OpenLIT vs Datadog LLM Observability
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.
Datadog LLM Observability
LLM tracing bolted onto the Datadog APM platform. Bills per LLM span rather than per trace, which is the single most misunderstood thing about it - agentic workloads can burn the 40,000-span free tier in a day.
| OpenLIT | Datadog LLM Observability | |
|---|---|---|
| Category | Observability & Tracing | Observability & Tracing |
| Our rating | 4/5 | 3/5 |
| Starting price | $0 (Apache 2.0) | $160/mo |
| Billing meter | No usage metering | span |
| 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 standardised on Datadog for infrastructure and APM, running LLM features inside a larger system, who value one pane of glass over best-in-class eval tooling and can model their span volume accurately. |
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 Datadog LLM Observability
Datadog LLM Observability is the obvious pick if Datadog is already your monitoring platform, and a poor one otherwise. The correlation story is real - being able to trace an LLM latency spike down through the service, host and database in one product is something no AI-native competitor matches. The eval tooling is competent but not the reason you would buy it. The thing that decides this product is billing. Datadog charges per LLM span, not per trace, and that distinction is where teams get hurt. A simple completion is 3 to 5 spans, but an agentic workflow with tool calls, retrieval and reasoning chains is commonly 20 to 50. Estimate your bill from requests and you will be wrong by more than an order of magnitude. Model span volume first, then decide.
Full Datadog LLM Observability review →Frequently Asked Questions
What is the main difference between OpenLIT and Datadog LLM Observability?
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. Datadog LLM Observability: Teams already standardised on Datadog for infrastructure and APM, running LLM features inside a larger system, who value one pane of glass over best-in-class eval tooling and can model their span volume accurately. 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 Datadog LLM Observability?
It depends entirely on your workload shape, because they meter differently - OpenLIT bills on no usage metering and Datadog LLM Observability bills on span. Published starting prices are $0 (Apache 2.0) and $160/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 Datadog LLM Observability?
OpenLIT: Yes, free. Datadog LLM Observability: No or paid tier only. Free self-hosting means no licence fee, not no cost - you still own the infrastructure, upgrades and on-call.