Cerebras vs Groq
A side-by-side comparison of Cerebras and Groq, two Inference tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | Cerebras | Groq |
|---|---|---|
| Category | Inference | Inference |
| Pricing | FREEMIUM | FREEMIUM |
| License | Proprietary | Proprietary |
| Deployment | Cloud | Cloud |
| Platforms (differs) | Web, API | API, Web |
| Model support | Multi-model | Multi-model |
| Vendor (differs) | Cerebras Systems | Groq |
| Capabilities (differs) |
|
|
The honest brief
Cerebras
Wafer-scale CS-3 hardware tops every rival on tokens/sec — fastest pure throughput for agent loops.
- Highest tokens/sec in the market
- Low time-to-first-token (~80-150ms)
- 2-3x faster end-to-end in agent loops
- OpenAI-compatible API, free daily tier
- Smaller model catalog than Groq/Together
- Less mature ecosystem and client libs
- Occasional capacity limits under demand
Groq
Custom LPU silicon delivers deterministic sub-100ms TTFT, ideal for voice and latency-critical apps.
- Hundreds of tokens/sec on open models
- Sub-100ms time-to-first-token
- Deterministic, low-variance latency
- OpenAI-compatible API with free tier
- Curated open-weight models only
- No frontier closed models (GPT/Claude)
- SRAM limits large context windows
- Rate limits during peak demand
When to pick which
Both cover Model inference / serving and Multi-model access.
Pick Cerebras if you need Fine-tuning / training.
- Fine-tuning / training (secondary capability)
Pick Groq if you need Transcription (STT) and Speech synthesis (TTS).
- Transcription (STT) (secondary capability)
- Speech synthesis (TTS) (secondary capability)
Groq leans on Multi-model access as a headline capability; Cerebras treats it as secondary.
- Multi-model access (primary capability)