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Fireworks AI pricing and limits

iScore41–60 · ≈#7/10
fireworks.ai · LLM APIs
SEPT 2026 audit

Serverless inference for open-weight models from other labs, priced per token, with fine-tuning and dedicated GPUs billed separately.

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RegionsMulti-regions GLOBAL, US, EUROPE and APAC, plus 26 named Fireworks-managed single regions (US, Canada, Frankfurt, Iceland, Tokyo, New South Wales, Malaysia) for dedicated deployments. Only GLOBAL has quota by default; other placements must be granted by Fireworks.
Free tierNo
CategoryLLM APIs

Reviewed by Palash BagchiUpdated 24 criteria comparedSources last checked

01.Evaluation guide

Who it suits

Running open-weight models without operating GPUs — the same DeepSeek and Qwen checkpoints the labs publish, served per token, with a path to dedicated hardware if throughput demands it.

Consider something else if…

You want a frontier proprietary model: Fireworks serves other people's open weights, so the ceiling is whatever the open ecosystem has released.

Editorial · Palash Bagchi · approved

Machine-derived signals

Not sourced facts — counts and matches read by a script, not a person. See each line for where it comes from.

Fireworks AI's own site (fireworks.ai) self-detects as: hosting — Indian hosting, Cloud PaaS, Cloud Hosting, US hosting, Server Location · CDN — Edge Delivery Network (category-level via BuiltWith, not a named vendor)

Each value below links to the page it was read from, with the sentence it came from. Criteria Fireworks AI does not publish are not listed.

Fireworks AI pricing

3 criteria
Free tierNo
Sources (1) →
Kind of free offerFree credit
Sources (1) →
Pricing modelThree products on one bill. Serverless inference is per token, per model. Fine-tuning is per million TRAINING tokens by parameter band — $0.50 for models up to 16B, $10.00 above 300B. Dedicated GPUs are hourly: $7.00 for an H100 or H200, $10.00 for a B200.
Sources (1) →
  • Pricing — Fireworks AI ↗

    “MODELS UP TO 16B PARAMETERS $0.50 $1.00 $1.00 $2.00 [...] MODELS >300B (E.G. DEEPSEEK V3, KIMI K2) $10.00 $20.00 $20.00 $40.00 [...] H100 80 GB GPU $7.00 $8.00 H200 141 GB GPU $7.00 $8.00 B200 180 GB GPU $10.00 $13.00”

    Read 2026-09-06 · official pricing · Report a correction

Infrastructure

1 criteria
RegionsMulti-regions GLOBAL, US, EUROPE and APAC, plus 26 named Fireworks-managed single regions (US, Canada, Frankfurt, Iceland, Tokyo, New South Wales, Malaysia) for dedicated deployments. Only GLOBAL has quota by default; other placements must be granted by Fireworks.
Sources (1) →
  • Regions - Fireworks AI Docs ↗

    “Supported multi-regions: GLOBAL , US , EUROPE , APAC . [...] Single regions are concrete locations (e.g. US_IOWA_1 , EU_FRANKFURT_1 ) where your deployment can run.”

    Read 2026-09-13 · official docs · Report a correction

Inference

8 criteria
Input price, top model$3.00 per million input tokens for Kimi K3, the most expensive model in its serverless catalogue. Fireworks hosts other labs' open-weight models rather than its own, so "flagship" here means the top of what it serves.
Sources (1) →
Output price, top model$15.00 per million output tokens for Kimi K3.
Sources (1) →
Input price, cheapest model$0.15 per million input tokens for GLM 5.3 Flash, with output at $0.50. DeepSeek-V4-Flash is $0.22 in and $0.66 out — the same model DeepSeek itself sells at $0.44 peak.
Sources (1) →
  • Models — Fireworks AI ↗

    “GLM 5.3 Flash $0.15/M Input • $0.5/M Output • 1048576 Context [...] DeepSeek-V4-Flash-0731 $0.22/M Input • $0.66/M Output • 1048576 Context”

    Read 2026-09-06 · official pricing · Report a correction

Context window1,048,576 tokens on Kimi K3, GLM 5.3 and both DeepSeek V4 variants; 262,144 on Qwen3.8 and 131,072 on Muse Glimmer 30B. The window is the model's, not Fireworks'.
Sources (1) →
  • Models — Fireworks AI ↗

    “Kimi K3 $3/M Input • $15/M Output • 1048576 Context [...] Qwen3.8-2.4T-A95B $2/M Input • $6/M Output • 262144 Context [...] Muse Glimmer 30B $0.35/M Input • $1.5/M Output • 131072 Context”

    Read 2026-09-06 · official pricing · Report a correction

Max output tokensNo separate output cap. max_tokens is bounded by the model's context length — prompt plus completion must fit inside it — and what happens on overflow is set by context_length_exceeded_behavior rather than a fixed ceiling.
Sources (1) →
Prompt cachingSupported
Sources (1) →
Batch discountBatch API processes async requests at 50% off serverless per-token prices; cached tokens get a further 50% off via automatic prompt caching.
Sources (1) →
  • Batch API - Fireworks AI Docs ↗

    “Process large volumes of requests asynchronously at 50% off Serverless per-token prices [...] Batch jobs automatically use prompt caching for additional 50% cost savings on cached tokens.”

    Read 2026-09-13 · official docs · Report a correction

Rate limitsAdaptive serverless TPM ceilings by model size: 64.8M total prompt / 648k generated TPM under 400B params, down to 21.6M / 216k at 1.6T+ params. Ceilings are upper bounds; effective limits ramp with usage and spending tier.
Sources (1) →

GPU

2 criteria
H100 SXM, per GPU-hour$7.00 per hour for an H100 80GB on demand, or $8.00 on the shorter commitment shown beside it. The same rate applies to an H200 141GB.
Sources (1) →
Largest GPU offered180GB per GPU on a B200, at $10.00 an hour on demand.
Sources (1) →

Compliance

10 criteria
SOC 2SOC 2 Type II certified; audit reports available in the Fireworks Trust Center.
Sources (1) →
ISO 27001ISO 27001 achieved for its ISMS, alongside ISO 27701 and ISO 42001; certificate PDFs downloadable from the Fireworks Trust Center.
Sources (1) →
  • Data Security - Fireworks AI Docs ↗

    “Fireworks has achieved all three ISO certifications covering security, privacy, and AI management: [...] Certificate PDFs for all three certifications are available for download from the Fireworks Trust Center”

    Read 2026-09-13 · official docs · Report a correction

HIPAAStates it is HIPAA compliant and supports healthcare and life sciences organizations.
Sources (1) →
GDPR / data residencyEnterprise-only data residency pins all account inference to one region; US (us.api.fireworks.ai) is the only listed option, default is None, other regions via sales.
Sources (1) →
  • Data residency - Fireworks AI Docs ↗

    “Data residency restricts inference on your account to a single region. Once a region is set, every request must use that region’s API endpoint and a model served in that region. [...] Data residency is available on Enterprise accounts. [...] None is the default. For a region that is not listed, contact sales”

    Read 2026-09-13 · official docs · Report a correction

PCI DSSData Processing Addendum states Fireworks is not PCI compliant and has no obligation to become so; payment card data is Prohibited Personal Data.
Sources (1) →
Encryption at restEncrypted at rest with AES-256, and in transit with TLS 1.2+.
Sources (1) →
Customer-managed keysBring your own AWS KMS, Google Cloud KMS or Azure Key Vault key; CMEK covers training datasets and checkpoints (LoRA SFT, DPO, RFT), not full-parameter training. Final model weights listed as "Coming soon" on the same page.
Sources (1) →
  • Data Security - Fireworks AI Docs ↗

    “Customers may integrate their own AWS KMS, Google Cloud KMS, or Azure Key Vault key to retain governance and apply their own encryption policies. [...] Customer-Managed Encryption Keys for Training (LoRA SFT, DPO, and RFT; full-parameter training not supported)”

    Read 2026-09-13 · official docs · Report a correction

Role-based access controlFour account roles - Admin, User, Contributor and Inference User - with documented per-role resource and API-key permissions. Contributor and Inference User must be enabled by Fireworks support.
Sources (1) →
  • Managing users - Fireworks AI Docs ↗

    “Each user in an account is assigned a role that determines their level of access: [...] The contributor and inference-user roles are newer roles that provide more granular access control.”

    Read 2026-09-13 · official docs · Report a correction

Private networking / VPCBring Your Own Cluster (Enterprise, Private Preview) runs Fireworks inference inside your own Kubernetes cluster, cloud account or data-center network boundary.
Sources (1) →
  • Bring Your Own Cluster - Fireworks AI Docs ↗

    “Bring Your Own Cluster (BYOC) lets Enterprise customers run Fireworks inference inside their own Kubernetes cluster. Your inference compute runs in your cloud account or data center boundary, while Fireworks installs and operates the serving stack for you. [...] Bring Your Own Cluster is in Private Preview for Enterprise customers.”

    Read 2026-09-13 · official docs · Report a correction

Dedicated infrastructureOn-demand deployments run on dedicated GPUs billed per GPU-second, with no hard rate limits and performance unaffected by other users.
Sources (1) →
  • Deployments - Fireworks AI Docs ↗

    “On-demand deployments give you dedicated GPUs for your models, providing several advantages over serverless: [...] Lower latency, higher throughput, and predictable performance unaffected by other users [...] on-demand deployments are billed by GPU-second”

    Read 2026-09-13 · official docs · Report a correction

When these numbers change, hear about it. Sources are re-checked monthly; a repricing goes out as a short note.

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Fireworks AI in the news

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Fireworks AI pricing questions

How does Fireworks AI charge?

Three products on one bill. Serverless inference is per token, per model. Fine-tuning is per million TRAINING tokens by parameter band — $0.50 for models up to 16B, $10.00 above 300B. Dedicated GPUs are hourly: $7.00 for an H100 or H200, $10.00 for a B200.

Pricing — Fireworks AI · read

Does Fireworks AI have a free tier?

No free tier; the free offer is a free credit.

Pricing — Fireworks AI · read

Comments

Every page here is sourced and dated.