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DeepInfra vs vLLM

DeepInfravLLM

Bottom line: DeepInfra for cost-sensitive developers; vLLM for teams self-hosting open-weight models.

DeepInfraDeepInfra

Cheapest serverless inference for open-source LLMs, pay per token

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vLLMvLLM

High-throughput open-source LLM inference engine

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Votes00
PricingPaidFree
CategoryAi InfrastructureAi Infrastructure
Tags
serverless-inferencellm-apiopen-source-modelsgpu-rentalpay-per-token
llm-inferenceopen-sourcemodel-servingself-hostedgpu
Best for
  • Cost-sensitive developers
  • Startups scaling inference
  • Batch workloads
  • Teams self-hosting open-weight models
  • ML platform and infra engineers
  • High-throughput production inference
Pros
  • Among the lowest per-token prices
  • Pay only for tokens, no idle charges
  • OpenAI-compatible API for easy migration
  • Discounted batch inference
  • Latency tiers to trade cost vs speed
  • 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
  • Focused on open-source, not proprietary models
  • No free plan
  • Latency and reliability vary by tier
  • Fewer enterprise features than large clouds
  • No self-hosting
  • 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

Comparison generated from each tool's listing. Add or remove tools above to change it.