OpenLIT
by OpenLIT · Open source OpenTelemetry platform for LLM and AI agent engineering
BenchRank score
Screenshots of OpenLIT
Homepage
Overview
OpenLIT is an open source platform for instrumenting and monitoring LLM and AI agent applications. It collects OpenTelemetry traces from its own SDKs, an eBPF controller, a GPU collector or any OTLP source, and stores them in ClickHouse. Alongside tracing it offers LLM evaluations, a Prompt Hub for versioning, a Vault for API keys and the OpenGround model comparison playground.
- Best for
- Engineering teams self-hosting OpenTelemetry tracing and evaluation for LLM and agent apps.
- Pricing
- Self-hosting is free under Apache 2.0 with unlimited usage, users and projects; a hosted Cloud plan is marked coming soon with pricing to be published at launch.
- Runs on
- Self-hostedCLI
Strengths and trade-offs
Strengths
- Apache 2.0 core, self-hosted free with unlimited usage
- OpenTelemetry-native; export to Grafana, Datadog or any OTLP backend
- Tracing, evals, Prompt Hub, Vault and GPU monitoring in one UI
- 56+ integrations plus ingest from OTel SDKs, OBI or OpenLLMetry
Trade-offs
- No hosted option yet; Cloud is listed as coming soon with no pricing
- You run and maintain the stack: OpenLIT, ClickHouse and an OTel collector
- Support is community-only via GitHub
How OpenLIT markets itself
A structured read of the promise, proof and page design on OpenLIT’s captured homepage.
Homepage capture
“Open source Agent Harness Engineering Platform”
- Angle: Open source / ownership
- Hero: Interactive demo
Pricing
Published plans and prices from OpenLIT’s own pricing page.
How this score is made up
Each dimension is scored out of 100 and combined into the headline score using fixed weights.
MCP support
Whether an agent can drive the product through the Model Context Protocol, and how much setup that takes.
API quality
Public API surface: machine-readable spec, official SDKs, documented auth, errors, rate limits and versioning.
Documentation
Publicly reachable docs — coverage, freshness, code samples and machine readability.
Agent friendliness
How readable the site is to an automated client: llms.txt, structured data, server-rendered content, crawler access.
Pricing transparency
Whether real prices are published, self-serve signup exists, and usage costs are knowable without a sales call.
Changelog
A public, dated record of what shipped and when — the clearest signal that a product is still alive.
Marketing site structure
Whether the site answers a buyer's questions: clear positioning, the pages that matter, and accessibility.
Page speed
How fast the site loads for real visitors: Chrome UX Report 75th-percentile LCP, INP and CLS, with a Lighthouse mobile run standing in where a site has too little traffic for field data.
Operational trust
Status page and incident history, security disclosure, compliance and data-processing documentation.
Measured, but not part of the score
Useful to know, but not a mark for or against the product — so these do not affect the ranking.
Openness
Source availability, self-hosting, data export and open standards. Scored and shown, but not part of the composite — paid SaaS is not worse for being paid SaaS.
Maintenance
Release cadence and repository activity. Scored and shown, but not part of the composite — it is only measurable for open repositories.
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Where this comes from
The OpenLIT pages BenchRank reads when it scores the product — its documentation, release notes, status and security pages, and its repository where there is one.
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