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SkyPilot vs LiteLLM

SkyPilotLiteLLM

Bottom line: SkyPilot for aI teams spanning multiple clouds; LiteLLM for engineering teams juggling multiple LLM providers.

SkyPilotSkyPilot

Open-source framework to run AI workloads on any cloud, cluster, or GPU

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LiteLLMLiteLLM

Open-source AI gateway to call 100+ LLM APIs in one format

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Votes00
PricingFreemiumFreemium
CategoryAi InfrastructureCoding
Tags
multi-cloudgpu-orchestrationai-computeopen-sourcekubernetes
llm-gatewayopen-sourceapi-proxymodel-routingllmops
Best for
  • AI teams spanning multiple clouds
  • Cost-sensitive GPU-heavy workloads
  • Training, fine-tuning, and RL jobs
  • Engineering teams juggling multiple LLM providers
  • Platform teams building an internal AI gateway
  • Startups wanting free multi-model routing
Pros
  • Runs on any cloud, neocloud, or Kubernetes
  • Automatically finds cheapest available GPUs
  • Open-source with 14M+ downloads
  • Spot-instance recovery and data sync built in
  • Used by notable AI companies
  • Free, actively maintained open-source core with a large community
  • Supports 100+ providers through one OpenAI-compatible interface
  • Built-in cost tracking, budgets, and virtual keys
  • Load balancing, retries, and fallbacks for reliability
  • Can be fully self-hosted for data control
Cons
  • Requires cloud credentials and infra knowledge
  • Not a compute provider — you pay clouds directly
  • Platform pricing is enterprise/quote-based
  • YAML-based workflow has a learning curve
  • Best value requires multi-cloud setup
  • Self-hosting means you own deployment, scaling, and maintenance
  • Advanced governance (SSO, RBAC, audit logs) requires the paid Enterprise tier
  • Enterprise pricing is negotiated and not fully transparent
  • Acting as a proxy adds an operational hop to debug when issues arise
  • Feature breadth can make initial configuration complex

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