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OpenObserve vs Langfuse

OpenObserveLangfuse

Bottom line: OpenObserve for teams running agents in production; Langfuse for teams wanting open-source LLM observability.

OpenObserveOpenObserve

OpenTelemetry-native observability for LLM calls, tools, and agent handoffs

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LangfuseLangfuse

Open-source LLM observability and evaluation

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Votes00
PricingFreemiumFreemium
CategoryLlm ObservabilityLlm Observability
Tags
opentelemetryllm tracingagent observabilitytoken costopen source
llm-observabilityopen-sourcetracingevaluationprompt-management
Best for
  • Teams running agents in production
  • Platform teams already using OpenTelemetry
  • Organisations needing LLM cost attribution
  • Teams wanting open-source LLM observability
  • Data-sensitive teams needing self-hosting
  • Prompt and evaluation workflows
Pros
  • Agent traces are standard OpenTelemetry spans, not a silo
  • Per-span token cost from your own model pricing
  • Unlimited users with no per-seat charge
  • AGPL-3.0 core with a self-hosted option
  • Self-hosted enterprise free up to 50 GB per day
  • 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
Cons
  • LLM observability is labelled preview
  • Online evaluations are an enterprise feature
  • Ingestion-based pricing needs volume forecasting
  • Self-hosting carries operational overhead
  • Payload capture raises data governance questions
  • 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

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