Pick Groq if your one job is fast inference through a simple OpenAI-style API and you don't plan to train or tune models. Pick Together AI if you also want fine-tuning, post-training, dedicated inference or your own GPU clusters under one account. Both offer OpenAI-compatible endpoints, so moving code between them later is a small job.
You've got a product that calls a model, and you want those calls answered quickly and reliably. That's where both of these start. Where they part company is everything around the call: whether you'll ever shape the model yourself, and whether you'll need raw GPU capacity of your own.
Our verdict, before the detail
If all you need is a quick, OpenAI-style endpoint for open models, and the team's happy to stay on inference only, Groq is the tighter fit. It pitches itself on one idea, that inference is the bottleneck, and it was built for that. If you expect to fine-tune, run post-training, book dedicated capacity or rent GPU clusters, Together AI covers far more of that journey in one place. Both lean hard on speed in their marketing, but those are the vendors' own words, so treat them as claims rather than results.
The rest of this page explains why, grouped by the jobs you're likely to have.
Neither is quite the company its reputation suggests
Most people still think of the first one as "the custom chip company". That's out of date. It now calls itself a neocloud, and its homepage talks about pairing its own LPU chips with NVIDIA GPUs through something it calls LPX. The vendor says this means you no longer have to trade speed against running things economically. Whatever you make of that claim, it's clearly no longer a pure custom-silicon story.
The second one has gone the other way, widening rather than narrowing. It positions itself as an AI native cloud, running from serverless inference right through to GPU clusters and storage. So you're not comparing two inference APIs. You're comparing a focused inference cloud with a full stack that happens to include inference.
The good news for anyone nervous about lock-in: both expose OpenAI-compatible endpoints. If you build against that shape, switching between them, or using both, shouldn't mean rewriting much code.
Grouped by what you'll be doing day to day
Serving a model in production
The inference-first cloud gives you an API with service tiers, a Flex option and batch processing, plus LoRA inference in its docs. The full-stack cloud has serverless, batch, provisioned throughput, dedicated model and dedicated container inference. If you know you'll want a model on capacity that's yours alone, that longer menu is the reason to look closer.
Shaping the model to your data
This is the clearest split. The inference-first cloud covers LoRA inference in its docs, but it doesn't sell a training product. The full-stack cloud offers post-training with SFT and GRPO, and managed fine-tuning with SFT and DPO. If tuning is anywhere on your roadmap, that settles it.
Hardware underneath
One runs on its own LPU chips, now combined with NVIDIA GPUs through LPX. The other lists NVIDIA GPU clusters for compute, naming GB300, B200, H200 and H100. For most app builders the hardware is invisible behind the API, but if your team wants to rent GPU capacity directly, only one of these sells it that way.
Agents and coding tools
The LPU cloud's docs cover built-in tools, code execution, MCP connectors and coding integrations with Cline and Roo Code. Together AI has Together Link in beta, which it says lets you run open models in your own coding harness, plus a setup guide for coding agents. Both are moving here, with the first one showing more ready-made pieces.
API shape
The first offers an OpenAI base URL and Responses API compatibility. The second adds Messages and Responses APIs on top of OpenAI compatibility. Either way, your existing client libraries are likely to work with small changes.
Both side by side
| Groq | Together AI | |
|---|---|---|
| Core focus | Inference only, built for it | Inference plus post-training, fine-tuning, evaluations, GPU clusters and storage |
| Hardware | Own LPU chips, combined with NVIDIA GPUs through LPX | NVIDIA GPU clusters (GB300, B200, H200, H100) |
| Inference options | Service tiers, Flex, batch, LoRA inference | Serverless, batch, provisioned throughput, dedicated model and container |
| API compatibility | OpenAI base URL, Responses API | OpenAI compatible, plus Messages and Responses APIs |
| Model shaping | LoRA inference only; no training product | Post-training (SFT, GRPO), managed fine-tuning (SFT, DPO) |
| Agent tooling | Built-in tools, code execution, MCP connectors, Cline and Roo Code | Together Link beta, coding agent setup guide |
If you're weighing more than these two, my roundup of other model APIs worth comparing goes wider, and if you're still choosing which model to run in the first place, start with the open-source LLMs worth building on.
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Other inference platforms to weigh up
Fireworks AI
Best for: One platform to run, ensemble and train open models, for teams who want to own specialised models of their own.
Replicate
Best for: Run and fine-tune models or deploy custom ones with one line of code, with a playground to compare models and many image models.
OpenRouter
Best for: A unified interface to many models from many providers, with the vendor saying it falls back to other providers when one goes down.
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Frequently asked questions
Is Groq only for its own LPU chips?
Not any more. Its homepage says it pairs the LPU with NVIDIA GPUs through LPX, and it now calls itself a neocloud rather than only a chip company.
Can I fine-tune a model on Groq?
Its docs cover LoRA inference, but there's no training product on its site. If you need fine-tuning or post-training, Together AI offers managed fine-tuning with SFT and DPO, and post-training with SFT and GRPO.
How hard is it to switch between them?
Usually not very. Both offer OpenAI-compatible APIs and Responses API support, so code written for one should move to the other with small changes.
Which is faster?
Both make speed claims, but they're the vendors' own marketing. Run your own workload through each before deciding on speed alone.
Can I rent GPUs directly?
Together AI lists NVIDIA GPU clusters, including GB300, B200, H200 and H100. Groq sells inference through its API rather than raw GPU capacity.
