Fireworks AI Inference vs Vercel AI Gateway

Fireworks AI Inference

Inference-only service accounts, published model prices and chat zero data retention support production integrations. Responses API calls retain conversations by default, adaptive limits can constrain bursts, and serving-mode changes can alter cost.

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.

Fireworks AI Inference

1. Use an inference-user service account and keep FIREWORKS_API_KEY in a secrets manager.
2. Use https://api.fireworks.ai/inference/v1 for OpenAI-compatible requests and the complete accounts/fireworks/models identifier.
3. Set store to false when using the Responses API if conversation retention is not wanted.
4. Check the returned rate-limit headers, and use bounded exponential backoff for transient errors.
5. Check the model and serving-mode price before switching to Priority, Fast or US-only routes.

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