opik vs helicone
LLM observability and tooling compared
opik and helicone help teams inspect and monitor LLM applications, including requests and traces. opik emphasizes evaluation across development and production, while helicone combines observability with a model-routing gateway.
opik: Trace, Evaluate, and Monitor LLM Applications
Opik is Comet’s open-source platform for tracing, evaluating, and monitoring LLM applications, RAG systems, and AI agents. It combines framework integrations, datasets and experiments, prompt tools, and production dashboards, with cloud and self-hosted options.
helicone: Monitor and Route LLM Requests
Helicone is a TypeScript platform for observing LLM requests and routing them across model providers. It helps AI teams inspect traces, track cost and latency, manage prompts, and self-host the observability stack.
| opik | helicone | |
|---|---|---|
| Language | Python | TypeScript |
| License | Apache-2.0 | Apache-2.0 |
| Stars | 22.8k | 6.2k |
| Forks | 1.9k | 683 |
| Last analyzed | Oct 22, 2026 | Oct 7, 2026 |
Key differences
- opik focuses on traces, datasets, experiments, and built-in evaluation metrics; helicone adds a unified gateway for routing across more than 100 models, with fallbacks.
- opik supports Python and TypeScript SDKs and framework integrations; helicone is a TypeScript platform with a gateway API and provider integrations.
- opik offers self-hosting, Comet Cloud, and deployment guidance that includes Kubernetes and Helm; helicone offers hosted use or self-hosting, with a documented multi-component architecture and Docker deployment.
- opik describes evaluation workflows for CI/CD and ongoing RAG assessment; helicone highlights request cost and latency, prompt versioning, and provider routing.
- Both use the Apache-2.0 license. opik lists 22.4k stars and 1.8k forks, while helicone lists 6.2k stars and 683 forks.
- helicone's project notes report no release in over a year and relatively slow issue resolution; opik's notes do not describe release cadence.
Choose opik if you…
- need datasets, experiments, and built-in metrics to evaluate LLM outputs.
- want to run evaluations in CI/CD or monitor RAG quality over time.
- prefer Python and TypeScript SDK options or deployment choices that include Kubernetes and Helm.
Choose helicone if you…
- need a unified API to route requests across model providers with fallbacks.
- want to track request cost and latency while managing prompt versions through a gateway.
- prefer a TypeScript platform centered on request observability and provider routing.
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This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.