Vllm
High-performance LLM inference engine for fast AI model execution.
The gold-standard open-source LLM serving engine — free to self-host, battle-tested at massive scale.
vLLM — free open-source LLM inference and serving engine (Apache 2.0).
vLLM is a community-driven, Apache-licensed library for high-throughput LLM inference, originally from UC Berkeley's Sky Computing Lab. The DB lists it at $19/month with a 'freemium' tier — both are incorrect: vLLM is entirely free open-source software with no paid SaaS tier. Its PagedAttention memory algorithm and continuous batching make it the de facto industry-standard self-hosted inference engine, powering production deployments at Amazon, Meta, LinkedIn, and Stripe.
vLLM has become the dominant self-hosted inference stack due to its PagedAttention breakthrough, broad hardware support (NVIDIA, AMD, TPU, Intel), and OpenAI-compatible API that makes migration trivial. Active community with thousands of GitHub stars and contributions from major AI labs.
Requires meaningful GPU infrastructure to run — not a zero-ops solution. Configuration complexity grows with multi-GPU/multi-node deployments, and keeping up with rapidly evolving model support requires ongoing maintenance.
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Alternatives
Inferact (commercial managed vLLM) or Anyscale Endpoints for enterprise SLA and managed infra.
Ollama for local developer inference with a simpler setup and smaller model focus.
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