores, versions, deploys, and tests prompts, then links them to traces. Evaluation features include LLM-as-a-judge, code evaluators, user feedback, manual labeling, datasets, and experiments. Integrations include Python and JS/TS SDKs, OpenTelemetry, an API, CLI, MCP Server, and popular AI frameworks. Teams can self-host the MIT-licensed core on customer infrastructure using Docker, with options including Docker Compose, Kubernetes, AWS, and Azure. Langfuse reports annual SOC 2 Type 2 and ISO 27001 audits, external penetration tests, GDPR compliance, a DPA, and a HIPAA-ready region. Its Cloud traffic uses TLS 1.2+ and stored data uses AES-256 encryption. The free Hobby plan is free and includes 50k units per month, 30 days of data access, two users, and community support via GitHub. Core costs $29 a month, billed monthly.