Evidently AI vs Langfuse
A side-by-side comparison of Evidently AI and Langfuse, two Observability tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
Langfuse
ObservabilityOpen-source LLM observability. Self-hostable, OpenTelemetry-native.
View LangfuseAt a glance
| Attribute | Evidently AI | Langfuse |
|---|---|---|
| Category | Observability | Observability |
| Pricing | FREEMIUM | FREEMIUM |
| License | Open core | Open core |
| Deployment | Hybrid | Hybrid |
| Platforms (differs) | Web, API | API, Web |
| Model support | Model-agnostic | Model-agnostic |
| Vendor (differs) | Evidently AI | Langfuse |
The honest brief
Evidently AI
One library spanning classic ML monitoring and LLM/RAG evals — 100+ metrics from data drift to hallucination — with an optional cloud.
- Open source (Apache-2.0), self-hostable
- Covers both ML and LLM evaluation
- Built-in metrics and presets
- LLM-as-judge plus drift detection
- Optional hosted cloud with free tier
- Python-library learning curve
- Less agent-trace-centric than rivals
- Cloud features gated to paid tiers
- Reports can get heavy at scale
Langfuse
The MIT-licensed, self-hostable answer to LangSmith — own your observability data, framework-agnostic.
- Own your observability data
- Framework-agnostic, OTel-native
- Tracing + evals + prompt mgmt
- Transparent unit-based pricing
- Self-host infra cost at scale
- Less deep LangChain integration
- Setup heavier than hosted-only