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Fireworks AI−17 Mixed

GILD Review

GILD's call and the buying facts behind it.

From GILD research

Should we adopt Fireworks AI?

Which Fireworks AI workloads belong on shared Serverless, and which need dedicated GPUs?

  1. 30-Second TakePick Fireworks AI if you serve open models per token and want Zero Data Retention by default.
  2. Best fitProduct and platform teams that serve open models through an OpenAI-compatible API, pay per token on Serverless, and want Zero Data Retention by default for open models.GILD Review
  3. Consider another workflowIf your team needs a wider catalog, a developer on a small team found fewer models on Fireworks than on OpenRouter (July 30, 2026). Azure customers can buy Fireworks inside Microsoft Foundry, billed through Azure and counted toward their Microsoft Azure Consumption Commitment.GILD Review

Drawn from 76 sources and 3 operator insights

GILD analysis

Fireworks AI Review

Practitioners pick Fireworks AI for low per-token bills on open models and report a price rise, costly cached tokens and speed that varies by model and day. Serverless is multi-tenant and billed per token from prepaid credits, and on-demand deployments reserve dedicated GPUs billed per GPU second. Adopt it with Auto Reload on, retries for 429s and 503s, and reserved capacity before a new model takes production traffic.

  • Inference Hosting
  • AI Costs

The decision

Best fit
Product and platform teams that serve open models through an OpenAI-compatible API, pay per token on Serverless, and want Zero Data Retention by default for open models.
Consider another workflow
If your team needs a wider catalog, a developer on a small team found fewer models on Fireworks than on OpenRouter (July 30, 2026). Azure customers can buy Fireworks inside Microsoft Foundry, billed through Azure and counted toward their Microsoft Azure Consumption Commitment.
Adoption condition
Auto Reload on, retries for 429s and 503s, reserved capacity and a fallback deployment before a new model carries production traffic, and an Enterprise account if you need custom SSO, SCIM, audit logs or data residency.

Basis: Fireworks AI's pricing page and documentation stored on November 1, 2026, three named deployment accounts (Cursor, Sourcegraph and GitLab), and 47 public accounts from people using Fireworks. Not a GILD performance benchmark.

Buying & deployment factsWhat a team building on it should verify before production traffic grows.
How you buy it

Per token or GPU second

Serverless draws on prepaid credits, and usage pauses at a zero balance unless Auto Reload is on.

Source 2, Fireworks AI, Billing FAQ: Billing FAQSource 1, Fireworks AI, Pricing: PricingSource 8, Fireworks AI, Cost FAQ: Cost FAQ

Details

Fireworks Serverless is pay per token with pre-paid, usage-based billing. On demand deployments are pay per GPU second. Fireworks bills pre-paid credits, and if your balance reaches zero and Auto Reload is not enabled, usage pauses until you add credits, so usage cannot exceed the balance. Contracted customers may have the option to move to post-paid billing.

Cost drivers

US$8.00 per H100 hour

Batch runs at 50% of serverless prices, and region-restricted deployments cost 1.5x.

Source 11, docs.fireworks.ai, Serverless pricing: Serverless pricingSource 1, Fireworks AI, Pricing: PricingSource 12, Fireworks AI, Pricing table: Pricing tableSource 13, Fireworks AI, Batch: BatchSource 14, Fireworks AI, Introduction: Introduction

Details

Serverless text and vision prices are listed per 1 million tokens in US dollars, for input, cached input, and output. Batch inference is billed at 50% of serverless pricing on both input and output. On demand H100 80 GB GPUs are listed at US$8.00 per hour. Region-restricted deployments are priced at a 1.5x premium. Contact Fireworks sales to get started.

Where it runs

Shared or reserved GPUs

US-only Serverless serves inference from the US on its listed US model IDs.

Source 16, docs.fireworks.ai, US-only Serverless: US-only ServerlessSource 15, Fireworks AI, Latency FAQ: Latency FAQSource 17, Fireworks AI, Deployments: Deployments

Details

With serverless, users are sharing a pool of GPUs with models pre-provisioned. With on-demand deployments, users are reserving GPUs. US-only Serverless serves inference exclusively from the US. To use it, call https://us.api.fireworks.ai and pass one of the US model IDs as model.

What to verify

TPM limits per model

Serverless limits are tokens per minute per model, and high traffic adds latency variability.

Source 24, docs.fireworks.ai, Rate limits: Rate limitsSource 15, Fireworks AI, Latency FAQ: Latency FAQSource 28, Fireworks AI, Error codes: Error codes

Details

Fireworks serverless limits are measured and enforced in tokens per minute per model, with separate total prompt, uncached prompt, and generated metrics. Fireworks says high traffic can cause minor latency and performance variability on serverless.

Sourced from Fireworks AI documentation