Cerebras vs Groq
Side-by-side comparison of features, pricing, and ratings
Cerebras or Groq: which should you choose?
| 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.
Cerebras inference cloud runs frontier LLMs in Ultrafast mode at up to 750 tokens/sec for real-time agents and voice AI.
Visit WebsiteGroq delivers fast LLM inference on its own LPU and LPX silicon through an OpenAI-compatible API.
Visit WebsiteWhat do real users say about Cerebras vs Groq?
Not marketing copy and not our opinion — a structured sweep of public discussion (reviews, forums, communities and video comments), showing what people praise and what they complain about for each tool.
Cerebras
68 mentions across 5 sources · 63% positive — mixed (weighted across 5 sources)
Hacker News, YouTube, Product Hunt, Stack Overflow, Lemmy
What users praise
- • Fastest documented inference in the market — 750 tokens/sec on GPT-5.6 Sol Ultrafast
- • Drop-in OpenAI API compatibility means quick migration from existing OpenAI-based code
- • In-region inference satisfies data residency and compliance obligations that GPU clouds can't easily match
- • Open model catalog spans Qwen, Gemma 4, GLM, Llama, and trillion-parameter Kimi K2.6
What frustrates them
- • Narrower model catalog than GPU clouds — you can't run everything you might want
- • Inference-only positioning means training workloads still belong elsewhere
- • Community evidence on support responsiveness and uptime at scale is thin
- • Compared constantly to Groq on the same latency budget, with less differentiation
Researched Oct 9, 2026
Groq
93 mentions across 5 sources · 78% positive (averaged across 5 sources)
Hacker News, YouTube, Product Hunt, Stack Overflow, Lemmy
What users praise
- • Sub-200ms inference is consistently praised as the fastest in the industry.
- • Free API tier with no credit card is a major draw for developers.
- • OpenAI-compatible API allows migration in just two lines of code.
- • Day-zero support for new open-weight models like Llama 3.3 and Qwen.
What frustrates them
- • Model catalog limited to open-weight options; no GPT-4o or Claude.
- • Frequent 429 rate-limit errors in production, especially under load.
- • 'Tool use failed' errors with function calling can break agents.
- • Token limits can cause 'Request too large' errors for long prompts.
Researched Aug 18, 2026
Who should pick Cerebras, and who should pick Groq?
- 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.
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.
What else are Cerebras and Groq compared with?
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Analysis generated by our model pipeline from live data: