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Langtrace vs OpenObserve

LangtraceOpenObserve

Bottom line: Langtrace for lLM app developers; OpenObserve for teams running agents in production.

LangtraceLangtrace

Open-source, OpenTelemetry-based observability for LLM applications

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OpenObserveOpenObserve

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

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Votes00
PricingFreemiumFreemium
CategoryLlm ObservabilityLlm Observability
Tags
llm-observabilityopentelemetrytracingevaluationsopen-source
opentelemetryllm tracingagent observabilitytoken costopen source
Best for
  • LLM app developers
  • RAG engineers
  • Platform teams
  • Teams running agents in production
  • Platform teams already using OpenTelemetry
  • Organisations needing LLM cost attribution
Pros
  • Fully open source and self-hostable
  • Built on the OpenTelemetry standard
  • Traces export to any observability stack
  • Supports Python and TypeScript
  • Covers models, frameworks, and vector DBs
  • 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
Cons
  • Smaller ecosystem than larger competitors
  • Fewer turnkey enterprise features
  • Evaluations can require manual effort
  • Requires OpenTelemetry familiarity for advanced setups
  • Managed cloud is less mature than incumbents
  • 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

Comparison generated from each tool's listing. Add or remove tools above to change it.