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Cerebras

AI inference on wafer-scale chips — 1000+ tokens/second

  • Free plan available
  • No credit card
Cerebras screenshot

What is Cerebras?

Cerebras is a cloud-based AI inference platform built on proprietary wafer-scale chip technology designed to deliver exceptionally fast token generation, often exceeding 1000 tokens per second. Rather than relying on traditional GPU clusters, the service uses a unified chip architecture that minimises latency, making it particularly suited for real-time applications where response speed matters. The platform operates on a freemium model, allowing users to experiment with the free tier before paying for production workloads. Access is provided via a simple web interface and API, so developers can integrate Cerebras inference into applications without managing hardware infrastructure. The service appeals to organisations building latency-sensitive features like live chatbots or translation tools, researchers prototyping models quickly, and cost-conscious teams that want high-speed inference without large capital expenditure. It competes on speed and efficiency rather than breadth of models.

Key features

Wafer-scale chip architecture delivering 1000+ tokens per second inference

Cloud-based access with no hardware setup or maintenance required

RESTful API for straightforward application integration

Free tier for testing and small-scale use

Low-latency response times suitable for interactive applications

Support for popular open-source and proprietary language models

Scalable infrastructure that adjusts to variable workload demands

Pros & cons

Advantages

  • Extremely fast token generation reduces latency in real-time applications
  • No hardware investment needed; purely cloud-based service
  • Free tier enables experimentation without committing budget
  • Proprietary chip design offers performance advantages over standard GPU inference
  • Simple API makes integration into existing projects straightforward
  • Particularly valuable for use cases where sub-second response times are essential

Limitations

  • Smaller model library compared to established providers like OpenAI or Anthropic
  • Fewer integrations and third-party tools available in the ecosystem
  • Newer company with smaller community and less extensive documentation
  • May have capacity constraints during periods of high demand
  • Pricing at scale could become costly for very high-volume production use
  • Less mature track record for evaluating long-term reliability and service stability

Use cases

Real-time chatbots and conversational AI that require immediate user responses

Interactive content generation tools where latency affects user experience

High-throughput inference on large batches of documents or data

Live translation, transcription, or customer support automation

Rapid model prototyping and iteration for research teams

Cost-effective inference for startups and small teams with modest budgets

Ready to try Cerebras?

Pricing

Free

Free

Limited monthly inference quota, access to core models, suitable for learning and small projects

Pay-As-You-Go

Based on tokens generated

Flexible per-token pricing, no minimum commitment, scales directly with usage

Enterprise

Contact sales

Custom pricing, dedicated support, guaranteed throughput, service level agreements

Get started with Cerebras

Click through to Cerebras and start using it now.

  • Free plan available
  • No credit card