- What each one sells, in one table
- Why the two speed claims can't be lined up
- Hardware you can rent versus software you can route
- How each one proves its output is right
- Regions, media and the research behind them
- Plain advice: which team should pick which
- Three more inference providers worth a look
- Frequently asked questions
Choose Together AI if you want inference, rented GPU clusters, fine-tuning and generative media deployment from one provider. Choose Fireworks AI if you run several open models, want them routed through one endpoint across many regions, and care about published quality limits for each model. Neither page gives you a fair way to crown a speed winner.
Before you open either sign-up page, answer three questions about your own product.
- Do you need raw GPU capacity for training or your own workloads, or only an endpoint that answers model calls?
- Are you serving one model, or routing traffic across several open models at once?
- Does your customer or your legal team care which region the data passes through?
If you answered yes to the first, you're leaning towards Together. If the second and third matter more, Fireworks starts to look like the better match. The rest of this page explains why, and where the two vendors' own claims don't help you as much as they'd like.
What each one sells, in one table
| Area | Together AI | Fireworks AI |
|---|---|---|
| Product scope | Inference, Compute (GPU clusters, sandboxes, storage) and Model Shaping (post-training, fine-tuning, evaluations) | Inference, model ensembling and training, plus a Fireworks Lab |
| Inference tiers | Serverless, batch (up to 30 billion tokens per model), provisioned throughput with a 99% uptime SLA, dedicated model and dedicated container inference | Start serverless, then reserve throughput as you grow; pitches cache hits up to 99.6% |
| Raw hardware | GPU clusters, naming GB300, GB200, B300, B200, H200 and H100 | Leads with software optimisation and "your cloud or ours"; no GPU cluster line |
| Quality assurance | Speed from research such as adaptive speculative decoding, with lossless quality claimed | Publishes limits: KL divergence below about 0.007 and token rejection below about 3% against an unquantised reference |
| Modalities | One API for text, image, video, code and voice; dedicated containers for generative media | Focus on open model inference, routing and training for multi-model organisations |
| Data location | No data residency claim on its main pages | One endpoint across 30+ regions with policy-aligned data residency |
| Research roots | FlashAttention, ATLAS and ThunderKittens | Optimised by the creators of PyTorch; FireAttention study |
Why the two speed claims can't be lined up
This is the part most comparisons get wrong. Both vendors lead with speed, and both quote a multiplier, so it's tempting to put the numbers next to each other and pick the bigger one. Don't.
Together says its inference is up to 2.75x faster, and around 2x faster than the next fastest provider. Fireworks says it delivers up to 4x the throughput of providers that wrap vLLM. Those are two different baselines. One compares against the next fastest rival, the other against a whole class of providers built on a popular open-source serving engine. Four isn't bigger than 2.75 in any useful sense here, because they're measuring against different things.
So treat both as the vendor's own claim and nothing more. The honest split between these two isn't speed at all. It's scope: one is a GPU cloud and training shop that also serves models, and the other is built around running several open models well and showing you the quality bar each one has to clear.
Hardware you can rent versus software you can route
Together sells GPU clusters outright and names the hardware: GB300, GB200, B300, B200, H200 and H100. Next to that sit sandboxes and storage, and a Model Shaping line covering post-training, fine-tuning and evaluations. If your team wants to train or fine-tune on its own terms and then serve the result from the same provider, that's one supplier for the whole job.
Fireworks talks about software optimisation instead, with the line "your cloud or ours". There's no GPU cluster product on its home or inference pages. What it does sell is model ensembling, which is the routing of requests across several open models, plus training and a Fireworks Lab. For a business that already has compute, or doesn't want to think about it, that focus may be a relief rather than a gap.
How each one proves its output is right
Here's where Fireworks stands out. It publishes the quality limits a model has to pass before it serves: KL divergence below about 0.007 and token rejection below about 3%, both measured against an unquantised reference from the original lab. In plain terms, it's telling you how close its fast version stays to the model the lab released.
Together's approach is different. It points to research such as adaptive speculative decoding and says the speed comes with lossless quality. That's a reasonable claim, but it's stated rather than measured on the page in the way Fireworks lays out. If you sell into a sector where someone will ask you to show your quality checks, the published thresholds are easier to hand over.
Regions, media and the research behind them
Where your data goes
Fireworks says it offers one endpoint across 30+ regions with policy-aligned data residency. Together's main pages don't make a residency claim, so if this is a hard requirement for you, ask before you sign anything.
Text only, or images and video too
Together offers one API for text, image, video, code and voice, and dedicated container inference for generative media. If your product makes pictures, clips or voice, that's a point in its favour. Fireworks' home page keeps the focus on open model inference, routing and training for organisations running many models.
Who built the engine
Together features its own research, including FlashAttention, ATLAS and ThunderKittens. Fireworks says it's optimised by the creators of PyTorch and links a FireAttention study. Both have serious engineering pedigree. It won't decide the choice for most business owners, but it tells you each team builds its own serving technology rather than reselling someone else's.
Plain advice: which team should pick which
Pick Together AI if you want inference and raw GPU capacity under one roof, if you plan to fine-tune or post-train open models, or if your product generates images, video or voice. Pick Fireworks AI if you route traffic across several open models, want a published quality bar you can show customers, and need control over the region your data sits in. There's no fair overall winner on speed, because the two vendors measure against different baselines.
Want AI doing the heavy lifting in your marketing?
I build the systems that handle the boring 80 percent, so you get your week back. Done properly, with the human kept in.
If you'd like a wider field before deciding, our guide to the best AI inference platforms compares more providers on the same jobs, and it's worth a skim if you've already ruled one of these two out.
Three more inference providers worth a look
Groq
Best for: An inference cloud built on its own LPU chips alongside NVIDIA GPUs; aimed at teams where inference speed is the bottleneck.
Replicate
Best for: Developers who want a one-line API to run and fine-tune models, deploy custom ones, and call official models from OpenAI, Black Forest Labs, Google and others.
DeepInfra
Best for: Teams that want an inference API across text, image, speech, video and embeddings models, plus on-demand GPU rental.
Do you make an AI inference or GPU cloud platform? Tool makers can ask to be considered for a featured place on this page.
Want your tool on this list? Featured spots and reviews here are paid partnerships. Here's how to partner with me.
Frequently asked questions
Is Fireworks faster than Together?
You can't tell from their own claims. Together compares itself with the next fastest provider, while Fireworks compares its throughput with providers that wrap vLLM. Different baselines, so the multipliers don't line up.
Can I rent GPUs from Fireworks?
Its home and inference pages don't show a GPU cluster product. It leads with software optimisation and running on your cloud or its own. Together does sell GPU clusters.
Which one is better for image and video generation?
Together offers one API for text, image, video, code and voice, plus dedicated container inference for generative media. Fireworks keeps its focus on open model inference and routing.
Does either offer data residency?
Fireworks says it runs one endpoint across 30+ regions with policy-aligned data residency. Together's main pages don't make a residency claim, so check with its team if you need it.
Can I fine-tune models on both?
Yes. Together lists post-training, fine-tuning and evaluations under Model Shaping, and Fireworks lists training as one of its three pillars.
