Ashr vs Lunary
Updated March 27, 2026
Overview
Generates synthetic multi-modal user simulations to stress-test AI agents before production, catching errors manual testing misses.
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.
Key features
Core capabilities each platform advertises.
- Multi-modal synthetic testing
- Voice/text/image simulations
- Pre-production agent stress testing
- Error detection
- Automated test generation
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- Addresses critical gap in systematic testing for probabilistic AI agents
- Multi-modal coverage across voice, text, image, and file generation is rare
- Free tier lowers adoption barrier for teams evaluating the platform
- Prompt versioning with regression tracking is a strong developer experience feature
- Both founders have prior AI startup experience including a successful exit
Cons
- Very early stage with only 2 people — scaling support uncertain
- Paid pricing tiers not publicly disclosed
- Competes with Braintrust, Arize, and LangSmith expanding into agent testing
- SDK-only integration may limit adoption with non-technical teams
Pros
- Production-ready platform
- Good performance
- Active development
- Strong features
Cons
- Enterprise pricing varies
- Setup complexity
- Learning curve required
Ashr or Lunary — which should you choose?
Choose Ashr if you wantChoose if you want
- Agent testing
- Multi-modal QA
- Pre-deployment validation
- Edge case discovery
Choose Lunary if you wantChoose if you want
Compare Ashr 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.