Should You Use vLLM?
Use the interactive decision tree below to figure out if vLLM is the right choice for your workload. Click an option to see the recommendation.
What best describes your workload?
Real-World Deployments
Roblox
1B+ tokens/week
Powers the AI Assistant feature for millions of concurrent users, keeping inference costs manageable at massive scale.
Stripe
73% cost reduction
Migrated LLM inference to vLLM, handling 50M daily API calls on one-third of its previous GPU fleet.
IBM watsonx
Enterprise-scale
Integrates vLLM for enterprise LLM serving, leveraging multi-model support and quantization for diverse workloads.
Meta
Core contributor
Uses vLLM for internal LLM serving and contributes as a key maintainer of the project.
When NOT to Use vLLM
Very short prompts and responses (< 50 tokens): When both input and output are tiny, scheduling overhead becomes a larger fraction of total latency. Simpler frameworks may have lower per-request overhead.
Strict per-request GPU isolation: vLLM batches multiple requests onto the same GPU for throughput. If you need strict isolation between requests for security or billing, you need a different approach.