Langfuse vs Sentry (2026)
Updated March 10, 2026
Overview
Langfuse is an open-source LLM engineering platform for tracing, evals, and prompt management, self-hosted or on Langfuse Cloud with a free plan.
Sentry provides runtime error monitoring and performance observability for AI applications. Its LLM monitoring capabilities track model calls, token usage, and latency alongside traditional error tracking. Sentry helps teams catch and debug issues in production AI pipelines with detailed stack traces and context.
Key features
Core capabilities each platform advertises.
- Tracing for LLM calls, tool runs, and agent steps
- Python and TypeScript SDKs plus OpenTelemetry
- LLM-as-a-judge and custom evaluations
- Datasets and experiments
- Prompt management and playground
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- MIT-licensed core, self-hostable with Docker Compose or Kubernetes
- Tracing, evals, and prompt management in one tool
- Unlimited users on paid cloud plans
- Free Hobby plan with 50k units a month
- Native SDKs plus OpenTelemetry for other languages
Cons
- No LLM gateway, so routing, failover, and spend limits need a separate tool
- Hobby plan keeps 30 days of data and caps at 2 users
- Enterprise SSO and RBAC need the $300/month Teams add-on on Pro
- Self-hosting means running and scaling Langfuse's services yourself
Pros
- Developer-friendly platform
- Production-ready
- Good performance
- Active development
Cons
- Enterprise pricing varies
- Setup complexity
- Learning curve
Langfuse or Sentry — which should you choose?
Choose Langfuse if you wantChoose if you want
- Tracing and debugging LLM apps and agents
- Self-hosting LLM observability
- Versioning prompts across environments
- Scoring outputs with LLM judges and human review
Choose Sentry if you wantChoose if you want
Compare Langfuse and Sentry on your own traffic
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