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Galileo vs DeepEval

GalileoDeepEval

Bottom line: Galileo for teams shipping GenAI apps to production; DeepEval for engineering teams treating evals like tests.

GalileoGalileo

Evaluation intelligence and observability platform for GenAI apps and agents

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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-evaluationobservabilityguardrailsgenai-metricsagent-monitoring
llm-evaluationtestingopen-sourceragci-cd
Best for
  • Teams shipping GenAI apps to production
  • Organizations needing evaluation rigor
  • Agent developers wanting guardrails
  • Engineering teams treating evals like tests
  • Teams gating deployments on LLM quality
  • RAG and agent developers
Pros
  • Purpose-built Luna evaluation models (fast, cheaper than LLM-judge)
  • 20+ research-backed metrics
  • Covers evaluation, observability, and guardrails
  • Polished console with minimal setup
  • Free plan with 5,000 traces/month
  • 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
  • Pro at $100/month may be steep for small teams
  • Free plan lacks RBAC and advanced analytics
  • Managed platform hosts your evaluation data (unless enterprise self-host)
  • Text/GenAI-focused
  • Advanced features gated to higher tiers
  • 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

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