Langfuse vs LiteLLM
Side-by-side comparison of features, pricing, and ratings
At a glance
| Dimension | Langfuse | LiteLLM |
|---|---|---|
| Pricing | Open-source (free) + Cloud with usage-based pricing | Open-source (free) + Enterprise from $5K/yr |
| Best For | Engineering teams, production LLM observability | Platform teams, multi-provider cost tracking |
| Core Feature | Observability, evaluations & prompt management | AI gateway with fallbacks & spend tracking |
| LLM Support | Any model (traced via integrations) | 100+ models via unified API |
| Self-Hosting | Yes, open-source | Yes, open-source |
| Latest News | Multi-modal datasets, monitors & alerts, AI search | Migrating core to Rust for performance |
If you need to centrally manage and route requests across 100+ LLMs with cost tracking and fallbacks, LiteLLM is your gateway. If you need deep observability, prompt management, and evaluations for production LLM apps, Langfuse is the observability layer. They integrate together, so a powerful stack uses both.
Open-source LLM observability that traces, evaluates, and manages prompts from prototype to production.
Visit Website
Self-hosted open-source AI gateway for 140+ LLM providers, one OpenAI API, cost control.
Visit WebsiteWho should pick which
- Platform team managing multi-provider accessPick: LiteLLM
LiteLLM provides unified API, fallbacks, and spend tracking across 100+ LLMs.
- Production LLM engineer debugging tracesPick: Langfuse
Langfuse offers deep hierarchical traces, evaluations, and prompt management.
- Enterprise needing cost chargebacks per teamPick: LiteLLM
LiteLLM tracks spend per key/user/team/org and integrates with S3/GCS.
- Team building multi-modal LLM appsPick: Langfuse
Langfuse now supports multi-modal datasets with images, audio, video.
- Team wanting observability + prompt versioningPick: Langfuse
Langfuse combines tracing, evals, playgrd, and prompt management in one platform.
Frequently Asked Questions
Langfuse vs LiteLLM: which should you choose?
If you need to centrally manage and route requests across 100+ LLMs with cost tracking and fallbacks, LiteLLM is your gateway. If you need deep observability, prompt management, and evaluations for production LLM apps, Langfuse is the observability layer. They integrate together, so a powerful stack uses both.
Can LiteLLM and Langfuse be used together?
Yes. LiteLLM integrates with Langfuse for LLM observability, enabling cost tracking and tracing.
Which is better for multi-provider fallback?
LiteLLM, as it provides automatic fallbacks and retries across 100+ LLMs with cooldowns.
Does Langfuse offer gateway features like load balancing?
No. Langfuse focuses on observability, not request routing or load balancing.
Are both products open-source?
Yes, both are open-source and can be self-hosted for free.
Which one has better evaluation tools?
Langfuse, with LLM-as-a-judge, heuristic evals, code evaluators, and human annotation.
Does LiteLLM have a cloud managed version?
LiteLLM offers an Enterprise tier but primarily targets self-hosted or proxy deployment.
Does Langfuse support multi-modal data?
Yes, as of June 2026, Langfuse supports images, audio, video, and documents in datasets.
Which product is better for cost tracking?
LiteLLM, with automatic spend tracking per key/user/team/org and budget enforcement.
More Langfuse or LiteLLM comparisons
Choose Langfuse if your priority is observability, debugging, and prompt management for production LLM apps, with a need for multi-modal evals and alerts. Choose LangGraph if you're building complex,
If you need deep agent debugging with autonomous failure clustering and fix suggestions, LangSmith is the edge. If you want open-source flexibility, self-hosting, and unified prompt management plus ob
Choose LangChain if you need deep agent observability, evaluation, and production deployment with checkpointing and human-in-the-loop; its latest prompt caching (June 2026) cuts latency/cost for repea
If you need a single open-source platform that covers both traditional ML (experiment tracking, model registry) and LLM agents (tracing, prompt versioning, AI Gateway), choose MLflow. If your primary
Choose Promptfoo if your priority is AI security — automated red teaming, guardrails, and CI/CD scanning against 50+ attack types, backed by recent OpenClaw injection analysis and ModelAudit launch. C
Explore each tool further
Browse these categories
One email a week — new tools, honest comparisons, no spam.
Last reviewed: May 12, 2026