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Koyeb vs vLLM

KoyebvLLM

Bottom line: Koyeb for aI startups deploying inference; vLLM for teams self-hosting open-weight models.

KoyebKoyeb

Serverless cloud with scale-to-zero GPUs for AI inference and apps

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vLLMvLLM

High-throughput open-source LLM inference engine

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Votes00
PricingFreemiumFree
CategoryAi InfrastructureAi Infrastructure
Tags
serverlessgpuinferencescale-to-zerodeployment
llm-inferenceopen-sourcemodel-servingself-hostedgpu
Best for
  • AI startups deploying inference
  • Developers wanting autoscaling
  • Cost-conscious GPU users
  • Teams self-hosting open-weight models
  • ML platform and infra engineers
  • High-throughput production inference
Pros
  • Scale-to-zero saves idle GPU cost
  • Per-second billing
  • Competitive H100 pricing
  • Broad GPU range up to B200
  • Global multi-region deployments
  • 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
Cons
  • Flat plan fee on top of usage
  • Not a full hyperscaler feature set
  • GPU availability can vary
  • Less mature ecosystem than AWS/GCP
  • Enterprise controls still maturing
  • 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

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