OpenLIT vs LangSmith
Both are observability & tracing tools. Here is how they actually differ on price, billing model and deployment.
OpenLIT
Apache-2.0 OpenTelemetry-native platform covering LLM tracing, evals, prompts, guardrails and a Vault - plus the one thing nearly every competitor ignores entirely, GPU monitoring for self-hosted inference.
LangSmith
LangChain's proprietary observability and eval platform. Turnkey and deeply integrated with LangChain and LangGraph - but closed-source, and the trace bill explodes at production scale.
| OpenLIT | LangSmith | |
|---|---|---|
| Category | Observability & Tracing | Observability & Tracing |
| Our rating | 4/5 | 3/5 |
| Starting price | $0 (Apache 2.0) | $39/seat/mo |
| Billing meter | No usage metering | seat |
| Free plan | Yes | Yes |
| Free self-hosting | Yes, free | No or paid tier only |
| 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. | Teams already all-in on LangChain and LangGraph who want the tightest-integrated observability and don't mind the bill |
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 LangSmith
The most turnkey observability platform if you already live in LangChain and LangGraph - the tracing is zero-config and the eval tooling is genuinely good. But it's closed source, self-hosting is Enterprise-only, and the trace bill is roughly 25x Langfuse at scale. It's great until you scale or want out, and you can't self-host your way around either problem.
Full LangSmith review →Frequently Asked Questions
What is the main difference between OpenLIT and LangSmith?
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. LangSmith: Teams already all-in on LangChain and LangGraph who want the tightest-integrated observability and don't mind the bill 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 LangSmith?
It depends entirely on your workload shape, because they meter differently - OpenLIT bills on no usage metering and LangSmith bills on seat. Published starting prices are $0 (Apache 2.0) and $39/seat/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 LangSmith?
OpenLIT: Yes, free. LangSmith: No or paid tier only. Free self-hosting means no licence fee, not no cost - you still own the infrastructure, upgrades and on-call.