OpenLIT
OpenLIT is an open-source, OpenTelemetry-native agent harness engineering platform for LLM tracing, evals, and guardrails you self-host.
Self-hosting OpenLIT costs $0/mo under Apache 2.0 with unlimited traces, users, projects, and history — the strongest price story in LLM observability if you have Kubernetes hands. The catch is exactly that: you own ClickHouse, the collector, and upgrades, and support comes from GitHub rather than an SLA. Take it when OTLP portability and data residency are requirements, not nice-to-haves.
Verified 4d ago · liveness 72/100 · cite: rightaichoice.com/tools/openlit
- AI engineers who want tracing, evals, prompts, Vault, and guardrails on one dataset
- Platform teams with Kubernetes skills that need self-hosted, data-owned agent observability
- Cost-sensitive teams that want unlimited LLM tracing with no per-trace fee
- Organizations standardizing on OpenTelemetry for portable, exportable telemetry
- Teams that need a fully managed, zero-ops service today while OpenLIT Cloud is still coming soon
- Groups without anyone comfortable running OpenLIT, ClickHouse, Helm, and an OpenTelemetry Collector
- Buyers who require commercial SLAs or enterprise support contracts right now
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Skip OpenLIT if you have no one to run ClickHouse, Helm, and an OpenTelemetry Collector — the Apache 2.0 self-hosted core is the only generally available deployment today, and OpenLIT Cloud is still coming soon with pricing shared at launch.
The $0/mo Apache 2.0 license buys software, not infrastructure — you still pay for the compute and storage behind OpenLIT, ClickHouse, and the Collector.
OpenLIT OSS is $0/mo under Apache 2.0 with unlimited self-hosted usage, historical data, users, and projects — cheaper than Langfuse or Helicone managed plans that bill monthly by event volume or seats. The catch is that you fund the infrastructure yourself: ClickHouse, the Collector, and the OpenLIT deployment. OpenLIT Cloud, the hosted counterpart, is coming soon with pricing shared at launch. For a platform team already running Kubernetes, that leaves hosted alternatives as the more
In short
OpenLIT — OpenLIT is an open-source, OpenTelemetry-native agent harness engineering platform for LLM tracing, evals, and guardrails you self-host. Best for AI engineers who want tracing, evals, prompts, Vault, and guardrails on one dataset, Platform teams with Kubernetes skills that need self-hosted, data-owned agent observability, Cost-sensitive teams that want unlimited LLM tracing with no per-trace fee. Free to use.
What's new in OpenLIT
Checked todayAcross the latest 6 updates: 3 feature updates and 3 community discussions.
TypeSafe Jev in OpenLIT: Day 1 Evals and Observability
OpenLIT adds day-1 TypeSafe Jev support: System One as eval judge and OpenTelemetry tracing of system_one calls from a single openlit.init().
Best open-source tools for LLM evaluation and prompt management (2026)
Roundup of LLM evaluation and prompt management tooling: OpenLIT Prompt Hub and evaluations, Langfuse, Arize Phoenix, Comet Opik, Braintrust and Promptfoo.
Best Langfuse alternatives (open source, 2026)
OpenLIT publishes an open-source Langfuse alternatives roundup covering license, self-host and OpenTelemetry stance for OpenLIT, Phoenix, Opik, Helicone and Traceloop.
AI agent observability with OpenLIT and Parseable
OpenLIT and Parseable integration traces model and tool calls, runs output evaluations and retains telemetry for incident investigation.
Observability for AI Applications Using Oodle and OpenLIT
One-line OpenLIT instrumentation ships LLM app and agent metrics and traces to Oodle.
GPU Utilization Is a Vanity Metric. Here is the GPU SLI for Inference
OpenLIT argues nvidia-smi, DCGM and NVML GPU utilization mislead for LLM inference; proposes memory headroom, throttle-free time and CUDA health SLIs as OTel metrics.
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.
Average across the 4 sources that answered — each source counts once, not each post.
- +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: October 2026
How we score →Key Features
- OpenTelemetry-native LLM tracing for every call, tool call, agent step, and retrieval
- Filter traces by user, session, cost, latency, or custom metadata
- LLM evaluation with LLM-as-a-judge, heuristics, and human review on production traffic
- Prompt Hub with versioning, dynamic variables, API retrieval, rollback, and AI-assisted improvement
- OpenGround side-by-side model comparison on latency, tokens, and cost
- Vault encrypted LLM API key storage with AES-256-GCM and key/tag-based retrieval
- Guardrails SDK for prompt-injection, jailbreak, and sensitive-topic detection
- AI agent and coding agent monitoring with session, cost, token, and repo activity tracking
- GPU monitoring for NVIDIA, AMD, and Intel — utilization, memory, temperature, power
- Zero-code eBPF controller for any language (enterprise)
- Fleet Hub collector monitoring and config via OpAMP
- OTLP ingest from OTel SDKs, OBI, OpenLLMetry, and other OpenTelemetry instrumentations
- OTLP export to Grafana, Datadog, and any OTLP backend
- SDKs for Python, JavaScript, and Go via openlit.init()
- ClickHouse-backed dashboards with charts, tables, filters, folders, import/export
About OpenLIT
OpenLIT is an open-source agent harness engineering platform. The pitch is the full loop around your model: OpenTelemetry-native LLM tracing for every call, tool call, and retrieval, then evaluation, guardrails, prompt management, and guardrail fixes on top of the same production data. It's aimed at AI engineers and platform teams who ship agents and coding agents and want production visibility without a per-trace meter running. Instrumentation is deliberately flexible. Drop `openlit.init()` in from the Python, JavaScript, or Go SDK, point an existing OTel SDK, OBI, or OpenLLMetry exporter at OpenLIT's OTLP endpoint, run the GPU collector, or use the enterprise eBPF controller for zero-code coverage in any language. Nothing forces a proxy onto every LLM call. The harness pieces each solve a specific problem: Prompt Hub versions and rolls back prompts without shipping app code, the evaluation hub runs LLM-as-a-judge, heuristics, or human review, OpenGround compares models side by side on latency, tokens, and cost, and Vault keeps OpenAI, Anthropic, and other API keys encrypted with AES-256-GCM outside your application. Guardrails catch prompt injection, jailbreaks, and sensitive topics in the SDK; a GPU collector and Fleet Hub (OpAMP) extend monitoring to NVIDIA, AMD, and Intel hardware and your OTel collectors. Storage is ClickHouse-backed, so queries stay fast on high-volume telemetry, and 56+ documented integrations span LLMs, agent frameworks, and vector databases. The Apache 2.0 core is free to self-host with unlimited usage, users, projects, and historical data. Pick it over Langfuse, Helicone, or LangSmith when OTLP portability and data ownership matter more than managed convenience — you run OpenLIT, ClickHouse, and a collector on your own Kubernetes or Docker infra.
Behind the Verdict
We'd reach for OpenLIT the moment someone asks where the LLM traces live and the answer needs to be "our cluster." Apache 2.0, OTLP in and out, ClickHouse underneath — that combination lets a platform team standardize on one telemetry pipeline for LLM calls, agent steps, GPU metrics, and coding-agent sessions without asking finance for a per-trace budget line. The harness angle is what separates it from plain tracing tools. Prompt Hub, Vault, OpenGround, evaluations, and guardrails all read from the same production data, so a bad prompt can be traced to a cost spike, rolled back, and re-evaluated without redeploying app code. Teams that already stitch Langfuse for traces, a separate eval framework, and a secrets manager get one stack instead of three. Where it bites is operations. You run OpenLIT, ClickHouse, and an OpenTelemetry Collector. Helm or Docker Compose smooths the install, but upgrades, storage sizing, and ClickHouse tuning are yours. If nobody on the team enjoys that work, the free license stops feeling free pretty quickly. Compare it to Langfuse and Helicone: those are hosted-first products with less setup friction and commercial support. OpenLIT trades that polish for portability and price. Export your traces to Grafana or Datadog and they keep working — no proprietary format to unwind later. OpenLIT Cloud is the missing piece. It's listed as coming soon, with feature set and pricing shared at launch, so teams that want zero ops today should either join the waitlist or budget for a hosted competitor. There's no published managed tier to buy right now. The free tier is genuinely whole, not a teaser: agent observability, evaluations, guardrails, Prompt Hub, Vault, OpenGround, coding-agent monitoring, and GPU metrics all ship in OSS. You create your own
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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.
Adds openlit.init() to a Python LLM service, watches traces appear in the OpenLIT dashboard, then wires up an LLM-as-a-judge evaluator against live production traffic and moves prompts into Prompt Hub so they can roll back without a deploy.
Outcome: End-to-end tracing, evaluation, and prompt versioning on one dataset, with no per-trace bill and no vendor lock-in on the telemetry format.
Deploys OpenLIT via Helm alongside ClickHouse and an OpenTelemetry Collector, points existing OTel SDKs and OpenLLMetry exporters at the OTLP endpoint, and uses Fleet Hub with OpAMP to monitor collectors across the cluster.
Outcome: One self-hosted observability plane for LLM traffic, with GPU collector data next to model traces and export to Grafana or Datadog for teams that already live there.
Runs the OpenLIT GPU collector on inference nodes to capture NVIDIA utilization, memory, temperature, and power, and reviews coding agent sessions and cost in the same dashboard as LLM spend.
Outcome: Cost and capacity decisions backed by real utilization and spend data instead of provider estimates.
Use Cases
- Trace every LLM call, tool call, and agent step from prototype to production with OpenTelemetry
- Run LLM-as-a-judge, heuristic, or human evaluations on live production traffic
- Version, retrieve, and roll back prompts through Prompt Hub without redeploying app code
- Compare models side by side in OpenGround on latency, tokens, and cost before switching providers
- Keep OpenAI and Anthropic API keys encrypted in Vault instead of in application code
- Block prompt injection, jailbreaks, and sensitive topics with the guardrails SDK
- Watch GPU utilization, memory, temperature, and power during LLM inference
- Track coding agent sessions, cost, and repo activity alongside LLM telemetry
Limitations
- OpenLIT is self-hosted under Apache 2.0 and deploys via Helm or Docker on your own infrastructure, so you run the platform, ClickHouse, and an OpenTelemetry Collector yourself.
- The fully hosted OpenLIT Cloud is listed as coming soon with only a waitlist, meaning no managed option is available currently.
- Support is the GitHub community, and sign-in is OAuth-based rather than a contracted SLA.
as of 2026-10-03
Verification history
We have re-verified OpenLIT 9 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-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
Showing the 6 most recent of 9 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-host (Apache 2.0)
$0/mo
Ideal for
Platform and AI engineering teams with Kubernetes or Docker skills that want LLM tracing, evals, prompts, guardrails, and GPU monitoring with full data ownership.
What this tier adds
Starting tier and the only generally available one: $0/mo, Apache 2.0, unlimited self-hosted usage, users, projects, and historical data, with GitHub community support.
Cloud (fully hosted)
Coming soon
Ideal for
Teams that want managed OpenLIT and zero ops, currently able to join only a waitlist while the service is in development.
What this tier adds
A managed alternative to running your own stack — managed infrastructure and upgrades — with feature set and pricing shared at launch.
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 OSS is $0/mo under Apache 2.0 with unlimited self-hosted usage, historical data, users, and projects — cheaper than Langfuse or Helicone managed plans that bill monthly by event volume or seats. The catch is that you fund the infrastructure yourself: ClickHouse, the Collector, and the OpenLIT deployment. OpenLIT Cloud, the hosted counterpart, is coming soon with pricing shared at launch. For a platform team already running Kubernetes, that leaves hosted alternatives as the more
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.
AI engineer with an instrumented app: minutes — pip install openlit and openlit.init() starts emitting traces. Platform team self-hosting the full stack: roughly half a day to stand up OpenLIT plus ClickHouse and an OpenTelemetry Collector with Docker Compose, longer with Helm if you need to provision storage and ingress. Teams wiring existing OTel SDKs, OBI, or OpenLLMetry just point
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: keep your existing instrumentation and point it at OpenLIT's OTLP endpoint, or swap in the OpenLIT SDK, then rebuild dashboards in ClickHouse-backed views.
- →From Helicone: move from proxy-based capture to SDK or eBPF instrumentation so traces carry tool and retrieval spans, then export to your existing OTLP backend if needed.
- →From LangSmith: replace the proprietary trace format with OpenTelemetry spans via the OpenLIT SDK, and move prompt versions into Prompt Hub.
- →From raw OpenTelemetry: leave existing OTel SDKs, OBI, or OpenLLMetry exporters in place and send OTLP to OpenLIT instead of building your own LLM-aware backend.
- →From a homegrown logging stack: use the eBPF controller for zero-code instrumentation and route existing collector output into OpenLIT's OTLP endpoint.
- ↗To Grafana or Datadog: export traces and metrics over OTLP from OpenLIT into your existing observability backend, since the data is OpenTelemetry-native.
- ↗To Langfuse: re-instrument with the Langfuse SDK or send OpenTelemetry traces to its OTLP endpoint; OpenLIT's Prompts and evaluations must be recreated.
- ↗To a managed service: export raw OTLP data before decommissioning the self-hosted ClickHouse store, as historical traces only live in your cluster.
- ↗To another OTLP backend: repoint OTEL_EXPORTER_OTLP_ENDPOINT at the new destination without changing application code.
Integrations
Resources & Guides
Tutorials & Learning
Monitor AI agents end to end | OpenLIT
OpenLIT
What is OpenTelemetry? LLM Observability with OpenLIT Explained
OpenLIT
YouTube returned 6 videos for “OpenLIT”, and we withheld 4: 4 could not be judged, because “OpenLIT” is a single word that other videos use for other things. Showing the 2 we can prove are about OpenLIT.
Official links
Tools that pair well with OpenLIT
Common stack mates teams adopt alongside OpenLIT, with the specific reason each pairing earns its keep.
Arize Phoenix
Arize Phoenix is open-source LLM observability: trace agent steps, run evals, and self-host your traces.
Phoenix
Open-source LLM observability and agent evaluation you can trace, evaluate, and self-host for free.
TruLens
TruLens traces AI agent runs with OpenTelemetry-native evals, showing where an agent fails and where you can cut cost.
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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Arize Phoenix
Arize Phoenix is open-source LLM observability: trace agent steps, run evals, and self-host your traces.
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