OpenLIT Review (2026)

The thing nobody else does: GPUs

Almost every tool in this category shares a hidden assumption - that you call an LLM over an API. Under that assumption the interesting telemetry starts at the request and ends at the response, and the hardware is somebody else’s problem.

If you self-host inference, that assumption is wrong, and it makes most of the category blind to a whole class of your failures.

OpenLIT monitors GPUs. Its OpenTelemetry GPU Collector exports utilisation, memory, temperature and power as standard OTel signals, so GPU health sits in the same dashboard as inference performance.

Two diagnostics from their own guidance show why this matters:

• A slowly increasing gpu.memory.used suggests a memory leak in your inference server, or a KV cache that is not being evicted properly - a known pattern with long-running vLLM or TGI instances.
• gpu.temperature hitting the throttle threshold while gpu.utilization drops at the same moment means the GPU is reducing clock speeds to protect itself.

Neither is diagnosable from LLM traces. Both present to a user as “the model got slow this week.” If you run your own inference, this capability is worth more than most of the feature comparisons in this category, and Langfuse, LangSmith and Braintrust do not offer it at all.

Unusually broad for something free

OpenLIT is Apache 2.0 across the main repository, the TypeScript client and the GPU collector, and the scope is wider than the licence would lead you to expect:

• OpenTelemetry-native LLM observability
• GPU monitoring
• Guardrails
• Evaluations
• Prompt management
• Vault for API keys
• Playground

Plus integration with 50+ LLM providers, vector databases, agent frameworks and GPU hardware, and zero-code instrumentation that needs no SDK or application changes.

One quietly excellent detail: custom model cost tracking via a JSON file. Fine-tuned models are priced differently from base models, and most observability tools either price them wrong or shrug. Being able to declare your own cost table is a small feature that produces correct numbers where competitors produce confident wrong ones.

Another: coding-agent observability, using the standard gen_ai.* semantic conventions plus a coding_agent.* extension namespace. As coding agents move into production workflows this is early but well-positioned, and because it is OTLP anything can consume it.

It works as instrumentation for someone else’s backend

This is the deployment most teams should consider first.

You do not have to run OpenLIT’s platform. Traces and metrics forward to Grafana, New Relic, Datadog, Honeycomb and Grafana Tempo, and GPU metrics can route through an OTel Collector to Prometheus. On Datadog they land as custom metrics you can alert on.

So OpenLIT can be purely the instrumentation layer feeding a backend you already operate - the same position OpenLLMetry occupies. That means very little lock-in, and it is the cheapest way to get GPU correlation into an existing stack.

If you do self-host the full platform, GPU metrics land in ClickHouse alongside traces, which brings the same operational burden as SigNoz: real servers, real upgrades, real on-call.

Two things we could not resolve

We would rather flag open questions than paper over them.

The enterprise eBPF controller. OpenLIT lists an enterprise eBPF controller among its ingestion paths, which implies a commercial tier alongside the Apache-2.0 core. We could not find clear public licensing terms for that component. The SDK and collector path is Apache 2.0; if your plan depends on eBPF ingestion, verify the terms before building on it.

The “Harness Engineering platform” positioning. OpenLIT has recently described itself this way. Whether that signals a relationship with Harness Inc, a rebrand, or simply a category description is not explained in any public source we found. Given that this category saw Traceloop absorbed by ServiceNow, WhyLabs shut down and Literal AI discontinued inside eighteen months, ask the maintainers directly before standardising on it. For what it is worth, we found no wind-down signals and the repository was actively updated on 29 May 2026.

Where it is thin

Community size. Roughly 2,487 GitHub stars against Langfuse’s 21,000+. Fewer worked examples, fewer Stack Overflow answers, fewer third-party integrations. That is the real cost of the less popular option.

Breadth over depth. The evals are lighter than DeepEval or promptfoo. The guardrails are lighter than Guardrails AI or NeMo. Having everything in one Apache-2.0 package is convenient, but if evaluation or safety is central to your work, a specialist tool will do it better.

Should you use it?

Use OpenLIT if you self-host inference on your own GPUs - the GPU correlation is close to unique and directly valuable. Also a strong pick if you want broad AI engineering tooling in one permissively licensed package, or want a vendor-neutral instrumentation layer feeding an existing backend.

Don’t use it if you only call hosted APIs and need best-in-class evaluation, or you need the community depth of a larger project.

Bottom line: the GPU monitoring is a genuine differentiator that the rest of this category has simply not addressed, and it is free under Apache 2.0. If you run vLLM or TGI on your own hardware, look at this before anything else. Get clarity on the eBPF licensing and the Harness question before you commit organisationally.

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License, repository activity and feature scope verified against GitHub and vendor documentation on 31 July 2026. The enterprise eBPF controller’s licensing and the “Harness Engineering platform” positioning could not be confirmed and are flagged as open questions. This is a researched directory entry - we have not yet instrumented this platform with our reference application.