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vLLM vs Groq

vLLMGroq

Bottom line: vLLM for teams self-hosting open-weight models; Groq for developers building latency-sensitive apps.

vLLMvLLM

High-throughput open-source LLM inference engine

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GroqGroq

Very fast LLM inference on custom LPU hardware.

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Votes00
PricingFreeFreemium
CategoryCodingCoding
Tags
llm-inferenceopen-sourcemodel-servingself-hostedgpu
inferencellm-apilow-latencyopen-sourcehardware
Best for
  • Teams self-hosting open-weight models
  • ML platform and infra engineers
  • High-throughput production inference
  • Developers building latency-sensitive apps
  • Teams running AI agents
  • Voice and real-time product builders
Pros
  • Completely free and open source (Apache 2.0)
  • Industry-leading throughput via PagedAttention
  • OpenAI-compatible API for easy integration
  • Broad model and quantization support
  • Multi-GPU tensor and pipeline parallelism
  • Exceptional inference speed on supported models
  • Competitive per-token pricing
  • OpenAI-compatible API is easy to adopt
  • Free tier with no credit card
  • Good fit for agents and real-time apps
Cons
  • You must provide and manage GPUs and infrastructure
  • No official managed cloud from the project
  • Rapid release cadence can introduce breaking changes
  • Requires ML systems knowledge to tune and operate
  • No built-in team collaboration or UI
  • Limited to a curated catalog of open models
  • No hosting of arbitrary custom weights
  • Model lineup changes over time
  • Corporate turbulence in 2026 (Nvidia deal, down round)
  • Free-tier rate limits are modest

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