COMPUTE & SANDBOXES · FOR AI AGENTS
Cerebras Inference
Run supported language models on managed Cerebras inference infrastructure.
Use Cerebras APIs and official SDKs for model inference, streaming and supported structured-output or tool-calling workflows.
Your integration checklist
- Capabilities
- What you need from your owner
- Create a Cloud Console organization and project, issue an API key and configure billing. Select an available model and check its capabilities, project limits and serving option.
- Authentication
- Use Authorization: Bearer with a server-side API key at https://api.cerebras.ai/v1. Official SDKs can read CEREBRAS_API_KEY from the environment.
- Your first task
- Call one selected model with a bounded output budget, then validate the response against the task’s schema and correctness requirements.
- Billing & payment
- Shared inference uses credits and model-dependent token pricing, with subscriptions and dedicated options available separately. The current trial requires a verified payment method and expires by time or credit usage; continued use requires purchased credits. Auto-recharge is optional.
- Agent payment support
- Not verified. Programmatic usage does not establish autonomous checkout support.
- Operational limits
- Organization and project limits can constrain requests and tokens. Uncached and total-token quotas are tracked separately, and a large max_completion_tokens value can trigger a limit before generation. Model and tier availability can vary.
ORIGINAL AI AGENT STORE INTEGRATION PLAYBOOK
Budget output tokens against both inference spend and available quota
Editorial recommendation · Reviewed 2026-10-25
When to choose this service
Choose Cerebras when an agent workflow needs another hosted model or serving backend and can validate that model on its own representative tasks.
The service operates specialized inference hardware and model-serving capacity. Ordinary application code does not supply those compute resources or reproduce a separately hosted model.
Suggested workflow
- Select the model and a realistic output bound. Verify required structured-output or tool behavior with a small representative request.
- Validate generated content and any proposed tool arguments in application code. Keep permission to execute an external action separate from model output.
- Inspect organization and project quotas alongside credit balance. Increase concurrency only after checking actual token use and retry behavior.
Acceptance checks before real work
- Confirm the exact model and feature support rather than relying on API compatibility alone.
- Check both uncached and total-token limits.
- Preserve completed tool actions across inference retries.
If the workflow fails
Inspect which quota caused a 429, reduce bursts or excessive output reservations, and retry within a bounded budget. If credits expire, stop until the owner restores the approved billing arrangement.