# OpenLIT vs Langfuse

> Both are open-source LLM observability tools. OpenLIT is built natively on OpenTelemetry standards with GPU monitoring and broader infrastructure coverage. Here is a detailed comparison.

- HTML: https://openlit.io/compare/openlit-vs-langfuse
- Markdown: https://openlit.io/compare/openlit-vs-langfuse.md

Compare OpenLIT and Langfuse for LLM observability. See how OpenTelemetry-native monitoring, GPU tracking, and self-hosted deployment stack up.

## Feature comparison

### Core Architecture

| Feature | OpenLIT | Langfuse |
| --- | --- | --- |
| OpenTelemetry-native | Yes | OTel ingestion added in v3; primary SDK is proprietary |
| Open Source | Apache 2.0 | MIT (core) + EE (enterprise) |
| Self-hostable | Yes | Yes |
| Cloud-managed option | Coming soon | Yes |
| Vendor-neutral data export | Yes | Via OTel export (v3+) |

### LLM Monitoring

| Feature | OpenLIT | Langfuse |
| --- | --- | --- |
| Token usage tracking | Yes | Yes |
| Cost per request | Yes | Yes |
| Latency / p95 metrics | Yes | Yes |
| Prompt & response logging | Yes | Yes |
| Streaming support | Yes | Yes |
| 60+ integrations (LLMs, frameworks, VectorDBs, GPUs) | Yes | Relies on OTel SDKs or manual instrumentation |

### Infrastructure Monitoring

| Feature | OpenLIT | Langfuse |
| --- | --- | --- |
| GPU monitoring (NVIDIA + AMD) | Yes | No |
| Vector DB tracing | Yes | No |
| Multi-environment tagging | Yes | Yes |
| Organisation management | Yes | Yes |

### Developer Tools

| Feature | OpenLIT | Langfuse |
| --- | --- | --- |
| Prompt Hub (versioning) | Yes | Yes |
| Evaluations | Yes | Yes |
| Secrets Vault | Yes | No |
| Fleet Hub (multi-deployment) | Yes | No |
| Custom model pricing | Yes | No |

### Observability Backends

| Feature | OpenLIT | Langfuse |
| --- | --- | --- |
| Grafana / Prometheus export | Yes | Via OTel (v3+) |
| Datadog export | Yes | No |
| Any OTLP-compatible backend | Yes | Via OTel (v3+) |

## When to choose OpenLIT

- You need GPU monitoring for self-hosted LLMs (Ollama, vLLM) on NVIDIA or AMD hardware
- You want a fully OTel-native stack where every span is a standard OpenTelemetry span
- You need Vector DB observability alongside LLM monitoring
- You want Vault, Fleet Hub, and 60+ auto-instrumented integrations in one platform
- You are already using OpenTelemetry and want data to flow to any OTLP backend

## When to choose Langfuse

- You want a mature cloud SaaS with a larger community and dedicated support tiers
- Your primary workflow centres on human annotation, labelling, and feedback loops
- You need a polished evaluation and dataset management UI with fine-grained scoring

## Related

- All comparisons: https://openlit.io/compare.md
- Pricing: https://openlit.io/pricing.md
- Docs: https://docs.openlit.io/latest/openlit/quickstart-ai-observability