Choose Cerebras if you want to try fast inference straight away through an OpenAI-compatible API, a stated free trial and a named list of models. Choose Groq if you're scoping dedicated capacity at larger scale and care about tiered infrastructure and a spread of 13 data centres. Neither is a clear winner, and the speed claims on both sides come from the vendors themselves.
A developer with a weekend project and a head of infrastructure planning long-term capacity will read this pair very differently, and they'll probably land on different answers. That's most of the story here, so it's worth deciding which of those two people you are before you go any further.
I'll call them the LPU cloud and the wafer-scale cloud from here on, after the chips each one is built around.
A repositioning that changes the answer
The LPU cloud has moved upmarket. Its home page now calls it a neocloud and sells three layers: GroqMetal for infrastructure, GroqCore for inference and GroqAssured for enterprise controls. It talks about racks that work alongside NVIDIA GPUs rather than about a quick developer API. There's a Start building button, but its site says little about models or how you'd call it.
The wafer-scale cloud still leads with the developer. It states a free trial, says you can get started in under 30 seconds, and offers drop-in OpenAI API compatibility so you change two lines of code. For a hobby project or a quick proof of concept, it's the easier one to start on from what each site shows.
Short advice for each reader
If you're the developer, start with the wafer-scale cloud: the OpenAI-compatible API, the free trial and the named models mean you can be calling it this afternoon. If you're the infrastructure lead scoping dedicated capacity, the LPU cloud's tiers and data centre spread are built for your conversation, and you'll want a call with its sales team. This rests on what each site says, not on measured speed.
The pair in one table
| Area | Groq | Cerebras |
|---|---|---|
| Chip | Its own LPU accelerators, 256 per rack, with on-chip SRAM, now alongside NVIDIA GPUs | A single wafer-scale chip it says is 58x larger than GPUs |
| How it's sold | Three tiers for infrastructure, inference and enterprise controls | Self-serve cloud with free trial, developer and enterprise tiers, plus on-premise |
| Getting started | Start building button; onboarding steps not described | Under 30 seconds with drop-in OpenAI API compatibility (vendor claim) |
| Models | No model catalogue on its site | Kimi K3.1, Qwen 3.8 27B and GPT 5.6 Sol Ultrafast named, with a catalogue |
| Footprint | 13 data centres across four continents | In-region inference for data residency; coming to Oracle Cloud Infrastructure |
| Scope | Inference only | Train, fine-tune and serve on one platform |
Two very different chips
The LPU cloud runs on its own LPU accelerators, 256 to a rack, with on-chip SRAM. Its LPX racks now pair with NVIDIA hardware, and the vendor says they'll work with NVIDIA Vera Rubin when it's released. It claims 1,000 tokens per second per user on those racks.
The wafer-scale cloud takes the opposite route: one very large chip, which it says is 58x larger than GPUs, and a claim of up to 30x faster inference than GPUs. Neither claim is something you can check from a website, so treat both as a reason to run your own workload on each before you commit to anything long term.
Where your prompts get processed
The LPU cloud lists 13 data centres across four continents, including the US, Canada, Finland, the UK, Australia and Saudi Arabia. The wafer-scale cloud says its inference runs in region to meet data residency and compliance needs, and that it's coming to Oracle Cloud Infrastructure.
If your legal or security team has views on where customer data is processed, ask both vendors for the specific region you'd be served from. Don't assume a list on a home page covers your contract.
Inference only, or the whole model lifecycle?
The LPU cloud keeps its message narrow: inference is the bottleneck, and that's the job it sells. Its site doesn't show a model catalogue, so you'd need to ask which models it serves.
The wafer-scale cloud pitches training, fine-tuning and serving on one platform, plus on-premise deployment. Its home page names models including Kimi K3.1, Qwen 3.8 27B and GPT 5.6 Sol Ultrafast, with a link to a fuller catalogue. If you want to fine-tune a model and then serve it from the same place, that's a clear point in its favour. For a wider look at who else does this, see my list of the best AI inference platforms for production apps.
Three more fast inference providers
If you'd like a broader field before you decide, these three are the closest rivals. If you're mainly after a single endpoint that routes to many models, my page on OpenRouter alternatives covers that angle.
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SambaNova
Best for: Enterprises and sovereign AI projects after SambaCloud, SambaStack or SambaRack hardware, all built on its own RDU dataflow chip.
Together AI
Best for: Serverless, batch and dedicated inference, GPU clusters, fine-tuning and a model library in one AI cloud, plus open source research such as FlashAttention.
Fireworks AI
Best for: Companies that run, ensemble and train open models, with inference across 30+ regions and a vendor claim of up to 4x the throughput of vLLM wrappers.
Do you make an AI inference cloud or API? Tool makers can ask to be considered for a featured place on this page.
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Frequently asked questions
Which of the two is faster?
Both claim big speed gains. One says 1,000 tokens per second per user on its LPX racks; the other says up to 30x faster than GPUs. Run your own workload before trusting either claim.
Which one is OpenAI API compatible?
The wafer-scale cloud says it offers drop-in OpenAI API compatibility, so you change two lines of code. The LPU cloud's site doesn't describe its API.
Can I fine-tune models on either?
The wafer-scale cloud pitches training, fine-tuning and serving on one platform. The LPU cloud is pitched as inference only.
Is there a free trial?
The wafer-scale cloud states a free trial. The LPU cloud's site shows a Start building button but doesn't say what's included.
Where are their data centres?
The LPU cloud lists 13 across four continents, including the UK and Finland. The wafer-scale cloud says it runs inference in region for data residency and is coming to Oracle Cloud Infrastructure.
