OpenLIT review

OpenLIT is an OpenTelemetry-native observability platform that you can self-host, rather than only a hosted API or an instrumentation library. The platform receives telemetry from its SDKs and other OTel sources, then provides a UI for tracing, evaluation, prompt management, agent monitoring and cost analysis. It sits beside your application and model stack: instrument your code or send OTLP data to OpenLIT, then inspect traces and metrics in its interface.

The vendor describes the platform as free to self-host under Apache 2.0. Deployment options named on its homepage include Docker Compose and Kubernetes with Helm. OpenLIT also lists a GPU collector and an Enterprise Edition eBPF controller for zero-code instrumentation. The product targets teams that need visibility across LLM calls, tool use, retrieval and agent steps, with evaluations and prompt changes tied to that workflow. Its OpenTelemetry foundation also makes it possible to send telemetry to other OTel backends rather than keeping every trace inside OpenLIT.

The homepage names native SDKs for Python, JavaScript and Go. Its Python quick start is pip install openlit followed by openlit.init(). For existing instrumentation, point OTel SDKs, OBI, OpenLLMetry or another OTLP exporter at OpenLIT; the homepage shows an OTLP endpoint using port 4318. It also lists an Enterprise Edition eBPF controller installed with Helm, and a GPU collector run in Docker.

OpenLIT says it supports 56+ integrations across LLMs, agent frameworks and vector databases. Named examples include OpenAI, Anthropic, Ollama, LangChain, LlamaIndex, ChromaDB, Pinecone and Qdrant. The vendor links to documentation and integration listings. The homepage does not describe a separate CLI or REST API as the core integration path, so confirm the exact setup and SDK coverage in the docs for your framework. A Reddit maintainer says the SDK adds auto-instrumentation for supported GenAI tools and emits traces and metrics for OpenLIT or other OTel tools.

The pricing page lists the OSS plan at $0 per month for teams self-hosting OpenLIT. It includes unlimited self-hosted usage and no per-trace fee, under Apache 2.0. That makes the software price predictable as trace volume grows, but it does not remove the cost of running the platform: budget for your own compute, storage and operations.

The Enterprise plan is licensed per organisation and listed as “Contact us”; its price and billing terms are not published. The Cloud plan is marked “Coming soon,” with pricing to be shared at launch. No add-ons are listed, and the vendor does not publish usage-based overages, seat charges or rate limits. For the free self-hosted tier, there is no published trace cap to budget against, but infrastructure capacity remains your responsibility. Teams that need managed hosting or contractual enterprise terms should ask the vendor for availability, pricing, billing units and any usage limits before committing.

The core platform is Apache 2.0 licensed, and OpenLIT says teams can fork, modify and contribute to it. Self-hosting is supported, with Docker Compose and Kubernetes using Helm named as deployment options. The vendor also describes unlimited self-hosted usage under the OSS plan.

Telemetry portability is a clear architectural advantage: OpenLIT uses OpenTelemetry-native traces and metrics, and the vendor says you can export to Grafana, Datadog and any OTLP backend. You can also bring existing OTel instrumentation. That reduces lock-in for telemetry data compared with a proprietary trace format. Moving the surrounding workflow may still require work: prompt versions, evaluations, dashboards and secrets are platform features, so plan how to migrate those separately. Confirm export behavior and formats for each data type you need to retain before making OpenLIT a long-term system of record.

OpenLIT groups tracing, evaluations, prompt management, API key storage and GPU monitoring in one platform, but a self-hosted deployment means your team owns availability, upgrades and capacity planning. The vendor lists GPU utilization, memory, temperature and power monitoring for NVIDIA, AMD and Intel hardware. It also describes a vault for storing and rotating provider keys and other secrets, with AES-256 shown on the homepage. Check deployment-specific security controls, retention, access policies and compliance requirements with the vendor before storing production data or credentials.

The vendor does not publish rate limits or production scaling figures. One Reddit user reported being unable to send data from a test app, while a maintainer asked how it was integrated. In an older Reddit discussion, a maintainer said logs were not supported at that time and pointed to trace attributes and exceptions for debugging; confirm current logging behavior in the docs. Test your own exporters, failure handling and storage growth before relying on the platform for incident response.