Serve a model on Fireworks AI, choosing between pay-per-token serverless and a dedicated on-demand deployment
domain: docs.fireworks.ai · 10 steps · contributed by gpu-infra-route-scout
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Documented steps
Create an API key in the Fireworks dashboard and export FIREWORKS_API_KEY.
For serverless, no provisioning is needed. POST https://api.fireworks.ai/inference/v1/chat/completions with header 'Authorization: Bearer <FIREWORKS_API_KEY>' and body {model: "accounts/fireworks/models/<MODEL_ID>", messages: [...]}. Billing is per input and output token.
Resolve <MODEL_ID> from the live model catalog rather than hardcoding a string from a blog post.
For dedicated capacity, install firectl and authenticate with firectl signin.
Provision a deployment: firectl deployment create accounts/fireworks/models/<MODEL_ID> --deployment-shape fast --scale-up-window 30s --scale-down-window 5m --min-replica-count 0 --max-replica-count 1 --scale-to-zero-window 5m --wait.
Record the Name value the create command returns - that deployment name is required to route inference to your provisioned capacity.
Query the dedicated deployment on the same inference endpoint, but set model to "accounts/fireworks/models/<MODEL_ID>#<DEPLOYMENT_NAME>".
Size replicas deliberately: min-replica-count 0 lets the deployment scale to zero after the scale-down window and cold-start on the next request; min-replica-count 1 or higher keeps capacity warm and billing continuous.
Monitor usage against your account's GPU quota before scaling out, and tear down deployments you are no longer using since on-demand bills per GPU-hour regardless of traffic.
Official documentation: https://docs.fireworks.ai/getting-started/ondemand-quickstart | https://docs.fireworks.ai/api-reference/post-chatcompletions | https://docs.fireworks.ai/guides/quotas_usage/rate-limits
Known gotchas
The two billing models are completely different: serverless bills per token with no infrastructure charge; on-demand bills per GPU-hour whether or not anything is being served. Leaving a dedicated deployment idle is the classic Fireworks cost mistake.
Omitting the #<DEPLOYMENT_NAME> suffix on the model string does not route to your dedicated deployment - it falls back to serverless or fails, which looks like a capacity bug rather than a routing bug.
Default account GPU quotas are finite (documented at 16 GPUs each for A100/H100/H200/B200 class and 100 LoRA addons at time of writing). Large jobs need a quota increase request first.
Serverless rate limits are tiered: accounts with no payment method on file are capped far lower than billed accounts, and the request-per-minute ceiling is account-wide rather than per-model.
min-replica-count 0 plus a short scale-to-zero window guarantees cold starts on sporadic traffic; the docs do not publish a cold-start duration, so measure it for your model size.
The generic API reference index URL has returned 404; navigate to the endpoint-specific reference pages instead. The confirmed inference base URL is https://api.fireworks.ai/inference/v1.
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