Why it matters
- The fastest integration path for LLM observability — literally one line of code (change base URL) vs. SDK instrumentation.
- Captures 100% of LLM calls automatically as a transparent proxy — nothing slips through even if you forget to add tracing.
- Real-time cost tracking across providers — essential for teams managing LLM API budgets.
- Open source, so self-hosting is an option for teams with strict data privacy requirements.
Key capabilities
- Proxy-based logging: Change your API base URL to Helicone's proxy; every request is logged automatically.
- Cost tracking: Per-request and aggregate cost in USD by provider, model, and user property.
- Latency analytics: P50/P95/P99 latency; slow request identification; model comparison.
- Request/response logging: Full prompt and completion stored with configurable retention.
- User tracking: Add
Helicone-User-Idheader to attribute usage to specific users or sessions. - Rate limiting: Configure per-user rate limits via Helicone headers — protect against runaway usage.
- Caching: Cache identical LLM requests to reduce costs and latency.
- Custom properties: Tag requests with metadata (experiment name, environment, model version) for filtering.
Technical notes
- Integration: Change base URL to Helicone proxy and add
Helicone-Authheader — no SDK needed - Proxy URLs:
oai.helicone.ai(OpenAI),anthropic.helicone.ai(Anthropic),groq.helicone.ai(Groq) - Open source: github.com/Helicone/helicone — MIT license; self-hostable
- Pricing: Free (10K req/mo); Growth $80/mo; Pro $200/mo; Enterprise custom
- Streaming: Full SSE streaming support
- Data retention: 30 days on free; 90 days on Growth; Enterprise configurable
- Founded: 2023 by Justin Torre; San Francisco; YC W23
Ideal for
- Teams who want LLM observability with minimal integration effort — no code instrumentation required.
- Applications already using OpenAI SDK where changing one URL is the preferred integration path.
- Teams tracking multi-user LLM usage and costs who need per-user attribution and rate limiting.
Not ideal for
- Complex evaluation and prompt management workflows — LangSmith or LangFuse are more comprehensive.
- Teams using custom LLM architectures that don't fit the proxy model.
- Teams with strict data residency requirements who can't route API traffic through Helicone's servers (self-host instead).