OpenLIT
Open-source, OpenTelemetry-native LLM observability and AI engineering platform for teams.
OpenLIT is the strongest free, self-hosted LLM observability platform we've tested, provided you're comfortable with Docker or Kubernetes. Its unlimited usage and OpenTelemetry-native design are unmatched at this price. The platform covers the full harness loop—tracing, evaluation, prompt management, Vault, and OpenGround—all on production data. If you need zero-ops, wait for the managed cloud, or choose a paid SaaS like Langfuse for now.
Verified 1d ago · liveness 72/100 · cite: rightaichoice.com/tools/openlit
- AI engineers building LLM apps who want end-to-end tracing and evaluation
- DevOps teams needing to monitor GPUs and coding agents in production
- Startups and cost-conscious teams wanting unlimited observability for free
- Teams standardizing on OpenTelemetry for portable telemetry
- Teams needing a fully managed cloud service right now
- Non-technical users who prefer zero-config SaaS
- Companies requiring commercial support or SLAs
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Skip OpenLIT if you need a fully managed cloud LLM observability service today, or if you are uncomfortable with self-hosting and managing your own infrastructure (Docker/Helm).
Self-hosting means you must pay for your own compute and storage to run the OpenLIT stack (ClickHouse, collector) — there's no free managed tier.
OpenLIT is free to self-host with unlimited usage, making it the most cost-effective option for teams that can manage their own infrastructure. Compared to paid SaaS like Langfuse (which charges per event) or Datadog LLM Observability (per-trace fees), OpenLIT eliminates per-trace costs entirely. It's ideal for startups and high-volume workloads, but if you need zero-ops, you'll pay more for a managed service.
In short
OpenLIT — Open-source, OpenTelemetry-native LLM observability and AI engineering platform for teams. Best for AI engineers building LLM apps who want end-to-end tracing and evaluation, DevOps teams needing to monitor GPUs and coding agents in production, Startups and cost-conscious teams wanting unlimited observability for free. Free to use.
What people actually say about OpenLIT — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
21 mentions across 4 sources (Reddit, Hacker News, Product Hunt, Lemmy) · researched Jul 3, 2026.
- +OpenTelemetry-native: vendor-neutral, works with Grafana, Datadog, etc.
- +Generous free tier: self-hosted, no usage limits, no license key needed.
- +GPU monitoring: supports NVIDIA and AMD, key for AI ops teams.
- +Easy setup: single Docker Compose command, setup under a minute.
- +Covers full stack: LLM traces, GPU, vector DB, cost tracking, evals.
- −Very limited independent user reviews; mostly founder/launch buzz.
- −Support channel is Slack-only; no documented response SLA.
- −Evaluation feature details are sparse and hard to find.
- −Hacker News posts flagged dead, hurting credibility.
- −No cloud tier available yet; self-hosted only (but coming).
- • Self-hosting infrastructure costs (servers, storage, network)
- • Time investment for setup, maintenance, and troubleshooting
Viability Score
How well maintained and how widely used is OpenLIT? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- OpenTelemetry-native LLM tracing for every call, tool, and retrieval
- LLM evaluation with LLM-as-a-judge, heuristics, and human review
- Prompt Hub for versioning, rollback, and dynamic variables
- OpenGround side-by-side model comparison by latency, tokens, cost
- Vault encrypted API key storage with AES-256-GCM
- AI agent monitoring with session, cost, and tool tracking
- GPU monitoring for NVIDIA, AMD, Intel via OpenTelemetry
- Zero-code eBPF controller for any language
- OTLP export to Grafana, Datadog, and any OTLP backend
- ClickHouse-backed dashboards with SQL
- Fleet Hub for OpenTelemetry collector monitoring via OpAMP
- Guardrails for prompt injection and jailbreak detection
- SDKs for Python, JavaScript, Go
- In-process instrumentation, proxy-free design
- Self-hosted via Docker Compose or Kubernetes Helm
About OpenLIT
OpenLIT is an open-source AI engineering platform that bundles LLM observability, prompt management, evaluation, and agent monitoring into one self-hosted tool. Built on OpenTelemetry, it traces every call across LLMs, AI agents, GPUs, and vector databases without vendor lock-in, and it's free under an Apache 2.0 license. You can start collecting telemetry in minutes with SDKs for Python, JavaScript, and Go, or use the zero-code eBPF controller for any language. The platform includes distributed tracing, a Prompt Hub for versioning and rolling back prompts, an evaluation hub with LLM-as-a-judge and human review, OpenGround for side-by-side model comparison, Vault for encrypted API key storage, and guardrails for prompt-injection and jailbreak detection. It also monitors GPUs from NVIDIA, AMD, and Intel, and tracks coding agents with session and cost data. With 56+ integrations across LLM providers, frameworks, and vector DBs, and export to Grafana, Datadog, or any OTLP backend, OpenLIT fits into existing stacks. The self-hosted stack runs via Docker Compose or Helm, with unlimited usage and no per-seat fees. It's ideal for teams that want data ownership and cost control, but it does require some ops skill to deploy and maintain.
Behind the Verdict
OpenLIT stands out in the crowded LLM observability space by being fully open source (Apache 2.0) and OpenTelemetry-native. This means you get portable telemetry that you can export to Grafana, Datadog, or any OTLP backend, avoiding lock-in. The breadth of features is impressive: distributed tracing, LLM evaluation, Prompt Hub, Vault for secret management, OpenGround for model comparison, GPU monitoring, and even coding agent observability. For teams already using OpenTelemetry, OpenLIT integrates seamlessly; you can point your existing OTel SDKs at it and get immediate value. The zero-code eBPF controller is a differentiator for polyglot environments where you don't want to instrument every service. However, the self-hosted nature means you take on operational burden. You need to manage Docker Compose or Helm charts, and you're responsible for scaling ClickHouse and the collector. There's no managed cloud yet—the pricing page says 'coming soon'—so if you need a fully managed service today, you'll have to look elsewhere. Also, advanced features like guardrails and Fleet Hub require configuration; they don't work out of the box without setup. Where OpenLIT really shines is for startups and cost-conscious teams that want unlimited observability without per-trace fees. The unlimited self-hosted usage is a game-changer for high-volume workloads. It's also great for privacy-sensitive organizations that need to keep data in-house. On the other hand, if you're a non-technical user who wants a zero-config SaaS, OpenLIT is not for you yet. In summary, OpenLIT is a powerful, open-source alternative to commercial platforms like Langfuse, Helicone, or Datadog LLM Observability. It gives you deep control and ownership, but demands some ops maturity. If you're an AI engineer or DevOps team comfortable with self-hosting, it's well worth trying.
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Real-world workflow fit
Concrete scenarios for the personas OpenLIT actually fits — and what changes day-one when you adopt it.
You're building a RAG-based support chatbot and need to trace every LLM call, evaluate answer quality, and manage prompts across environments.
Outcome: With OpenLIT, you can deploy the Docker Compose stack, instrument your Python app with openlit.init(), and immediately see traces for every call. You can create an evaluation pipeline using LLM-as-a-judge to score responses on production traffic, and use Prompt Hub to version your system prompt and roll back if
You need to monitor GPU utilization and track coding agent activities across your Kubernetes cluster.
Outcome: Using the eBPF controller, you can instrument any language without code changes. OpenLIT provides GPU monitoring for NVIDIA, AMD, and Intel, and Fleet Hub lets you monitor the health of your OpenTelemetry collectors via OpAMP. You can set up a unified dashboard in ClickHouse-backed dashboards, and export metrics to
You must keep all LLM telemetry and API keys in-house due to strict data privacy policies.
Outcome: OpenLIT is fully self-hosted under Apache 2.0, so you retain complete ownership of your data. You can store LLM API keys in Vault with AES-256-GCM encryption, and because telemetry is OpenTelemetry-native, you can export it to your own Grafana or Datadog instance. This gives you full control without sending data to
Use Cases
- Monitor LLM application performance with end-to-end tracing.
- Run offline evaluations to compare prompts and models.
- Manage prompt versions centrally and deploy across environments.
- Track token usage and GPU metrics to optimize costs.
- Unified dashboard across deployments with Fleet Hub.
- Instrument Kubernetes workloads without code changes.
- Export telemetry to existing Grafana or Datadog dashboards.
Limitations
- Self-hosted platform requires Docker, Helm, or Linux.
- Cloud managed option is not yet available; it is listed as 'coming soon'.
- Setup involves running Docker, Helm, or deploying the eBPF controller.
- Advanced features like guardrails, Fleet Hub, and prompt management require configuration.
as of 2026-09-01
Verification history
We have re-verified OpenLIT 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published OpenLIT tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
OSS Self-Hosted
$0/mo
Ideal for
Teams that want unlimited, self-hosted LLM observability and are comfortable managing Docker or Kubernetes.
What this tier adds
Provides full-featured OpenLIT platform for free with unlimited usage, no per-seat fees, and includes all core features like tracing, evaluations, prompt management, Vault, and OpenGround.
Cloud
Coming soon
Ideal for
Teams that prefer a fully managed, zero-ops hosted solution and don't want to self-host infrastructure.
What this tier adds
Comes with managed infrastructure, automatic upgrades, and a waitlist for launch; pricing and feature set yet to be announced.
Where the pricing makes sense
The company stage and team size where OpenLIT's pricing actually pencils out — and where peers do it cheaper.
OpenLIT is free to self-host with unlimited usage, making it the most cost-effective option for teams that can manage their own infrastructure. Compared to paid SaaS like Langfuse (which charges per event) or Datadog LLM Observability (per-trace fees), OpenLIT eliminates per-trace costs entirely. It's ideal for startups and high-volume workloads, but if you need zero-ops, you'll pay more for a managed service.
Setup time & first value
How long it actually takes to get something useful out of OpenLIT — broken out by persona, not the marketing-page minute.
For a developer familiar with Docker: you can have OpenLIT running and sending traces within 15–30 minutes using the Docker Compose deployment, followed by pip install openlit and openlit.init() in your app. For Kubernetes with Helm, plan for 1–2 hours. The eBPF controller setup takes about 30 minutes to an hour, depending on your cluster configuration.
Switching to or from OpenLIT
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Langfuse: Export your traces and evaluations from Langfuse (or any OTLP endpoint) and point your OpenTelemetry SDKs to OpenLIT's OTLP endpoint. You can then recreate dashboards and prompts in OpenLIT's UI.
- →From Helicone: Use OpenLIT's OpenTelemetry-native SDKs to replace Helicone's proxy-based instrumentation, and export historical data via OTLP if needed.
- ↗To any OTLP backend: Since OpenLIT exports native OpenTelemetry, you can point your telemetry to Grafana, Datadog, or any other OTLP-compatible service with minimal changes.
- ↗To a commercial platform: Export your traces and evaluations via OTLP to a paid service like Datadog or Langfuse if you move to a managed solution.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with OpenLIT
Common stack mates teams adopt alongside OpenLIT, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Openlit vs Spider Cloud
OpenLIT and Spider Cloud serve completely different needs. Choose OpenLIT if you're building LLM applications and need free, self-hosted observability, prompt management, and model evaluation. Choose Spider Cloud if your AI system requires real-time web data for RAG or agentic workflows, and you want a fast, pay-per-use scraping API with the latest browser AI and scraper catalog features.
Openlit vs Temporal Ai
OpenLIT is the clear winner if you need deep LLM observability with token tracking, evaluation, and GPU monitoring in a self-hosted package. Temporal AI excels at durable workflow execution for AI agents that demand crash recovery and stateful orchestration. Your choice depends on whether you prioritize monitoring (OpenLIT) or reliability (Temporal).
Openlit vs Screenplayiq
These tools serve entirely different needs. If you're building LLM applications and need deep observability with OpenTelemetry, OpenLIT is the best free, self-hosted choice. If you're a screenwriter or producer wanting data-driven script analysis and box office predictions, ScreenplayIQ is the tailored solution. Choose based on your domain, not feature overlap.
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