Fireworks AI pricing: API cost per model, user, and month

Fireworks AI lists 16 priced models in the ByteCosts index. This provider page has 3 source-backed model rows, but no source-backed 70% input / 30% output blended chat row to quote, so ByteCosts does not make a lowest-price claim here. For most AI apps the bill is driven by output tokens, retry rate, and prompt-cache hit rate far more than the headline input price, so Fireworks AI is cost-effective when your workload is input-heavy (RAG, classification) or can cache a large shared prefix. Compare Fireworks AI against alternatives on a real workload - seats, requests, and token mix - before committing to a model or a subscription, because a cheaper per-token price can still lose once power users and long outputs are priced in.

Fireworks AI pricing at a glance

• 3 source-backed model rows on this provider page; no blended chat minimum is quoted without comparable input/output data
• Last recorded price event: Base Embeddings embedding on Jun 1, 2026
• 16 priced models in the index, updated July 19, 2026

Fireworks AI model prices

Per-million-token list prices for current Fireworks AI models, from the ByteCosts pricing index. Output tokens usually cost several times more than input.

When Fireworks AI is cost-effective

Token price alone does not decide cost. The variables that actually move an AI bill are: how many output tokens each call emits (output is the expensive side), how often calls retry, what fraction of the input prefix can be served from prompt cache, and how heavily your top 1% of users use the product.

Fireworks AI tends to win when your workload is input-heavy with short outputs, when you can cache a large shared system prompt or document context, or when a smaller Fireworks AI model clears your quality bar. It tends to lose when outputs are long and uncached, where a cheaper-per-output model compounds the saving across millions of calls.

Hidden costs to watch with Fireworks AI

List pricing rarely equals your invoice. Budget for these before you commit:

• Output-token blow-up: reasoning and verbose responses multiply the most expensive token class.
• Retries and tool loops: a single failed agent step can re-send the whole context.
• Cache misses: prompt caching only saves money above a break-even reuse count; cold prefixes pay the write premium.
• Power users: a small share of heavy users can dominate spend and erase a plan's margin.
• Egress and gateway fees: LLM gateways and observability add a percentage on top of raw model cost.

Limitations before production billing decisions

Treat ByteCosts calculations as planning estimates, not final billing totals. Real invoices can differ because token mix, retry rate, cache hit rate, rate limits, taxes, gateway fees, regional pricing, and negotiated discounts change the effective cost.

Verify the provider source before production billing decisions, then compare the estimate with your own logs or invoice once production traffic is live.

Frequently asked questions

Fireworks AI pricing. ByteCosts. Updated July 19, 2026. https://bytecosts.com/pricing/fireworks-ai/

Sources

• Fireworks AI official pricing
• Fireworks AI website

Machine-readable

• Markdown
• JSON reference
• CSV reference