High-speed AI inference
How to use
Cerebras Inference.
Latency-sensitive language-model applications that need very fast token generation through a hosted API.
Cerebras Inference is a hosted high-speed inference API for supported language models. Latency-sensitive language-model applications that need very fast token generation through a hosted API. This guide covers the whole path in one place: official access, a first session that produces something reviewable, the checks that make output trustworthy, and the permissions worth limiting before you connect real work.
AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.
Access & setup
Find, install, and sign in to Cerebras Inference
Get into the official Cerebras Inference experience with the right account and a setup you understand.
- 01
Start at https://www.cerebras.ai/inference and confirm the domain before entering account or payment information.
- 02
Availability: Cerebras Inference is accessed through its developer console, playground, HTTP API, and official Python and JavaScript libraries.
- 03
A Cerebras account and API key, network access, and Python 3.7+, TypeScript 4.5+, or another compatible HTTP environment.
- 04
Sign in with the account you intend to keep using, then review plan, data, notification, and permission settings.
- 05
Run one low-risk test task before connecting sensitive files, repositories, or workspace data.
- Use official download pages.
- Review permissions during setup.
- Keep installers and applications updated.
First session
Your first useful Cerebras Inference session
Learn the interaction loop using a small task with a clear outcome.
- 01
Create a development API key and store it in an environment secret rather than code or browser storage.
- 02
Choose a current production model after reviewing capability, context, rate limits, and announced deprecations.
- 03
Run a fixed set of representative prompts and record quality, time to first token, total latency, and token use.
- 04
Test limits, errors, retries, and structured-output validation before connecting any downstream action.
- State the outcome before the background.
- Provide the real source material.
- Review the result before expanding the task.
Quality control
Check the quality of Cerebras Inference output
Establish that a model's output is reliable enough for the job you are handing it.
- 01
Restate the task, the acceptance criteria, and the failure cost before evaluating any response.
- 02
Test with a fixed evaluation set rather than ad-hoc prompts, so changes are measurable across model versions.
- 03
Check determinism and drift: record model version, parameters, and date, because providers update models underneath you.
- 04
Probe the failure modes deliberately — ambiguous inputs, adversarial phrasing, and out-of-scope requests.
- 05
Validate structure as well as content: confirm schemas, types, and required fields survive real inputs.
- Pin and record the model version with every result.
- Evaluate against a fixed set, not one-off prompts.
- Validate output structure before it reaches downstream code.
Privacy & permissions
Use Cerebras Inference safely
Understand what leaves your system on every call, and what the provider keeps.
- 01
Classify the data in each request before it is sent to a hosted model.
- 02
Check the provider's retention and training terms for your specific plan, not the marketing page.
- 03
Store API keys in a secret manager, scope them per project, and rotate them on a schedule.
- 04
Strip or tokenise personal and regulated data before it reaches the request body.
- 05
Set rate limits, spend caps, and monitoring so a runaway loop is bounded and visible.
- High token speed does not guarantee correct or safe output. Protect keys, distinguish preview and production models, track deprecations, validate responses, bound tokens and retries, and monitor organization-level limits and spend.
- Follow your organisation's approved-use policy.
- Never treat fluent output as authorization to act.
Core workflows
Step-by-step ways to use Cerebras Inference for the work it does best.
Each workflow below has its own outcome, steps and review checkpoints.
Measure whether fast token generation also meets task quality, context, reliability, and cost requirements.
↓ 02 WorkflowPrepare a Cerebras API integration for productionSecure credentials and add structured output checks, limits, observability, and predictable failure handling.
↓Workflow 01
Evaluate a high-speed model on Cerebras Inference
Measure whether fast token generation also meets task quality, context, reliability, and cost requirements.
Outcome: Select a current production model using repeatable evidence rather than throughput alone.
- 01
Define representative prompts, expected outputs, safety cases, context lengths, latency targets, and a scoring rubric.
- 02
Review the current supported-model list and separate production offerings from previews or models with announced deprecation dates.
- 03
Run identical tests with fixed parameters and record accuracy, time to first token, total latency, token use, refusals, and errors.
- 04
Repeat at realistic concurrency and input sizes while monitoring organization rate-limit headers and bounded retry behavior.
- 05
Pin the selected model ID, document compatible fallbacks and thresholds, and rerun the benchmark before any model migration.
What good use looks like
- Evaluate production models separately from previews.
- Measure end-to-end latency.
- Track announced deprecations.
Workflow 02
Prepare a Cerebras API integration for production
Secure credentials and add structured output checks, limits, observability, and predictable failure handling.
Outcome: Operate a Cerebras-backed feature without exposing keys or allowing fast failures to propagate downstream.
- 01
Create environment-specific API keys, store them in a server-side secret manager, and define rotation and revocation ownership.
- 02
Set explicit request timeouts, completion-token bounds, concurrency limits, and cost budgets based on the current account tier.
- 03
Validate user input and generated structure, restrict downstream tools, and reject unsupported or unsafe output before action.
- 04
Test 429, timeout, 5xx, invalid model, long context, streaming interruption, and fallback behavior with bounded exponential backoff.
- 05
Monitor model ID, latency, tokens, status, remaining limits, and spend without logging sensitive prompts or credentials.
What good use looks like
- Keep keys out of client applications.
- Bound maximum output tokens.
- Make failures visible and non-destructive.
Official references
Check the current product documentation.
Features, plan limits, availability, and data controls change. These official pages are the starting points used for this guide.