DstackvsLiteLLM

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

Dstack

MLOps & Monitoring

Open-source engine for running AI workloads on any cloud

Full review →Website ↗

LiteLLM

LLM APIs & Inference

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

Full review →Website ↗
FeatureDstackLiteLLM
CategoryMLOps & MonitoringLLM APIs & Inference
PricingFree (open-source) + EnterpriseFree (open-source), hosted proxy available
GitHub Stars
5k
✓ More stars
16k
PlatformsLinux, macOSLinux, macOS, Docker
Key Features
  • ✓ Multi-cloud GPU
  • ✓ Dev environments
  • ✓ Training
  • ✓ Deployment
  • ✓ Cost optimization
  • ✓ 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
Pros
  • + Multi-cloud GPU management
  • + Cost optimization
  • + Training + deployment
  • + Open-source
  • + Cloud-agnostic
  • + One API for 100+ providers
  • + Built-in load balancing and fallbacks
  • + Spend tracking and rate limiting
  • + OpenAI SDK compatible
Cons
  • − Complex configuration
  • − Limited documentation
  • − Smaller community
  • − Requires cloud accounts
  • − Adds a proxy layer (slight latency)
  • − Complex config for advanced features
Tags
mlopsgpumulti-cloudopen-source
api-gatewaymulti-providerproxyopen-source

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