OpenLIT vs Lunary

Both are observability & tracing tools. Here is how they actually differ on price, billing model and deployment.

OpenLIT Lunary
Category Observability & Tracing Observability & Tracing
Our rating 4/5 3/5
Starting price $0 (Apache 2.0) Around $20-30/mo
Billing meter No usage metering event
Free plan Yes Yes
Free self-hosting Yes, free Yes, free
Best for Teams running self-hosted inference on their own GPUs, where correlating GPU health with model performance matters. Also a strong free choice for anyone wanting broad AI engineering tooling in one Apache-2.0 package. Small teams shipping RAG pipelines or chatbots who want basic tracing working this afternoon, and who will either stay small or self-host before volume becomes expensive.

Our verdict on OpenLIT

OpenLIT is the only tool in this category that takes the GPU layer seriously, and if you run your own inference that alone may decide it. Its OpenTelemetry GPU collector exports utilisation, memory, temperature and power as standard OTel signals, so you can correlate a latency regression with thermal throttling or a KV cache leak in the same dashboard - a class of problem that is completely invisible to Langfuse, LangSmith or Braintrust, because they assume you call an API. Beyond that it is unusually broad for a free tool - Apache 2.0 across the core, covering observability, evals, guardrails, prompt management, a Vault and a Playground. The trade-off is breadth over depth, since each module is lighter than a dedicated competitor, and the community is small at roughly 2,500 stars. Two things we could not resolve - an enterprise eBPF controller is referenced without clear licensing terms, and the project has recently described itself as a Harness Engineering platform without public explanation of what that means.

Full OpenLIT review →

Our verdict on Lunary

Lunary is a good starter tool that knows what it is. It is optimised for RAG pipelines and chatbots rather than trying to be a complete AI engineering platform, it is Apache 2.0 and self-hostable, and it is among the fastest things in the category to get running. Radar - which buckets responses against criteria you define - is a genuinely useful idea for spotting patterns that individual traces hide. Two caveats matter. The free tier is 1,000 events per day rather than per month, which is a meaningfully tighter constraint than the headline suggests and will not survive a real chatbot for long. And published pricing is inconsistent across sources in a way we could not resolve, with figures ranging from around $20 to $200 a month depending on where you look. Against Langfuse it is lighter on nearly every axis, and Langfuse is the better default for most teams. Lunary earns its place when speed to first trace matters more than depth.

Full Lunary review →
These two meter differently, so published prices are not comparable. Model both against your own workload →

Frequently Asked Questions

What is the main difference between OpenLIT and Lunary?

OpenLIT: Teams running self-hosted inference on their own GPUs, where correlating GPU health with model performance matters. Also a strong free choice for anyone wanting broad AI engineering tooling in one Apache-2.0 package. Lunary: Small teams shipping RAG pipelines or chatbots who want basic tracing working this afternoon, and who will either stay small or self-host before volume becomes expensive. Both sit in Observability & Tracing, so the decision usually comes down to billing model and deployment rather than raw capability.

Which is cheaper, OpenLIT or Lunary?

It depends entirely on your workload shape, because they meter differently - OpenLIT bills on no usage metering and Lunary bills on event. Published starting prices are $0 (Apache 2.0) and Around $20-30/mo respectively, but those numbers are not comparable until you apply them to the same traffic. Use our cost calculator to model both against your own request volume and span count.

Can I self-host OpenLIT or Lunary?

OpenLIT: Yes, free. Lunary: Yes, free. Free self-hosting means no licence fee, not no cost - you still own the infrastructure, upgrades and on-call.