Fireworks AI
Production-grade open-source inference with fast cold starts
Fireworks AI provides optimised inference for open-weight models with a focus on production reliability and low latency. They host Llama, DeepSeek R1, and other popular open-source models with competitive per-token pricing and a serverless deployment model that minimises cold-start times.
Production-grade reliability with 320+ tokens/sec throughput and DeepSeek R1 reasoning at $3/1M input — a strong balance of speed and capability.
- 320+ tokens/sec on Llama 3.3 70B — fast GPU inference
- DeepSeek R1 hosting with strong reasoning capability
- Production-grade reliability with SLAs
- Serverless with minimal cold-start times
- OpenAI-compatible API
- Smaller model catalog than Together AI
- No fine-tuning on standard plans
- Slightly higher pricing than budget alternatives
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Fireworks AI — Frequently Asked Questions
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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. They host Llama, DeepSeek R1, and other popular open-source models with competitive per-token pricing and a serverless deployment model that minimises cold-start times. 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 320+ tokens/sec throughput and DeepSeek R1 reasoning at $3/1M input — a strong balance of speed and capability. 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's key strengths are: 320+ tokens/sec on Llama 3.3 70B — fast GPU inference; DeepSeek R1 hosting with strong reasoning capability; Production-grade reliability with SLAs. Limitations to consider: Smaller model catalog than Together AI; No fine-tuning on standard plans. 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. Prices shown are sourced from Fireworks AI's public pricing page and updated daily. 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. 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.