DeepEval vs Lunary
Updated March 10, 2026
Overview
DeepEval is an open-source LLM evaluation framework built for unit testing AI outputs. It provides 14+ evaluation metrics including hallucination detection, answer relevancy, and contextual recall. Integrates with pytest, supports custom metrics, and works with any LLM provider for automated quality assurance in CI/CD pipelines.
Lunary is an open-source LLM observability platform for monitoring AI applications in production. It provides request tracing, cost tracking, user analytics, and prompt management with a clean dashboard. Lunary can be self-hosted for data privacy and offers a managed cloud option.
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- Open-source
- Comprehensive metrics
Cons
- Manual setup
Pros
- Production-ready platform
- Good performance
- Active development
- Strong features
Cons
- Enterprise pricing varies
- Setup complexity
- Learning curve required
Compare DeepEval and Lunary on your own traffic
Respan lets you trace LLM and agent calls across any model or framework, A/B test prompts on production traffic, and route requests across 500+ models through one gateway.