These tools integrates with
vLLMvsLiteLLM
High-throughput LLM serving with PagedAttention versus Universal LLM proxy — 100+ models, one API
Compare interactively in Explore →Choose vLLM when…
- •You're serving LLMs at high throughput in production
- •Continuous batching and PagedAttention are needed
- •You're running your own GPU inference cluster
Choose LiteLLM when…
- •You want a unified API across 100+ LLM providers
- •You're switching between providers or running A/B tests
- •You need fallbacks and load balancing across models
Side-by-side comparison
Field
vLLM
LiteLLM
Category
LLM Infrastructure
LLM Infrastructure
Type
Open Source
Open Source
Free Tier
✓ Yes
✓ Yes
Pricing Plans
—
Enterprise: Custom
GitHub Stars
⭐ 32,000
⭐ 16,000
Health
●75 — Active
●75 — Active
vLLM
Production-grade LLM inference server. PagedAttention enables high throughput and efficient KV cache memory management.
Shared Connections3 tools both integrate with
Only vLLM (10)
LiteLLMModalRunPodAxolotlUnslothLlamaFactoryTorchtunePredibaseQwen-VLInternVL2
Only LiteLLM (29)
ContinueAiderClaude CodeOpenHandsPlandexCrewAILangGraphSemantic KernelLangChainCohere API
Explore the full AI landscape
See how vLLM and LiteLLM fit into the bigger picture — 207 tools, 452 relationships, all mapped.