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Comparison

Groq vs Together AI

Fast open-model inference: throughput vs breadth.

1 min readupdated 2026-07-04

/ quick answer

Both host open-weight models via API. Groq's LPU delivers extreme tokens/sec; Together offers a wider model catalog and fine-tuning. Fast open-model inference: throughput vs breadth.

Fast open-model inference: throughput vs breadth. Both host open-weight models via API. Groq's LPU delivers extreme tokens/sec; Together offers a wider model catalog and fine-tuning. Recommendation: Groq for latency-critical UX; Together when you need model choice or LoRA fine-tunes. This comparison node is part of the Onexial knowledge graph and links to related concepts, workflows and tools below.
Overview
Both host open-weight models via API. Groq's LPU delivers extreme tokens/sec; Together offers a wider model catalog and fine-tuning.
Differences
DimensionOption AOption B
Speed500+ tok/s (Groq)Standard GPU speeds (Together)
Model catalogSelected (Groq)Very broad (Together)
Fine-tuningLimited (Groq)Yes (Together)
Use Cases
  • Real-time voice / streaming UI → Groq
  • Model diversity, fine-tunes → Together
Recommendation
Groq for latency-critical UX; Together when you need model choice or LoRA fine-tunes.
/ frequently asked

What is the difference in Groq vs Together AI?

Both host open-weight models via API. Groq's LPU delivers extreme tokens/sec; Together offers a wider model catalog and fine-tuning.

What are the main points of comparison?

Speed: 500+ tok/s (Groq) vs Standard GPU speeds (Together) · Model catalog: Selected (Groq) vs Very broad (Together) · Fine-tuning: Limited (Groq) vs Yes (Together)

Which one should I choose?

Groq for latency-critical UX; Together when you need model choice or LoRA fine-tunes.

/ topics#ai#infra