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Langfuse vs Helicone

LangfuseHelicone

Bottom line: Langfuse for teams wanting open-source LLM observability; Helicone for teams wanting simple, cheap LLM logging.

LangfuseLangfuse

Open-source LLM observability and evaluation

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HeliconeHelicone

Open-source LLM observability and AI gateway in one line of code.

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Votes00
PricingFreemiumFreemium
CategoryCodingCoding
Tags
llm-observabilityopen-sourcetracingevaluationprompt-management
llm-observabilityai-gatewayopen-sourcemonitoringcaching
Best for
  • Teams wanting open-source LLM observability
  • Data-sensitive teams needing self-hosting
  • Prompt and evaluation workflows
  • Teams wanting simple, cheap LLM logging
  • Developers self-hosting an open-source gateway
  • Cost and usage monitoring across providers
Pros
  • Open source with nearly all features MIT-licensed
  • Self-host the full product free, no seat or usage caps
  • Framework-agnostic (works with or without LangChain)
  • Strong tracing, prompt management, and evaluation
  • Managed cloud with a free tier available
  • Extremely easy integration via a one-line base-URL swap
  • Open source under Apache 2.0 and free to self-host
  • Tiny Docker image that runs almost anywhere
  • Gateway features like caching, failover, and rate limiting cut cost and improve reliability
  • Generous free tier of 10,000 requests per month
Cons
  • Self-hosting still requires running infrastructure
  • Enterprise compliance features are commercial
  • Cloud Pro tier jumps significantly in price
  • Focused on observability, not app building
  • Analytics depth may need tuning for large scale
  • Product is in maintenance mode after the Mintlify acquisition, with no roadmap
  • Long-term hosted-service viability is uncertain and migration may be needed
  • Usage-based overages mean the Pro price is a floor, not a cap
  • Short retention on lower tiers (7 days free, 30 days Pro)
  • Large price jump from Pro to Team for compliance features

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