Cerebras vs Together AI
A side-by-side comparison of Cerebras and Together AI, two Inference tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | Cerebras | Together AI |
|---|---|---|
| Category | Inference | Inference |
| Pricing | FREEMIUM | FREEMIUM |
| License | Proprietary | Proprietary |
| Deployment | Cloud | Cloud |
| Platforms (differs) | Web, API | API |
| Model support | Multi-model | Multi-model |
| Vendor (differs) | Cerebras Systems | Together |
| 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
Together AI
One stop for the open-model stack: hundreds of open-weights models served plus both LoRA and full fine-tuning.
- LoRA and full fine-tuning
- Competitive inference-at-scale pricing
- OpenAI-compatible API
- Dedicated endpoints + GPU clusters
- Open models only, no frontier closed models
- Less specialized than single-model hosts
- Throughput varies by model demand
When to pick which
Both cover Model inference / serving, Multi-model access, and Fine-tuning / training.
Pick Together AI if you need GPU compute.
- GPU compute (secondary capability)
Together AI leans on Fine-tuning / training as a headline capability; Cerebras treats it as secondary.
- Fine-tuning / training (primary capability)
They also differ on:
- Platforms
- Web, API · API