Reserved GPU clusters for training

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Committed clusters for sustained training work.
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Serverless model endpoints

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Run open models without managing GPUs.
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Pay-per-token inference

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Only pay for the tokens you generate.
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Saving on Together AI

Inference is billed per token, so you only pay for what you actually generate. Choosing the right model size for the task is the simplest way to keep that bill down.

How to keep costs down

  1. Use a smaller open model where it does the job.
  2. Pay per token for inference rather than renting idle GPUs.
  3. Reserve GPU clusters only for sustained training runs.

Worth knowing

Serverless endpoints suit spiky traffic, while reserved clusters make sense for heavy, steady work. Building on open models also avoids per seat lock in to a closed provider.