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OpenLIT vs Helicone
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
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