Cerebras Inference vs Vercel AI Gateway

Cerebras Inference

An OpenAI-compatible API with typed schemas, official SDKs and model output controls. Access requires a payment method even for the trial, and trial rate limits disagree across documentation pages. Shared-model availability and dedicated-model availability differ.

Vercel AI Gateway

Token prices pass through with no markup, prompts are not stored by the gateway, and budgets, provider failover and per-request logs are built in. The free monthly credit needs a card on file, rate limits are not published as numbers, no SLA covers the API, and the contractual promise not to train on content applies to Enterprise subscriptions only.

Cerebras Inference

1. Set CEREBRAS_API_KEY and use https://api.cerebras.ai/v1 with an OpenAI-compatible client.
2. Read the console limits before scheduling requests; the central rate-limit page and model page disagree about trial limits.
3. Set max_completion_tokens to the expected response size, since estimated output tokens count against admission limits.
4. Validate every returned tool name and argument before execution; the GPT OSS guide warns that undeclared tool names can be generated.

Vercel AI Gateway

1. Read GET https://ai-gateway.vercel.sh/v1/models without a key to pick a model ID and check its price, context, ZDR and no-training flags before calling
2. Set zeroDataRetention or disallowPromptTraining to true in providerOptions.gateway on sensitive requests. Default routing ignores provider retention and training policies
3. Treat a 429 as retryable and honour retry-after when present. A 402 with quota_for_entity_exceeded is a spent budget, so do not retry it
4. On a Token Pass key, a 403 with no_providers_available means spent credit or a model outside the pass. Retry with a standard AI Gateway key, which bills credits
5. Set inferenceRegion when data residency matters. The default global may run inference outside the user's jurisdiction