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
| Dimension | Option A | Option B |
|---|---|---|
| Speed | 500+ tok/s (Groq) | Standard GPU speeds (Together) |
| Model catalog | Selected (Groq) | Very broad (Together) |
| Fine-tuning | Limited (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.
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