Head-to-head comparison
GroqvsTogether AI
Groq and Together AI are both inference infrastructure providers for open-weight models, but they optimized for different things. Groq built custom LPU chips specifically for extremely fast token generation, with a narrower model selection. Together AI hosts a much broader catalog of open-weight models and adds fine-tuning and training infrastructure on top, at solid but not class-leading speed.
At a glance
| Groq | Together AI | |
|---|---|---|
| Category | Chat | Coding |
| Function | Fast inference chat | Open-model inference |
| Pricing | Freemium | Freemium |
| Starting price | Free chat playground, API pay-as-you-go from a few cents per million tokens | Free credits, then $0.20-$5/M tokens |
| Editor rating | 4.5 / 5 | — |
| Released | July 2026 | June 2022 |
Groq
Pros
- Speed is not marketing spin, it is noticeably faster than GPT or Claude chat
- Free tier is enough to actually get work done, not just a demo
- API pricing undercuts most GPU-based inference providers
Cons
- No access to closed frontier models like GPT or Claude
- Model selection depends on what Groq has bothered to host
- Speed advantage matters less for tasks that are thinking-bound, not typing-bound
Together AI
Pros
- Fastest inference for Llama 4 / Qwen3
- Pricing 60-80% cheaper than frontier closed models
- Strong fine-tuning tooling
Cons
- Only open-weight models — no GPT or Claude
- Closed-source models still better at some tasks
- Some bleeding-edge models added late
Which one should you pick?
Pick Groq if
Your application is latency-critical — a real-time voice agent or anything where response speed is itself part of the product — and the model you need is one of the ones Groq hosts.
See Groq details →Pick Together AI if
You need a wide choice of open-weight models, want to fine-tune a model on your own data, or need training infrastructure beyond plain inference.
See Together AI details →Bottom line
Groq for raw speed on a narrower model list. Together AI for model variety and fine-tuning on top of solid inference. Teams building latency-sensitive products lean Groq; teams experimenting across many open models or training their own variants lean Together AI.