Loading page
Cerebras
Fast LLM inference on wafer-scale hardware
Inference specialist🇺🇸 USinferencellmfast
Last reviewed Sep 5, 2026
Cerebras Systems builds wafer-scale AI processors and runs Cerebras Inference, a token-billed LLM API served on its own CS-3 systems rather than GPUs. The public endpoints expose a small catalog of open-weight models through an OpenAI-compatible API, with private dedicated endpoints available for reserved capacity and additional model families.
We're actively tracking prices for Cerebras. Check back soon, or browse other providers with current pricing.
Pros & Cons
Advantages
- Published throughput in the thousands of tokens per second on public models
- Drop-in OpenAI-compatible API with first-party Python and TypeScript SDKs
- Serves original, unpruned open-weight models with a stated no-modification policy
- Free trial credits on signup, then self-serve pay-as-you-go
- Per-model documentation lists context, output limits, and rate limits per tier
Limitations
- Very small public model catalog compared with GPU-based inference providers
- Free-tier context windows are roughly half the paid-tier limits
- Preview models can be withdrawn and are not intended for production use
- Larger model families require a dedicated endpoint quoted through sales
- Inference only: no GPU rental, training, or self-serve fine-tuning
Key Features
Wafer-Scale Inference
Models run on the Cerebras Wafer-Scale Engine, which keeps model weights in on-chip SRAM to reach published speeds in the thousands of tokens per second
OpenAI-Compatible API
Chat Completions and Completions endpoints at api.cerebras.ai/v1, usable from the OpenAI SDKs or the Cerebras Python and TypeScript SDKs
Open-Weight Model Catalog
Public endpoints serve unmodified open-weight models such as OpenAI GPT OSS and Qwen, with a documented policy of no pruning on hosted models
Reasoning and Tool Calling
Configurable reasoning effort, streaming, structured outputs, parallel tool calling, and prompt caching across the catalog
Image Inputs
Vision-capable models accept PNG and JPEG images alongside text
Dedicated Endpoints
Private, provisioned endpoints on reserved capacity with fine-tuning, weight management, and additional model families, arranged through sales
Cloud Console and Playground
Web console for API keys, usage and cost monitoring by model, and an interactive playground
Pricing Options
| Option | Details |
|---|---|
| Free Trial | Time-limited credits granted on signup, with lower rate limits and context windows than paid tiers |
| Developer (Pay as You Go) | Prepaid credits billed per million input and output tokens at published per-model rates |
| Enterprise and Dedicated Endpoints | Reserved capacity, higher rate limits, and additional models on custom terms through sales |
Availability & Support
Regions
Cerebras-operated data centers in North America, with expansion into Europe; no region selection on the public API
Support
Documentation and API reference, model-specific guides, Cloud Console usage monitoring, Discord community, and enterprise support for dedicated customers
Getting Started
- 1
Create an account
Sign up at cloud.cerebras.ai and add a payment method to activate the free trial
- 2
Get an API key
Generate a key from the Cloud Console
- 3
Install an SDK
pip install cerebras_cloud_sdk or npm install @cerebras/cerebras_cloud_sdk, or use the OpenAI SDK pointed at api.cerebras.ai/v1
- 4
Make a request
Call chat completions with a model ID from the catalog, such as gpt-oss-120b
Frequently Asked Questions
Which models does Cerebras serve?
Check the pricing table above for the models Cerebras currently serves and their per-token rates.
How do I get started with Cerebras?
Create an account, Get an API key, Install an SDK, Make a request
What are Cerebras's main advantages?
Cerebras's main advantages include: Published throughput in the thousands of tokens per second on public models, Drop-in OpenAI-compatible API with first-party Python and TypeScript SDKs, Serves original, unpruned open-weight models with a stated no-modification policy, Free trial credits on signup, then self-serve pay-as-you-go, Per-model documentation lists context, output limits, and rate limits per tier.
What are Cerebras's limitations?
Cerebras's main limitations include: Very small public model catalog compared with GPU-based inference providers, Free-tier context windows are roughly half the paid-tier limits, Preview models can be withdrawn and are not intended for production use, Larger model families require a dedicated endpoint quoted through sales, Inference only: no GPU rental, training, or self-serve fine-tuning.