Pick Cerebras if you're a developer or product team who wants very fast generation through an OpenAI-compatible API, with a free trial to start. Pick SambaNova if you run a data centre, a neocloud or a sovereign AI programme and want racks and software to own, or you need to bring your own model checkpoints. Neither wins overall on shared evidence.
There are two ways to use an AI chip company. You can call its cloud from your code and ignore the hardware entirely, or you can bring its hardware and software into your own building. Both of these firms will happily sell you the first. Only one of them puts the second at the centre of its pitch, and that's what this decision really comes down to.
I'll call them the wafer-scale firm and the RDU firm from here, after the chips each one is built on, because it keeps the comparison easy to follow.
Six differences in one table
| Area | Cerebras | SambaNova |
|---|---|---|
| Chip design | Wafer-scale engine, described as 58x larger than GPUs | RDU chip with dataflow processing and three-tier memory |
| How you buy | Inference cloud with developer and enterprise tiers, plus the CS-4 rack for hyperscale | SambaCloud API, SambaStack software, SambaOrchestrator and SambaRack hardware |
| API | OpenAI-compatible, with two code changes to start (vendor claim) | OpenAI-compatible; quickstart shows a bearer key and a chat completions call |
| Headline pitch | Raw speed, such as 2,000 tokens per second on one model (vendor claim) | The infrastructure behind inference, sold to operators as well as developers |
| Models shown | Kimi K3.1, Qwen 3.8 27B, GPT 5.6 Sol Ultrafast, Codex-Spark | GLM-5.3, Llama 3.3 70B, MiniMax M3.1 Flash, gemma-4-31B-it, gpt-oss-120b |
| Who it targets | Builders and enterprises, with quotes from OpenAI, Notion, GSK and Mayo Clinic | Developers, plus sovereign AI, neoclouds and the US public sector; bring your own checkpoints |
| Getting started | Free trial stated | Not mentioned |
Two claims to be fastest, two different products
Here's what most quick comparisons miss. Both vendors call themselves the fastest inference platform, yet they're selling quite different things.
The wafer-scale firm leads with tokens per second and the models running on its hardware. That includes partner models, such as an OpenAI model offered in an Ultrafast mode, and the vendor mentions an Oracle Cloud Infrastructure option on the way. Its site also says the CS-4 rack delivers up to 30x faster inference than GPUs. That's the vendor's own claim, and like all such claims it depends on the workload.
The RDU firm says a lot less about one headline speed number and a lot more about the stack underneath. Much of its site is about selling racks, software and orchestration to data centres, neoclouds and whole countries building sovereign AI. So it's as much an infrastructure supplier as an API provider. If you only want an endpoint, you'll use a small slice of what it offers.
One more thing worth knowing: both say their speed gains come from vendor or third-party testing that varies by workload. There's no shared benchmark across the two sites, so neither set of numbers settles anything on its own.
What you can run on each today
The model lists don't overlap much, and that may decide it for you faster than any chip debate. The wafer-scale firm's home page lists Kimi K3.1, Qwen 3.8 27B, GPT 5.6 Sol Ultrafast and Codex-Spark, which leans towards coding and agent work. The RDU firm's cloud lists GLM-5.3, Llama 3.3 70B, MiniMax M3.1 Flash, gemma-4-31B-it and gpt-oss-120b, a broader spread of open-weight models.
The RDU side also offers bring your own checkpoints. If you've already tuned a model and want it served on someone else's hardware, that's a real reason to look closer. If you haven't tuned one yet, this feature won't matter to you for now.
Switching code between them
The good news is that both expose an OpenAI-compatible API. The wafer-scale firm says it takes two code changes to start, and the RDU firm's developer quickstart shows a bearer key and a standard chat completions call. In practice that means you can build against one shape and try the other without rewriting your app. It's a sensible way to hedge, since neither vendor's numbers have been compared head to head.
If you're still weighing up why special chips matter at all, our explainer on CPU vs GPU covers the basics without the jargon.
Other inference providers to compare
Groq
Best for: Teams buying inference capacity at scale on its LPU chip, which it now pairs with NVIDIA GPUs through LPX.
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.
Together AI
Best for: Developers who'd like serverless, batch and dedicated inference alongside GPU clusters, fine-tuning and post-training from one AI cloud.
Fireworks AI
Best for: Companies building specialised models on open weights, with serverless inference, model training and data residency options in one platform.
My advice for builders and for operators
If you're building an app where response time is the whole experience, think coding assistants, voice agents or chains of agent calls, start with Cerebras. It's got a free trial, an OpenAI-compatible API and a model list tilted towards that kind of work. If you run infrastructure, a data centre, a neocloud or a national AI programme, or you need your own checkpoints served, SambaNova is built far more around you. For everyone else, the honest answer is to point the same prompts at both and judge on your own workload, because no shared benchmark exists on either site.
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Frequently asked questions
Are both APIs OpenAI-compatible?
Yes. The wafer-scale firm says you can start with two code changes, and the RDU firm's quickstart uses a bearer key and a standard chat completions call.
Can I bring my own model checkpoints?
The RDU firm says it supports bring your own checkpoints. The other side's pages focus on the models it already hosts and its partner models.
Which one is faster?
Both claim to be fastest, and both say their results depend on workload and come from vendor or third-party testing. Neither site gives a shared benchmark, so run your own prompts on each.
Can I buy the hardware rather than use the cloud?
Both sell racks. The wafer-scale firm has the CS-4 for hyperscale deployment, and the RDU firm sells SambaRack with SambaStack software and SambaOrchestrator for data centres.
Is there a free trial?
The wafer-scale firm states a free trial for its inference cloud. The RDU firm's site doesn't state one.
