LiteLLM vs 
Langfuse
LiteLLM vs Langfuse compared for 2026 — features, license, ease of use, performance and which one to choose. One API for 100+ LLM providers vs See what your LLM app actually did.
Updated regularly · curated by OpenSourceAI.tech
LiteLLM vs Langfuse at a glance
| Spec | LiteLLM | Langfuse |
|---|---|---|
| Category | LLM / RAG framework | LLM / RAG framework |
| Type | LLM gateway / SDK | LLM observability |
| License | MIT | MIT |
| Runs locally | Cloud-optional | Yes |
| Primary language | Python | TypeScript |
| Ease of use | Beginner | Intermediate |
| Best for | teams standardizing on one LLM interface | debugging and monitoring LLM apps in production |
| GitHub stars | 53.8k | 31.3k |
How LiteLLM and Langfuse score
| Criterion | LiteLLM | Langfuse |
|---|---|---|
| Popularity | 4.5 | 4.0 |
| Maintenance | 5.0 | 5.0 |
| Ease of use | 5.0 | 3.5 |
| Privacy | 3.5 | 5.0 |
| License freedom | 5.0 | 5.0 |
Scores are computed automatically from public signals — GitHub stars (popularity), recent commit activity (maintenance), license type (freedom), local-first design (privacy) and onboarding complexity (ease of use). Indicative, not a verdict.
What each one is
LiteLLM
LiteLLM is a gateway and SDK that exposes 100+ LLM providers behind the OpenAI format, adding routing, fallbacks, budgets and observability.
- OpenAI-format access to 100+ providers
- Routing, fallbacks, budgets and rate limits
- Proxy server for org-wide governance
Langfuse
Langfuse traces every LLM call, tool use and cost in your application, with prompt management and evaluation built in — self-hostable.
- Full tracing of chains and agents
- Cost and latency tracking
- Self-hosted, MIT licensed
Key differences
LiteLLM is lLM gateway / SDK, while Langfuse is lLM observability. LiteLLM leans more beginner-friendly, whereas Langfuse is more suited to intermediate users. They also differ in how they run (Cloud-optional vs Yes). In short, LiteLLM fits teams standardizing on one LLM interface, and Langfuse fits debugging and monitoring LLM apps in production.
Which should you choose?
Choose LiteLLM for teams standardizing on one LLM interface. Choose Langfuse for debugging and monitoring LLM apps in production.
There is rarely one winner — many setups use both. The right pick depends on your hardware, your team's skills, and whether you value simplicity or control.
Frequently asked questions
Is LiteLLM or Langfuse easier to use?
LiteLLM is generally the easier of the two to get started with, while Langfuse rewards more setup with more control.
Are LiteLLM and Langfuse free?
LiteLLM is free and open source (MIT), and Langfuse is free and open source (MIT). Neither charges for the core software.
Can I run LiteLLM and Langfuse locally?
LiteLLM: cloud-optional · Langfuse: yes. Both can be used without sending your data to a third-party cloud where their setup allows.
LiteLLM vs Langfuse — which should I pick in 2026?
Choose LiteLLM for teams standardizing on one LLM interface. Choose Langfuse for debugging and monitoring LLM apps in production.
People also compare
Explore more open-source AI
Browse thousands of open-source AI tools, models and projects — all curated in one place, updated daily.
Explore the directory →