Fireworks AI - Fastest Inference for Generative AI

Why Fast Inference Matters for Your AI Products

Building AI-powered products is exciting, but here's what most developers quickly discover: getting models to respond quickly at scale is genuinely hard. You've likely experienced the frustration—your chatbot takes seconds to reply, your code assistant lags while you're in the flow, or your RAG system times out during peak usage. And let's not even talk about the costs when traffic grows.

This is exactly the problem Fireworks AI was built to solve.

Fireworks is a globally distributed generative AI inference cloud platform designed from the ground up for speed, scale, and affordability. Whether you're a startup moving fast or an enterprise needing enterprise-grade security, Fireworks gives you access to 100+ open-source models running on the latest GPU hardware—with performance that actually delivers.

The results speak for themselves: Notion uses Fireworks to power AI features for over 100 million users, cutting latency from 2 seconds down to 350 milliseconds—a 4x improvement. Quora saw 3x faster response times after migrating to open-source models on Fireworks. Cursor's code editing capabilities got so fast that users consistently mention it as a reason to switch.

The secret? A custom-built inference engine that's approximately 250% higher throughput and 50% faster than open-source alternatives, running on globally distributed infrastructure with the latest NVIDIA and AMD GPUs.

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What You Can Do with Fireworks AI

Fireworks isn't just another API wrapper. It's a complete inference platform built by engineers who previously led deep learning infrastructure at Meta PyTorch and Google Vertex AI. Here's what this means for you:

One-Click Access to 100+ Open-Source Models

You can deploy popular models like Llama 3/4, Gemma 3, Qwen3, DeepSeek R1/V3, GLM-4/5, Kimi K2, Mistral, and Mixtral with a single line of code. Fireworks handles the optimization, scaling, and infrastructure—so you can focus on building your product instead of managing servers.

The model library is updated fast. When new open-source models release, Fireworks typically supports them on Day 0, meaning you're never waiting weeks to try the latest advancements.

Serverless Inference: Start Fast, Scale Naturally

If you want to experiment without upfront costs, Fireworks serverless inference is designed for you. It works like this: you pay per token, there's zero setup required, no cold starts to worry about, and automatic scaling handles traffic spikes effortlessly.

New users receive $1 in free credits—just sign up and you're ready to make your first API call. For startups in early stages, this means you can validate your AI idea before spending a dime on infrastructure.

Fine-Tuning: Make Models Yours

Sometimes the base model isn't enough. Fireworks supports full fine-tuning capabilities including Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Fine-Tuning (RFT). You can train models on your own data to create vertical-specific solutions—whether that's legal document analysis, medical coding assistance, or domain-specific customer support.

Here's what makes this valuable: once you fine-tune a model, serving it costs exactly the same as the base model. You're not penalized for customization.

On-Demand Deployments: Dedicated Power When You Need It

For production workloads with demanding latency requirements, Fireworks offers dedicated GPU deployments. You get reserved A100, H100, H200, or B200 GPUs billed by the second—with no startup fees.

Pricing is transparent: A100 80GB runs $2.90/hour, H100 80GB is $4.00/hour, H200 141GB is $6.00/hour, and the latest B200 180GB is $9.00/hour. This is ideal for applications where every millisecond matters.

Enterprise-Grade RAG and Security

If you're building AI for regulated industries, Fireworks has you covered. You get zero data retention (your data is never stored), complete data sovereignty (you control where data lives), and a full suite of compliance certifications including SOC 2 Type 2, HIPAA, GDPR, ISO 27001, and ISO/IEC 42001 for AI management systems.

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Who's Using Fireworks AI

Fireworks serves a diverse range of teams—from early-stage startups to Fortune 500 companies. Here's how different types of users are applying the platform:

Code Assistance: Building Faster Developer Tools

Cursor, the AI-powered code editor, uses Fireworks to power its Fast Apply feature. By leveraging speculative decoding (a technique that predicts multiple tokens ahead), they achieved lightning-fast code edits with nearly lossless quantization quality. If you're building developer tools, this same optimization can dramatically improve user experience.

The Vercel team saw a 40x speed improvement on code fix models—a massive gain for developer productivity.

Conversational AI: Real-Time Customer Support

Cresta, which provides AI coaching for contact centers, uses Fireworks' Multi-LoRA technology to run multiple fine-tuned models simultaneously. The result? Real-time, context-aware customer guidance with cost reductions of up to 100x compared to GPT-4.

This matters because customer support teams need instant responses without burning through budget. Multi-LoRA lets you specialize models for different scenarios—billing inquiries, technical support, sales—without deploying separate infrastructure.

Agentic Systems: Complex Multi-Step Workflows

Sentient, an AI agent platform, runs 15 concurrent agent workflows through Fireworks. They achieved sub-2-second latency across complex chains while boosting GPU throughput by 50%. No infrastructure chaos.

For teams building AI agents—whether for research, automation, or autonomous decision-making—the ability to maintain speed across multi-step reasoning is crucial. Fireworks handles the orchestration so you can focus on agent logic.

Search and Recommendation: Semantic Understanding at Scale

Quora migrated their semantic search to Fireworks and saw response times improve 3x. This directly impacted user engagement and time spent on the platform.

If you're building search experiences—whether for e-commerce, content platforms, or enterprise knowledge bases—Fireworks' fast inference enables real-time semantic matching that traditional keyword search can't match.

Multimodal Generation: Images, Vision, and Speech

Fireworks supports the full spectrum of generative models: FLUX and Stable Diffusion for image generation, vision-language models for image understanding, and Whisper for speech-to-text.

This means you can build everything from AI design assistants to video analysis pipelines on a single platform, without stitching together multiple vendors.

Enterprise Knowledge Management: Secure RAG

For enterprises handling sensitive documents—legal contracts, medical records, financial reports—Fireworks provides the security foundation you need. Zero data retention means your prompts and outputs never persist on Fireworks servers. Complete data sovereignty lets you bring your own cloud or keep everything on-premises.

Combined with SOC 2 Type 2, HIPAA, and GDPR certifications, this makes Fireworks viable for even the most regulated environments.

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Understanding Fireworks AI Pricing

One thing you'll notice about Fireworks: pricing is completely transparent. No "contact sales for pricing" walls, no complicated tier structures. Here's what you actually pay:

Serverless Inference Pricing

Pay per million tokens. Simple.

Text and Vision Models:

Image Generation:

• Stable Diffusion series: $0.00013 per step
• FLUX.1 [dev]: $0.0005 per step
• FLUX.1 [schnell]: $0.00035 per step
• FLUX.1 Kontext Pro: $0.04 per image

Speech-to-Text:

• Whisper-v3-large: $0.0015 per minute
• Whisper-v3-large-turbo: $0.0009 per minute

Embeddings:

• Models ≤150M parameters: $0.008 per million tokens
• Models 150M-350M: $0.016 per million tokens

Fine-Tuning Pricing

Fine-tuning training is charged per million training tokens:

Remember: after fine-tuning, serving your custom model costs the same as the base model. No premium pricing for your own trained weights.

On-Demand Deployment Pricing

Dedicated GPUs billed by the hour:

Special Offers

• New users: $1 free credit on sign-up
• Batch inference: 50% off standard Serverless rates (you get 50% pricing when processing offline batches)

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The Technology Behind Fireworks AI

If you're curious about what actually makes Fireworks fast, here's the technical picture:

Infrastructure: Built for Performance

Fireworks runs on globally distributed virtual cloud infrastructure equipped with the latest hardware: NVIDIA A100, H100, H200, and B200 GPUs. This isn't legacy data center equipment—it's the same GPUs driving frontier AI research.

Custom Inference Engine: Built by Deep Learning Engineers

The Fireworks inference engine was built by the same engineers who built PyTorch at Meta and Vertex AI at Google. This matters because inference optimization is fundamentally different from model training—every microsecond counts when you're serving millions of requests.

Key optimizations include:

• Speculative Decoding: Predicts multiple tokens ahead, then validates them in parallel. This is how Cursor achieved its fast code editing.
• Multi-LoRA: Runs multiple fine-tuned models simultaneously on the same GPU. Cresta uses this to run specialized models for different support scenarios at a fraction of the cost.
• Quantization-Aware Tuning: Reduces model size without significant quality loss, enabling faster inference.
• Adaptive Speculation: Dynamically adjusts prediction strategies based on workload patterns.
• Dynamic Batching: Groups requests efficiently to maximize GPU utilization.

The result: Approximately 250% higher throughput and 50% faster latency compared to open-source inference engines like vLLM or Text Generation Inference.

Day 0 Model Support

When a new open-source model releases—whether it's Llama 4, Qwen3, or DeepSeek—Fireworks typically has it available the same day. This gives you immediate access to the latest capabilities without waiting for manual integration.

Advanced Fine-Tuning

For teams that need more than base models, Fireworks supports three fine-tuning approaches:

• Supervised Fine-Tuning (SFT): Standard approach for teaching models on labeled data
• Direct Preference Optimization (DPO): Aligns models with human preferences without reinforcement learning
• Reinforcement Fine-Tuning (RFT): Latest technique for domain-specific reasoning improvements

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Frequently Asked Questions

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Ready to Build Fast AI Products?

Whether you're a startup moving fast or an enterprise needing enterprise-grade security, Fireworks AI gives you the infrastructure to ship AI products that actually perform. The combination of open model choice, industry-leading inference performance, and transparent pricing removes the traditional trade-offs between speed, cost, and flexibility.

Start experimenting today—your $1 free credit awaits at fireworks.ai.