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Langtrace vs DeepEval

LangtraceDeepEval

Bottom line: Langtrace for lLM app developers; DeepEval for engineering teams treating evals like tests.

LangtraceLangtrace

Open-source, OpenTelemetry-based observability for LLM applications

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DeepEvalDeepEval

Open-source LLM evaluation framework with pytest-style testing.

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Votes00
PricingFreemiumFreemium
CategoryLlm ObservabilityLlm Observability
Tags
llm-observabilityopentelemetrytracingevaluationsopen-source
llm-evaluationtestingopen-sourceragci-cd
Best for
  • LLM app developers
  • RAG engineers
  • Platform teams
  • Engineering teams treating evals like tests
  • Teams gating deployments on LLM quality
  • RAG and agent developers
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
  • pytest-style workflow fits developer habits
  • 50+ research-backed metrics out of the box
  • Apache-2.0 and free to use
  • Covers RAG, agents, conversations, and safety
  • Integrates into CI/CD for quality gates
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
  • Eval reliability depends on judge model/config
  • Competitive, crowded evaluation category
  • Richer collaboration features require Confident AI cloud
  • LLM-as-a-judge adds model API costs
  • Requires writing and maintaining test suites

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