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vLLM vs Together AI

vLLMTogether AI

Bottom line: vLLM for teams self-hosting open-weight models; Together AI for cost-conscious teams on open models.

vLLMvLLM

High-throughput open-source LLM inference engine

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Together AITogether AI

Inference, fine-tuning, and GPU clusters for open models.

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Votes00
PricingFreeFreemium
CategoryCodingCoding
Tags
llm-inferenceopen-sourcemodel-servingself-hostedgpu
inferencefine-tuninggpu-cloudopen-sourcellm-api
Best for
  • Teams self-hosting open-weight models
  • ML platform and infra engineers
  • High-throughput production inference
  • Cost-conscious teams on open models
  • ML teams that fine-tune
  • Startups scaling inference volume
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
  • Large catalog of open and open-weight models
  • Competitive per-token pricing
  • Fine-tuning with weight ownership
  • Dedicated GPU clusters for scale
  • OpenAI-compatible API
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
  • Broad pricing surface across several product lines
  • You own quality and safety evaluation of open models
  • Dedicated clusters require commitment and planning
  • Less turnkey than closed frontier APIs
  • Model catalog and prices change over time

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