Fireworks AI
H100/H200/B200/B300/GB300 on-demand — Serverless Training API, Forge 2026 conference
Fireworks AI provides optimised inference for open-weight models with a focus on production reliability and low latency. On-demand GPU compute (H100, H200, B200, B300, GB300) is available alongside serverless inference. The Serverless Training API enables fine-tuning for GLM 5.3, Qwen 3.8 27B, Kimi K3, DeepSeek V4 Flash, and Muse Glimmer 30B without managing GPU infrastructure. Fireworks hosted its inaugural Forge 2026 conference.
Production-grade reliability with the latest GPU hardware (B300/GB300) and a Serverless Training API — the most complete platform for open-weight model inference and fine-tuning.
- 320+ tokens/sec on Llama 3.3 70B — fast GPU inference
- H100/H200/B200/B300/GB300 on-demand GPU access
- Serverless Training API for fine-tuning without infrastructure
- Production-grade reliability with SLAs
- OpenAI-compatible API
- Slightly higher pricing than budget alternatives
- Smaller model catalog than Together AI
- Serverless Training is newer and less battle-tested
Fireworks AI Models
| Model | Modality | Tier | Price | Cached /1M | TTFT | Throughput | Context | Intel. | Caps | |
|---|---|---|---|---|---|---|---|---|---|---|
llama-v3p1-8b-instruct Llama 3.1 | Chat | budget | $0.10/ $0.10 in / out per 1M EST Jul 2026 | — | 180ms | 220 t/s | 131K | 62 | ||
qwen3-235b-a22b Qwen 3 | Chat | advanced | $0.22/ $0.88 in / out per 1M EST Jul 2026 | — | 500ms | 55 t/s | 131K | 82 | ||
mixtral-8x7b-instruct Mixtral | Chat | efficient | $0.50/ $0.50 in / out per 1M EST Jul 2026 | — | 250ms | 110 t/s | 33K | 68 | ||
llama-v3p3-70b-instruct Llama 3.3 | Chat | efficient | $0.90/ $0.90 in / out per 1M EST Jul 2026 | — | 200ms | 320 t/s | 131K | 64 | ||
llama-v3p1-70b-instruct Llama 3.1 | Chat | efficient | $0.90/ $0.90 in / out per 1M EST Jul 2026 | — | 320ms | 95 t/s | 131K | 72 | ||
deepseek-v3 DeepSeek V3 | Chat | advanced | $0.90/ $0.90 in / out per 1M EST Jul 2026 | — | 350ms | 85 t/s | 131K | 80 | ||
deepseek-r1 DeepSeek R1 | Chat | advanced | $3.00/ $8.00 in / out per 1M EST Jul 2026 | — | 1.8s | 55 t/s | 164K | 78 |
Fireworks AI — Frequently Asked Questions (FAQs)
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Full comparison tableFireworks AI LLM pricing overview
Fireworks AI is an inference API provider that hosts open-weight and third-party large language models. Fireworks AI provides optimised inference for open-weight models with a focus on production reliability and low latency. On-demand GPU compute (H100, H200, B200, B300, GB300) is available alongside serverless inference. The Serverless Training API enables fine-tuning for GLM 5.3, Qwen 3.8 27B, Kimi K3, DeepSeek V4 Flash, and Muse Glimmer 30B without managing GPU infrastructure. Fireworks hosted its inaugural Forge 2026 conference. Common use cases include Production, Reasoning, Speed, Open-source. Headquartered in San Francisco, CA, founded 2022. All models are accessible via a REST API compatible with standard OpenAI-style request formats, enabling drop-in integration with most LLM frameworks and orchestration tools.
Fireworks AI vs other LLM providers
Fireworks AI competes with OpenAI, Anthropic, Google, Groq, Together AI, Mistral, Cohere, and other inference API providers across dimensions of price, throughput, latency, context window, and model intelligence. Production-grade reliability with the latest GPU hardware (B300/GB300) and a Serverless Training API — the most complete platform for open-weight model inference and fine-tuning. Use the LLM provider comparison tool to see Fireworks AI token pricing, latency, and throughput side-by-side with any other provider. The full LLM pricing table shows all providers ranked by input token cost, output token cost, and throughput in a single sortable view.
Why choose Fireworks AI?
Fireworks AI stands out because: Production-grade reliability with the latest GPU hardware (B300/GB300) and a Serverless Training API — the most complete platform for open-weight model inference and fine-tuning.. The API currently offers 7 models, spanning 3 advanced, 3 efficient, 1 budget tiers. For teams running high-volume inference workloads, prompt caching and batch API endpoints can reduce effective input token costs by 50–90% — check the context window cost guide for a full breakdown of caching economics.
Understanding Fireworks AI token pricing
Fireworks AI charges separately for input (prompt) and output (completion) tokens, priced per 1M tokens in USD. Output tokens are typically 3–5× more expensive than input tokens due to the compute cost of autoregressive generation. The cheapest {meta.name} input rate is currently $0.10/1M tokens. Prices shown are sourced from Fireworks AI's public pricing page and updated every 15 minutes. Need help estimating your spend? Read the LLM API cost calculator guide — it covers tokens, context windows, prompt caching, and batch discounts with worked examples for RAG, chat history, and document processing workloads.
Fireworks AI context window and model capabilities
Context window size directly affects both capability and cost — every token in the context window is charged as an input token. Fireworks AI's largest context window is 164K tokens. For RAG and document processing workloads, longer context windows enable richer retrieval but increase per-call costs proportionally. Prompt caching — where supported — stores the KV state of repeated prefixes and charges 75–90% less for cache hits, making it the most impactful cost optimization for applications with consistent system prompts or retrieved documents. See the LLM context window cost guide for a full analysis of how context length affects your API bill.
Self-hosted vs managed inference: when Fireworks AI makes sense
Managed inference APIs like Fireworks AI eliminate infrastructure overhead — no GPU provisioning, driver management, or model serving stack to maintain. The trade-off is cost at scale: a single H100 at ~$2.50/hr can serve ~500K tokens/min of Llama 3.3 70B, which at Fireworks AI API rates would cost significantly more per token. The break-even point depends on your request volume, latency requirements, and engineering capacity. For teams processing fewer than ~10M tokens/day, managed APIs are almost always cheaper when total cost of ownership is considered. Above that threshold, self-hosted inference on rented GPU compute typically wins on unit economics. Read the cheapest GPU cloud guide for a full break-even analysis. Historical Fireworks AI token price data is available in the LLM price history charts.
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