Lunary vs Respan
Updated March 10, 2026
Overview
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.
Respan provides comprehensive LLM observability with real-time monitoring, tracing, and debugging for AI applications in production. It tracks prompts, completions, latency, cost, and quality metrics across all LLM providers, with built-in evaluation tools, prompt management, and alerting. Respan gives engineering teams full visibility into their AI stack from a single dashboard.
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- Production-ready platform
- Good performance
- Active development
- Strong features
Cons
- Enterprise pricing varies
- Setup complexity
- Learning curve required
Pros
- Unified observability across all LLM providers in one dashboard
- Real-time alerting for cost spikes, latency, and quality issues
- Built-in evaluation tools for automated quality scoring
- Prompt management with versioning and A/B testing
- Easy integration with popular AI frameworks
Cons
- Newer platform building out integrations with more frameworks
- Advanced evaluation features require configuration effort
- Smaller community compared to established observability tools
Compare Lunary and Respan 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.