Fireworks AI
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What is Fireworks AI
Fireworks AI is a platform for running and deploying generative AI models, with an emphasis on serving large language models (LLMs) via APIs and managed infrastructure. It targets engineering and ML teams that need to integrate text generation, embeddings, and related inference workloads into applications. The product focuses on model hosting, performance-oriented inference, and operational tooling for production use (for example, monitoring and scaling). It also supports using third-party and open model families alongside managed endpoints.
Production-focused LLM serving
Fireworks AI centers on deploying and operating LLM inference endpoints rather than end-to-end data science workflows. This aligns well with application teams that need stable APIs, predictable latency, and scaling controls. It can reduce the amount of infrastructure engineering required to run model serving stacks internally. The offering is positioned for production integration use cases such as chat, summarization, and retrieval-augmented generation pipelines.
API-based developer integration
The platform provides API access patterns that fit common software delivery workflows (application backends, microservices, and CI/CD). This can simplify embedding LLM capabilities into products without building custom serving layers. Teams can standardize on a single service for multiple generative tasks (generation and embeddings) rather than stitching together disparate components. The approach is typically easier to operationalize than tools primarily designed for interactive analytics or desktop modeling.
Managed infrastructure operations
Fireworks AI abstracts infrastructure concerns such as provisioning, scaling, and runtime management for model inference. This is useful for organizations that do not want to manage GPU capacity planning and serving reliability on their own. Centralized operations can also help with governance practices like usage tracking and endpoint management. Compared with general-purpose ML platforms, the scope is narrower but more directly aligned to LLM inference operations.
Narrower than full ML platforms
Fireworks AI is oriented toward generative model inference and deployment, not the full lifecycle of classical ML (data preparation, feature engineering, training, and experiment management). Organizations seeking an integrated environment for building a wide variety of predictive models may need additional tooling. This can increase platform sprawl when teams also require broader analytics, AutoML, or statistical modeling capabilities. Fit depends on whether the primary need is LLM serving versus end-to-end ML development.
Model and workload constraints
Supported models, context lengths, and runtime behaviors depend on what the service offers and maintains. If a team requires specific architectures, custom kernels, or highly specialized inference configurations, they may face limitations compared with self-managed deployments. Some workloads (for example, strict on-prem requirements or highly customized fine-tuning pipelines) may not align with a managed service model. Buyers should validate compatibility with required model families and performance targets.
Vendor dependency for operations
Using a managed inference platform introduces dependency on the vendor for availability, pricing changes, and roadmap decisions. Data handling, retention, and compliance controls must be evaluated against internal policies, especially for regulated workloads. Migration to another serving stack can require application changes if APIs or model behaviors differ. Teams should assess contractual SLAs and portability options early.
Plan & Pricing
Pricing model: Pay-as-you-go Free tier/trial: Get started with $1 in free credits (postpaid billing). No permanently free plan listed.
Example costs (selected notable SKUs from official pricing page):
-
Text & Vision (serverless, $ / 1M tokens):
- Less than 4B parameters — $0.10 per 1M tokens
- 4B - 16B parameters — $0.20 per 1M tokens
- More than 16B parameters — $0.90 per 1M tokens
- MoE 0B - 56B (e.g., Mixtral 8x7B) — $0.50 per 1M tokens
- MoE 56.1B - 176B (e.g., DBRX, Mixtral 8x22B) — $1.20 per 1M tokens
- Selected model examples (input/output pricing where applicable):
- DeepSeek V3 — $0.56 input, $1.68 output
- GLM-4.7 — $0.60 input, $2.20 output
- GLM-5 — $1.00 input, $0.20 cached input, $3.20 output
- Kimi K2 Instruct / Thinking — $0.60 input, $2.50 output
- OpenAI gpt-oss-120B — $0.15 input, $0.60 output
-
Speech-to-Text (billed per audio minute, billed per second):
- Whisper-v3-large — $0.0015 per audio minute
- Whisper-v3-large-turbo — $0.0009 per audio minute
- (Diarization +40% surcharge; batch API prices reduced 40%)
-
Image generation (serverless, priced per diffusion step unless noted):
- All non-FLUX models — $0.00013 per step (~$0.0039 per 30-step image)
- FLUX.1 [dev] — $0.0005 per step (~$0.014 per 28-step image)
- FLUX.1 [schnell] — $0.00035 per step (~$0.0014 per 4-step image)
- FLUX.1 Kontext Pro — $0.04 per image (flat)
- FLUX.1 Kontext Max — $0.08 per image (flat)
-
Embeddings (per 1M input tokens):
- up to 150M params — $0.008 per 1M input tokens
- 150M - 350M params — $0.016 per 1M input tokens
- Qwen3 8B — $0.10 per 1M input tokens
-
Fine-tuning (priced per 1M training tokens):
- Supervised Fine Tuning (SFT) / Direct Preference Optimization (DPO):
- Models up to 16B — SFT $0.50 / DPO $1.00 per 1M training tokens
- Models 16.1B - 80B — SFT $3.00 / DPO $6.00 per 1M
- Models 80B - 300B — SFT $6.00 / DPO $12.00 per 1M
- Models >300B — SFT $10.00 / DPO $20.00 per 1M
- Reinforcement Fine Tuning — priced per GPU hour at on-demand deployment rates (billed per second).
- Supervised Fine Tuning (SFT) / Direct Preference Optimization (DPO):
-
On-demand deployments (pay per GPU second; listed as $ / hour):
- A100 80 GB GPU — $2.90 per hour
- H100 80 GB GPU — $4.00 per hour
- H200 141 GB GPU — $6.00 per hour
- B200 180 GB GPU — $9.00 per hour
Discounts & notes (official):
- Cached input tokens are priced at 50% for text & vision models unless otherwise specified.
- Batch inference is priced at 50% of serverless pricing for input and output tokens.
- Fireworks operates pay-as-you-go for non-Enterprise usage; for enterprise-grade security, reliability, and lower costs/contact sales for custom pricing and bulk discounts.
Sources: Official Fireworks AI Pricing page and Docs (pricing page: fireworks.ai/pricing; docs: docs.fireworks.ai).
Seller details
Fireworks AI, Inc.
Redwood City, CA, USA
2022
Private
https://fireworks.ai/
https://x.com/FireworksAI_HQ
https://www.linkedin.com/company/fireworks-ai/
