LiteLLMvsMLflow

Full side-by-side comparison — features, pricing, platforms, and which one wins in 2026.

LiteLLM

LLM APIs & Inference

Unified API proxy for 100+ LLM providers — one interface, any model

Full review →Website ↗

MLflow

MLOps & Monitoring

Open-source platform for the ML lifecycle

Full review →Website ↗
FeatureLiteLLMMLflow
CategoryLLM APIs & InferenceMLOps & Monitoring
PricingFree (open-source), hosted proxy availableFree (open-source)
GitHub Stars
16k
✓ More stars
19k
PlatformsLinux, macOS, DockerLinux, macOS, Windows
Key Features
  • ✓ Unified API for 100+ LLM providers
  • ✓ Load balancing across multiple API keys/providers
  • ✓ Automatic fallbacks when providers fail
  • ✓ Spend tracking and budget alerts per team/project
  • ✓ Rate limiting and retry logic built-in
  • ✓ OpenAI SDK compatible — zero code changes
  • ✓ Self-hostable proxy server
  • ✓ Supports streaming, function calling, vision
  • ✓ Experiment tracking
  • ✓ Model registry
  • ✓ Deployment
  • ✓ Projects
  • ✓ Recipes
Pros
  • + One API for 100+ providers
  • + Built-in load balancing and fallbacks
  • + Spend tracking and rate limiting
  • + OpenAI SDK compatible
  • + Complete ML lifecycle management
  • + Framework-agnostic
  • + Strong model registry
  • + Apache open-source license
  • + Databricks integration
Cons
  • − Adds a proxy layer (slight latency)
  • − Complex config for advanced features
  • − UI is dated
  • − Setup can be complex
  • − Limited real-time monitoring
  • − Less polished than W&B
Tags
api-gatewaymulti-providerproxyopen-source
mlopstrackingdeploymentopen-source

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