Fireworks AI is a high-performance generative AI inference cloud platform running on globally distributed infrastructure with latest hardware. It offers industry-leading throughput and latency, supporting 100+ open-source models including Llama, Qwen, DeepSeek, and GLM. Perfect for AI startups and enterprises requiring fast, secure deployment with full compliance certifications.




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.
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:
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.
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.
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.
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.
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.
If you're new to Fireworks, begin with the Serverless tier. You can validate your use case with the $1 free credit, then seamlessly migrate to On-Demand deployments when you need guaranteed performance.
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:
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.
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.
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.
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.
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.
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.
If your team is building a code assistant, ask about Cursor's approach with speculative decoding. For customer service, Multi-LoRA delivers the best cost-efficiency. For agent systems, prioritize low-latency configurations from the start.
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:
Pay per million tokens. Simple.
Text and Vision Models:
| Model Size | Input (per 1M tokens) | Output (per 1M tokens) |
|---|---|---|
| < 4B parameters | $0.10 | $0.10 |
| 4B - 16B parameters | $0.20 | $0.20 |
| > 16B parameters | $0.90 | $0.90 |
| MoE models (0-56B like Mixtral 8x7B) | $0.50 | $0.50 |
| MoE models (56B-176B like DBRX) | $1.20 | $1.20 |
| DeepSeek V3 | $0.56 | $1.68 |
| GLM-5 | $1.00 | $3.20 |
Image Generation:
Speech-to-Text:
Embeddings:
Fine-tuning training is charged per million training tokens:
| Model Size | SFT (per 1M tokens) | DPO (per 1M tokens) |
|---|---|---|
| ≤ 16B parameters | $0.50 | $1.00 |
| 16B - 80B | $3.00 | $6.00 |
| 80B - 300B | $6.00 | $12.00 |
| > 300B | $10.00 | $20.00 |
Remember: after fine-tuning, serving your custom model costs the same as the base model. No premium pricing for your own trained weights.
Dedicated GPUs billed by the hour:
| GPU Type | Price per Hour |
|---|---|
| A100 80GB | $2.90 |
| H100 80GB | $4.00 |
| H200 141GB | $6.00 |
| B200 180GB | $9.00 |
For most startups and side projects, start with Serverless. You get automatic scaling, no idle costs, and the $1 free credit lets you test extensively. Migrate to On-Demand only when you need guaranteed latency SLAs or running costs exceed dedicated GPU pricing.
If you're curious about what actually makes Fireworks fast, here's the technical picture:
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.
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:
The result: Approximately 250% higher throughput and 50% faster latency compared to open-source inference engines like vLLM or Text Generation Inference.
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.
For teams that need more than base models, Fireworks supports three fine-tuning approaches:
Fireworks was built by engineers who led deep learning infrastructure at Meta PyTorch and Google Vertex AI. This background shows in three ways: (1) Day 0 support for new open-source models, (2) a custom inference engine delivering 250% higher throughput and 50% faster latency, and (3) the most open model library with 100+ options. You're not locked into a single model family.
Absolutely not. Fireworks does not use customer content to train any models. You can opt for zero data retention (data is processed and discarded) or complete data sovereignty (keep everything in your own cloud environment). Your prompts and outputs remain yours.
Full enterprise compliance: SOC 2 Type 2, HIPAA (healthcare), GDPR (EU data protection), ISO 27001:2022, ISO 27701, and ISO/IEC 42001:2023 (AI management systems). This makes Fireworks suitable for regulated industries including healthcare, finance, and government.
Sign up at fireworks.ai and you immediately receive $1 in free credits. No credit card required. You can start making API calls in minutes using the Serverless tier—zero configuration needed. When you're ready for guaranteed performance, explore On-Demand deployments with dedicated GPUs.
Over 100 open-source models including Llama 3/4, Gemma 3, Qwen3, DeepSeek V3/R1, GLM-4/5, Kimi K2/K2.5, Mistral, Mixtral, Stable Diffusion, FLUX, and Whisper. The library is updated on Day 0 when new models release.
Fine-tuning training is charged per million training tokens (see the pricing table above). Once your model is fine-tuned and deployed, serving it costs exactly the same as the base model. You're not penalized for running your custom model.
Yes. Batch inference (for offline, large-scale processing) is priced at 50% of standard Serverless rates. This makes it cost-effective for tasks like document processing, data enrichment, or scheduled analysis workloads.
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.
Fireworks AI is a high-performance generative AI inference cloud platform running on globally distributed infrastructure with latest hardware. It offers industry-leading throughput and latency, supporting 100+ open-source models including Llama, Qwen, DeepSeek, and GLM. Perfect for AI startups and enterprises requiring fast, secure deployment with full compliance certifications.
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