OpenLIT
Open-source OpenTelemetry-native observability for LLM apps, self-hosted and free.
OpenLIT is a top choice for teams wanting full control over their LLM observability data with zero cost. It offers comprehensive features like distributed tracing, evaluations, prompt management, and GPU monitoring, all under an Apache 2.0 license. However, the lack of a mature managed cloud means it's less suitable for teams without DevOps resources. Self-hosting requires Docker and some operational know-how.
- AI engineers building LLM applications needing full-stack observability
- DevOps teams monitoring GenAI infrastructure with GPU and vector DB metrics
- Startups seeking a free, self-hosted observability platform with no usage limits
- Teams wanting OpenTelemetry-native tooling for seamless integration
- Teams needing a fully managed cloud solution available today
- Non-technical users who prefer zero-configuration SaaS with SLAs
- Companies requiring commercial support or guaranteed uptime without self-hosting
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Skip OpenLIT if you need a fully managed cloud observability platform with commercial support and SLAs available today, or if you prefer zero-configuration SaaS over self-hosting.
Self-hosting requires operational overhead: you must maintain Docker infrastructure, ClickHouse storage, and updates.
OpenLIT is free to self-host forever with Apache 2.0 license and no usage limits. For teams that can handle Docker, it beats per-seat pricing of Langfuse (cloud) or Helicone (usage-based). But if you lack DevOps resources, you'll incur the time cost of self-hosting or need to wait for OpenLIT Cloud.
In short
OpenLIT — Open-source OpenTelemetry-native observability for LLM apps, self-hosted and free. Best for AI engineers building LLM applications needing full-stack observability, DevOps teams monitoring GenAI infrastructure with GPU and vector DB metrics, Startups seeking a free, self-hosted observability platform with no usage limits. Free to start; paid plans from $10/mo.
In users’ own words
“OpenLIT is an open-source GenAI and LLM observability platform native to OpenTelemetry with traces and metrics in a single application 🔥 🖥 . 👉 Open source GenAI and LLM Application Performance Monitoring (APM) & Observability tool https://github.com/openlit/openlit”
“Hey Everyone! I am the maintainer of OpenLIT, An open source tool built on OpenTelemetry for Evaluating and monitoring LLMs, VectorDB and GPUs. We just launched on Product Hunt and would love to get your review and feedback on it. If you have any queries, do connect with us on slack : [https://join.slack.com/t/openlit](https://join.slack.com/t/openlit) And don't forget to checkout our github repo :…”
Real posts from independent users, linked to the source — not testimonials we collected.
Viability Score
How likely is OpenLIT to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- OpenTelemetry-native distributed tracing
- LLM token usage and cost tracking
- GPU monitoring (NVIDIA & AMD GPUs)
- Vector database monitoring (Pinecone, Chroma, Qdrant, Milvus, AstraDB)
- Prompt management with versioning
- AI model evaluation (online/offline via UI & SDKs)
- OpenGround prompt and model playground
- Real-time dashboards with custom SQL queries
- Multi-deployment management (Fleet Hub)
- Secrets Vault for API key handling
- Zero-code Kubernetes operator for automatic instrumentation
- Custom model pricing configuration
- Export telemetry to Grafana, Datadog, New Relic, SigNoz, Jaeger
- Organization management for team collaboration
- Supports 60+ integrations across LLMs, vector DBs, frameworks
About OpenLIT
OpenLIT is an open-source, self-hosted AI engineering platform that combines full-stack LLM observability with prompt management, evaluation, and experimentation tools. Built on OpenTelemetry, it provides distributed tracing, token and cost tracking, GPU monitoring, and real-time dashboards for any GenAI application. Designed for AI engineers and DevOps teams, it eliminates vendor lock-in and per-seat fees with an Apache 2.0 license. The platform supports 60+ integrations across LLM providers (OpenAI, Anthropic, Mistral, etc.), vector databases (Pinecone, Chroma, Qdrant, etc.), and frameworks (LangChain, LlamaIndex, CrewAI). It can export telemetry to Grafana, Datadog, New Relic, or any OTLP-compatible backend. Key features include distributed tracing, an evaluation hub (online/offline via UI and SDKs), prompt management with versioning, a playground called OpenGround for experimenting with prompts and models, multi-deployment management via Fleet Hub, a Secrets Vault for API key handling, and GPU/vector DB monitoring.
Behind the Verdict
OpenLIT stands out in the crowded LLM observability space by being fully open-source and free to self-host with no usage limits or feature gates. Built on OpenTelemetry, it integrates naturally into existing observability stacks—you can send data to Grafana, Datadog, or any OTLP backend. The feature set is impressive: distributed tracing, token/cost tracking, GPU monitoring (NVIDIA & AMD), prompt versioning, an evaluation hub for online/offline evals, and a playground called OpenGround for experimenting with prompts and models. Fleet Hub lets you manage multiple deployments from one dashboard. The zero-code Kubernetes operator is a nice touch for teams running on K8s. That said, OpenLIT is not a plug-and-play SaaS. You'll need Docker and some operational know-how to self-host; the cloud tier is still in waitlist mode. While community support via GitHub and Discord is active, there are no commercial SLAs unless you sponsor at higher tiers ($50/month for monthly calls). GPU monitoring and advanced evaluations may require extra configuration. If you need managed infrastructure, out-of-the-box guardrails, or enterprise support today, you might prefer Langfuse (cloud) or Datadog LLM Observability. But for teams that prioritize data sovereignty, flexibility, and zero cost, OpenLIT is hard to beat.
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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.
An AI engineer adds a few lines of code to a Python LLM app to initialize OpenLIT, then views real-time traces and token costs in the dashboard.
Outcome: Identifies that the app is using high-cost models for simple queries, then switches to a cheaper model, reducing monthly API spend.
A DevOps engineer deploys the OpenLIT Kubernetes operator to a cluster, automatically instrumenting all LLM workloads without code changes.
Outcome: Gains visibility into GPU usage, request latency, and error rates across microservices, enabling proactive scaling and bottleneck resolution.
The team lead sets up OpenGround to experiment with different prompts and models, then runs offline evaluations via the evaluation hub.
Outcome: Compares prompt variants and selects the best performing one, which is then versioned and deployed via Prompt Hub across all environments.
Use Cases
- Monitor LLM application performance and trace requests end-to-end with OpenTelemetry.
- Run offline evaluations to compare prompt variations and model outputs.
- Manage and version prompts centrally, then deploy them across environments.
- Track token usage and GPU metrics to optimize cost and resource allocation.
- Get a unified dashboard across multiple deployments using Fleet Hub.
- Automatic instrumentation of Kubernetes workloads without code changes.
- Export observability data to existing Grafana or Datadog dashboards.
Limitations
- OpenLIT self-hosted requires Docker and Docker Compose to run; cloud managed option is not yet available.
- Setup involves running a docker command and configuring OpenTelemetry endpoints.
- Some advanced features may require additional configuration.
as of 2026-07-06
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.
Self-Hosted
$0/forever
Ideal for
Teams with DevOps capabilities who want full control of their observability data at zero cost.
What this tier adds
Starting tier: free forever with Apache 2.0 license, no usage limits, no license key.
Token Supporter
$10/month
Ideal for
Solo developers or small teams who want to support OpenLIT development and get priority issue triage.
What this tier adds
Adds community supporter badge, priority GitHub issue triage, and access to sponsor channel on Discord compared to Self-Hosted.
Context Window Hero
$50/month
Ideal for
Companies or power users who want direct input on roadmap and monthly syncs with the core team.
What this tier adds
Adds logo in README, direct input on roadmap priorities, and monthly call with core team compared to Token Supporter.
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 forever with Apache 2.0 license and no usage limits. For teams that can handle Docker, it beats per-seat pricing of Langfuse (cloud) or Helicone (usage-based). But if you lack DevOps resources, you'll incur the time cost of self-hosting or need to wait for OpenLIT Cloud.
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 Python apps: install pip package, call openlit.init() — under 1 minute. For Docker self-hosting: run `docker compose up -d` in the repo — under 2 minutes. For Kubernetes: deploy the operator via a single manifest — under 5 minutes.
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 (self-hosted): Export training data via Langfuse's export API and import into OpenLIT's evaluation hub.
- →From Helicone: Migrate proxy logs by configuring OpenLIT as a tracing backend while keeping Helicone proxy active temporarily.
- ↗To Langfuse Cloud: Use OpenLIT's OpenTelemetry export to send traces to Langfuse's OTLP endpoint.
- ↗To Datadog LLM Observability: Configure OpenLIT to export OTLP to Datadog's intake endpoint.
- ↗To Grafana: OpenLIT natively exports to any OTLP backend—point at Grafana's OTLP gateway.
Integrations
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Official links
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