# Together AI — Optimizing inference speed and costs: Lessons learned from large-scale deployments

- Company: Together AI (together.ai)
- Announced: 2026-01-22T00:00:00+00:00
- Subject: Inference platform
- Models affected: DeepSeek-R1, Llama, Qwen, Mistral, DeepSeek, ATLAS
- Source: https://www.together.ai/blog/optimizing-inference-speed-and-costs
- Record: https://forck.live/items/2387-optimizing-inference-speed-and-costs-lessons-learned-from-large-scale

Together AI shares lessons learned from large-scale deployments on optimizing inference speed and costs, covering quantization, distillation, regional inference proxies, reducing compute stalls, decoding optimizations, and hardware choices.

## Evidence

Verbatim from https://www.together.ai/blog/optimizing-inference-speed-and-costs:

> How can teams reduce inference latency without massive costs? Achieving faster inference doesn't always mean paying more for a bigger cluster. At Together AI, we’ve seen teams that consistently deliver both low latency and low cost share these key habits:

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