Fireworks AI
Production-grade serverless inference platform for open AI models
About Fireworks AI
Fireworks AI is an enterprise AI inference and model serving platform built to run open-weights LLMs, vision models, and multimodal architectures with lightning-fast speeds and lowest cost. Created by former Meta AI and PyTorch infrastructure engineers, Fireworks powers millions of daily AI requests with sub-100ms time-to-first-token (TTFT) and high token throughput. Fireworks allows developers to seamlessly deploy, fine-tune, and serve models like Llama 3.1/3.3, DeepSeek-R1/V3, Mixtral, Qwen 2.5, and Flux.1 with zero cold starts. It uniquely supports instant LoRA fine-tuning switching on shared GPU infrastructure, allowing thousands of custom fine-tuned adapters to run without paying for dedicated hardware.
The Fireworks AI engine utilizes proprietary GPU compilation optimizations, speculative decoding, dynamic kernel fusing, and custom tensor-parallel kernels to maximize memory bandwidth and FLOPS efficiency on NVIDIA H100 and B200 clusters. Fireworks provides a fully OpenAI-compatible REST and streaming API alongside native function calling, JSON schema guarantees, and multimodal image input. Its FireAttention technology drastically cuts KV-cache memory overhead, enabling massive concurrency and context lengths up to 128k tokens while maintaining deterministic latency SLAs.
Sign up at Fireworks AI and generate your OpenAI-compatible API key.
Select from over 100+ state-of-the-art open models (Llama 3.3, DeepSeek-R1, Qwen 2.5, Flux.1).
Point your existing OpenAI SDK client to the Fireworks base URL endpoint.
Deploy and serve custom LoRA weights dynamically without provisioning dedicated GPUs.
Monitor latency, tokens per second, and error rates via the developer telemetry dashboard.
Capabilities & Features
Common Use Cases
ultra-fast-llm-inference
custom-lora-fine-tuning
function-calling-pipelines
compound-ai-systems
multimodal-vision-serving
Frequently Asked Questions
How is Fireworks AI faster than standard open-source serving engines?
Fireworks uses custom GPU kernels, FireAttention KV-cache optimization, and speculative decoding to achieve 3-5x higher throughput than standard vLLM deployments.
Can I drop Fireworks into my existing OpenAI codebase?
Yes, Fireworks is 100% OpenAI API compatible. You simply change the baseURL to api.fireworks.ai/inference/v1 and pass your Fireworks API key.
Does Fireworks support fine-tuned models?
Yes, Fireworks allows you to train and deploy LoRA adapters instantly, serving custom fine-tunes on shared serverless infrastructure with zero idle cost.
Free Plan
$1 in free credits to test all serverless models. Pay-per-token with zero monthly subscription fees.
Paid Plan
Serverless pricing from $0.20 / 1M tokens for Llama 3.1 8B, $0.90 / 1M tokens for 70B, and dedicated GPU clusters from $2.20/GPU-hr.
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Pros & Cons
Industry-leading inference speeds with sub-100ms time-to-first-token (TTFT)
Substantial cost savings (up to 80% cheaper than proprietary model APIs)
Instant LoRA adapter switching with zero provisioning delay or dedicated GPU costs
Flawless OpenAI API compatibility with native function calling and structured outputs
Enterprise SLAs, SOC2 Type II compliance, and dedicated private VPC deployments
Focused on open-weights model ecosystem (does not serve closed proprietary models like Claude)
Advanced LoRA training pipelines require understanding of PyTorch datasets
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