OpenLIT

OpenLIT is an open-source LLM observability platform on OpenTelemetry for monitoring, tracing, and evaluating AI apps. Free self-hosted, minimal code.

AI Developer Tools Monitor & Log Management LLM

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OpenLIT at a glance

OpenLIT is an open-source platform for AI engineering focused on LLM observability, tracing, and evaluation. It is built on OpenTelemetry and is positioned for production workloads that need monitoring across AI applications, prompts, models, and surrounding infrastructure.

The product is designed to work with existing SDK-based applications and observability stacks. The homepage emphasizes quick setup with a few lines of code, while the pricing page states the self-hosted edition is Apache 2.0 licensed, free forever, and available without usage limits or license keys.

Features

Distributed tracing

Monitor and trace LLM applications in real time with OpenTelemetry-powered distributed tracing. The site says this helps visualize request flows, identify bottlenecks, and follow the lifecycle of each AI interaction.

AI model evaluation

Run online and offline evaluations either from the UI or through SDKs. The product positioning ties evaluation to prompts, models, and end-to-end application testing.

Prompt management

Centrally manage prompts with versioning and deployment controls. OpenLIT presents this as a way to experiment with prompt variations and track prompt performance over time.

Real-time monitoring

Inspect telemetry across deployments with real-time dashboards and custom SQL queries. The homepage also mentions configurable widgets and layouts for visualizing OpenTelemetry data.

Multi-deployment management

Manage multiple deployments from one view. OpenLIT describes Fleet Hub as a way to compare performance metrics across environments and coordinate an AI fleet from a single dashboard.

Infrastructure-aware observability

Track infrastructure signals alongside LLM activity, including GPU monitoring, vector database tracing, token usage, and cost tracking. The pricing page also mentions exports to Grafana, Datadog, and any OTLP backend.

Use cases

Add observability to live LLM apps
Instrument an existing LLM application directly in the SDK and capture traces, token usage, and cost data without routing traffic through a proxy. This fits teams that want observability with minimal code changes and no added proxy latency.
Test prompts and models
Run prompt and model experiments in the UI or through SDKs, then use evaluations to compare outputs and iterate on application behavior. OpenLIT also includes prompt versioning so teams can track changes over time.
Observe model and infrastructure workloads
Monitor self-hosted or distributed AI infrastructure where GPU utilization, VRAM, temperature, and power draw matter. The comparison pages specifically call out NVIDIA and AMD GPU monitoring and vector DB tracing.
Coordinate multiple deployments
Manage several AI deployments from a single dashboard and compare performance across environments. Fleet Hub is presented as a multi-deployment view for teams operating more than one application or environment.
Fit into an existing observability stack
Export telemetry to existing observability tools such as Grafana, Datadog, or any OTLP-compatible backend. This suits teams that already have a standard monitoring stack and want to keep data flowing there.

Pros and Cons

Pros

OpenTelemetry-native design makes it compatible with existing OTLP workflows and exporters.
Free self-hosted edition with Apache 2.0 licensing, no usage limits, and no license key requirement.
Broad coverage across tracing, evaluations, prompt management, dashboards, and multi-deployment management.
Supports both application observability and infrastructure signals such as GPUs, vector databases, token usage, and cost tracking.
Works with a wide set of providers, frameworks, vector databases, GPUs, and OTLP-compatible backends.

Cons

The managed Cloud tier is not yet available; the site says it is coming soon.
Some advanced platform capabilities appear to be concentrated in the self-hosted product, so teams need to self-host to use the full stack now.

FAQ

Is OpenLIT really free?

Yes. OpenLIT is Apache 2.0 licensed and free to self-host, with no usage limits, no feature gates, and no license key required.

What do I need to self-host OpenLIT?

The pricing page says Docker and Docker Compose are enough to self-host. It also says you can run `docker compose up -d` in the repo to start the full stack, including the UI, ClickHouse storage, and OpenTelemetry Collector.

Can OpenLIT send data to an existing observability backend?

Yes. OpenLIT is OpenTelemetry-native and can export to OTLP-compatible backends such as Grafana, Datadog, New Relic, SigNoz, and Jaeger.

Does OpenLIT support GPU monitoring?

Yes. The pricing page says GPU metrics can be enabled with `openlit.init(collect_gpu_stats=True)`, and that OpenLIT collects utilization, VRAM usage, temperature, and power draw from NVIDIA and AMD GPUs.

Is there a cloud version available?

A managed cloud service is listed as coming soon. The page says the Cloud tier is in development and invites users to join the waitlist by emailing [email protected].

Quick Facts

Category: AI observability platform
License: Apache 2.0
Deployment: Self-hosted now; cloud managed option coming soon
Primary users: Developers and engineers building LLM applications
Source domain: openlit.io
Notable workflow: One-line or few-line SDK instrumentation plus OpenTelemetry export