Cerebras vs Groq
Side-by-side comparison of features, pricing, and ratings
At a glance
| Dimension | Cerebras | Groq |
|---|---|---|
| Pricing | Freemium | Freemium |
| Speed | 1,800+ tok/s (Gemma 4 31B), 2,000+ tok/s (Meta Scout) | Sub-200ms latency |
| Key Architecture | Wafer-Scale Engine (58x larger than GPU) | Language Processing Unit (LPU) |
| Notable Models | Gemma 4, Meta Scout | GPT-OSS, Kimi K2 |
| Extra Capabilities | Model training, Multi-LoRA | Compound AI, Orpheus TTS, Batch API |
| Integrations | AWS, HuggingFace, Vercel, LiveKit | OpenAI SDK, BrowserBase, Stripe, Tavily |
If you need raw token throughput for heavy agentic workloads and want the ability to train as well as infer on the same platform, Cerebras is your pick. If you prioritize sub-200ms latency, flexibility with open-source models, and a rich ecosystem of agentic tools, go with Groq. Both are fast, but they target different pain points.
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Cerebras is built on the Wafer-Scale Engine, which is 58 times larger than a GPU, delivering up to 15x faster inference than GPU systems. It supports multimodal models like Gemma 4 (over 1,800 tokens/sec) and Meta Scout (over 2,000 tokens/sec), making it ideal for real-time code agents and low-latency voice AI. Cerebras also offers model training and pre-training on the same platform, plus Multi-LoRA for efficient fine-tuning—features not highlighted for Groq. Its drop-in OpenAI API compatibility and partnerships (AWS, LiveKit) broaden its enterprise appeal. Groq, on the other hand, uses custom LPU silicon for sub-200ms inference, which is crucial for interactive apps. It provides day-zero support for open-source models like GPT-OSS and Kimi K2, and advanced features like Compound AI systems (web search, code execution, browser automation in one API call), Remote MCP server integration, Orpheus TTS for real-time speech, and Whisper ASR. Groq also offers a Batch API with 50% lower cost for async workloads and prompt caching that saves up to 50% on cached tokens. While Cerebras shines on raw speed and training capability, Groq excels in ecosystem breadth and developer convenience.
Pricing compared
Both Cerebras and Groq use a freemium model, but their commercial structures differ. Cerebras offers serverless API access, dedicated cloud endpoints, and on-prem deployment, but does not list granular pay-as-you-go rates; its 'not for' notes mention users needing open pricing should look elsewhere, implying custom enterprise agreements. Groq, however, advertises linear, predictable pricing with no idle infrastructure costs, and includes specific perks like Batch API at 50% lower cost and prompt caching for up to 50% savings. This makes Groq more transparent and cost-effective for variable workloads. Cerebras's cost-performance guarantees suggest it may be competitive at scale, but the lack of published rates makes it harder to compare directly. For startups or teams on a budget, Groq's predictable pricing and batch discounts are a bigger draw; for enterprises with heavy, consistent inference needs, Cerebras's performance may justify custom pricing.
Who should pick which
- Real-time code agent builderPick: Cerebras
Cerebras delivers over 2,000 tokens/sec on Meta Scout and 1,800+ on Gemma 4, enabling instant code generation without stalling—critical for agents that need sub-second responses.
- Voice AI developerPick: Groq
Groq's Orpheus TTS produces speech at 100+ chars/s with sub-200ms latency, making it ideal for real-time conversational voice applications.
- Enterprise with predictable cost needsPick: Groq
Groq offers linear, predictable pricing with batch discounts and prompt caching, way clearer than Cerebras's custom enterprise quotes.
- Multimodal AI researcherPick: Cerebras
Cerebras's support for multimodal models like Gemma 4 at high throughput, plus its training capabilities, suits research that needs both inference and fine-tuning.
- Agentic workflow orchestratorPick: Groq
Groq's Compound AI integrates web search, code execution, and browser automation in one call, plus Remote MCP, simplifying complex agent architectures.
Benchmarks
| Metric | Cerebras | Groq |
|---|---|---|
| Inference speed (tokens/second) | 2000+ tokens/secCerebras official claims | 1000 tokens/secGroq official claims |
| Latency (end-to-end) | <1 secondCerebras official claims | <0.1 secondGroq official claims |
| Speed improvement vs GPU | 15x timesCerebras official claims | 7.41x timesGroq official claims |
| Cost reduction vs GPU | N/A %Not claimed | 89 %Groq official claims |
Frequently Asked Questions
Cerebras vs Groq: which should you choose?
If you need raw token throughput for heavy agentic workloads and want the ability to train as well as infer on the same platform, Cerebras is your pick. If you prioritize sub-200ms latency, flexibility with open-source models, and a rich ecosystem of agentic tools, go with Groq. Both are fast, but they target different pain points.
Can I train models on Groq?
No, Groq focuses solely on inference; Cerebras supports both training and inference on the same platform.
Which platform has lower startup costs?
Groq is more transparent with freemium and linear pricing, while Cerebras may require custom enterprise agreements, making Groq easier to start with.
Does Cerebras support fine-tuning?
Yes, Cerebras offers Multi-LoRA for efficient fine-tuning, a feature not mentioned for Groq.
How do I integrate these APIs if I already use OpenAI?
Both are OpenAI-compatible; Groq switches in two lines of code, while Cerebras offers drop-in compatibility.
Which platform is better for global deployment?
Groq has global data centers for low-latency responses worldwide, while Cerebras is partnering internationally but less specified.
More Cerebras or Groq comparisons
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Last reviewed: August 3, 2026