# OpenLIT vs Helicone

Helicone routes traffic through a proxy to capture telemetry. OpenLIT instruments your existing SDK calls directly — no proxy, no added latency, fully OpenTelemetry-native.

| Feature | OpenLIT | Helicone |
| --- | --- | --- |
| Core Architecture |
| OpenTelemetry-native |  |  |
| Open Source | Apache 2.0 | Apache 2.0 |
| Self-hostable |  |  |
| Proxy-free (no added latency) |  |  |
| SDK-based instrumentation |  |  |
| Vendor-neutral data format |  |  |
| LLM Monitoring |
| Token usage tracking |  |  |
| Cost per request |  |  |
| Latency / p95 metrics |  |  |
| Prompt & response logging |  |  |
| 60+ integrations (LLMs, frameworks, VectorDBs, GPUs) |  | OpenAI-compatible endpoints only |
| Infrastructure Monitoring |
| GPU monitoring (NVIDIA + AMD) |  |  |
| Vector DB tracing |  |  |
| Multi-environment tagging |  |  |
| Organisation management |  |  |
| Developer Tools |
| Prompt Hub (versioning) |  |  |
| Evaluations |  | Basic |
| Secrets Vault |  |  |
| Fleet Hub (multi-deployment) |  |  |
| Custom model pricing |  |  |
| Request caching |  |  |
| Rate limiting & key management |  |  |
| Observability Backends |
| Grafana / Prometheus export |  |  |
| Datadog export |  |  |
| Any OTLP-compatible backend |  |  |

## Choose OpenLIT when…

- You cannot tolerate proxy-introduced latency on every LLM call
- You need GPU monitoring for self-hosted models (NVIDIA or AMD)
- You use non-OpenAI-compatible APIs (Anthropic native SDK, Bedrock, Vertex AI, etc.)
- You want to forward telemetry to Grafana, Datadog, or any OTLP backend
- You need Vector DB observability alongside LLM request tracing

## Choose Helicone when…

- You want built-in request caching to reduce API costs
- You need proxy-layer rate limiting and API key management
- You prefer a cloud-first setup with minimal local infrastructure

## More Comparisons

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