Fireworks AI
Fireworks AI enables developers to deploy and fine-tune open-source models with ultra-low latency inference.
Fireworks AI provides a unified inference and fine-tuning platform for open-source large language, vision, and multimodal models, eliminating the need for users to manage GPU infrastructure. The platform serves over 100 state-of-the-art models with 4x lower latency than competitors like vLLM and up to 12x faster inference than comparable engines. Built by former Meta and Google AI leaders, Fireworks enables developers and enterprises to deploy, customize, and scale AI models with enterprise-grade compliance and zero data retention by default.
Problem solved
Teams struggle to deploy open-source AI models efficiently at scale, facing high latency, infrastructure complexity, and vendor lock-in with proprietary model providers.
Target customer
Mid-market to enterprise development teams and AI-native companies (e.g., Uber, DoorDash, Notion, Cursor, Perplexity) needing fast, cost-effective inference for open-source models without infrastructure overhead.
Founders
L
Lin Qiao
CEO & Co-Founder
Former Senior Director of Engineering at Meta leading 300+ engineers on PyTorch and Caffe2; PhD in computer science from UC Santa Barbara; previously at LinkedIn and IBM.
B
Benny Chen
Co-Founder
Former Meta ads infrastructure lead.
C
Chenyu Zhao
Co-Founder
Former Google Vertex AI Lead.
D
Dmytro Dzhulgakov
Co-Founder
Former PyTorch core maintainer at Meta.
Funding history
Series A
$25M
March 2024
Led by Benchmark
· Unknown
Series B
$52M
Unknown
Led by Sequoia Capital
· NVIDIA, AMD, MongoDB Ventures
Series C
$250M
October 28, 2025
Led by Lightspeed Venture Partners, Index Ventures, Evantic
· Sequoia Capital
Total raised:
$327M
Industries
Pricing
Pay-as-you-go model with free credits for new users. Serverless pricing starts at $0.10/M tokens for models under 4B parameters, scaling up to $2.17/M tokens for larger models. On-demand GPU deployments from $2.90/hr (A100 80GB) to $9.00/hr (B200). Fine-tuning starts at $0.50/M tokens. Cached inputs and batch inference receive 50% discounts.
Notable customers
Samsung, Uber, DoorDash, Notion, Shopify, Upwork, Cursor, Perplexity, Sourcegraph
Tech stack
Webpack
Open Graph
Vercel Analytics (Analytics)
HSTS (Security)
Google Workspace (Email)
Vercel (PaaS)
Priority Hints (Performance)
Google Analytics (Analytics)
Website
Competitors
Together AI
Closest direct competitor with similar serverless inference and fine-tuning, but Together focuses more broadly on GPU clusters and batch processing; raised $305M Series B and generates $150M+ ARR.
Baseten
Positioned more as an enterprise inference engineering platform for custom/proprietary models with configurable runtime optimization rather than a commodity model catalog.
OpenAI/Anthropic
Proprietary closed models with limited control; Fireworks offers lower costs, greater model flexibility, fine-tuning capabilities, and no vendor lock-in through open-source models.
vLLM
Open-source inference engine; Fireworks provides 4x lower latency and managed infrastructure without self-hosting requirements.
Why this matters: Fireworks represents a credible open-source alternative to closed AI providers, backed by an exceptional founding team (former Meta/Google AI leaders) and $327M in funding from top-tier VCs including Sequoia and Lightspeed. The platform's demonstrated 4x latency improvements and growing customer base (Uber, DoorDash, Notion) suggest it's becoming infrastructure for the emerging AI application layer.
Best for: AI-native startups and enterprises needing sub-second latency inference on open-source models without managing GPU infrastructure or dealing with vendor lock-in.
Use cases
Code Assistance & Generation
Cursor uses Fireworks' custom Llama 3-70B model to deliver 1000 tokens/sec for code generation features like instant apply, smart rewrites, and cursor prediction, enabling real-time developer workflows without latency friction.
Conversational AI & Search
Perplexity and Sourcegraph leverage Fireworks for sub-second latency conversational AI and enterprise search, enabling responsive user experiences at scale without self-managed GPU infrastructure.
Fine-Tuned Model Customization
Development teams can transition from dataset preparation to querying a custom fine-tuned model in minutes, allowing rapid experimentation with domain-specific language models for customer-facing applications.
Cost-Sensitive Production Inference
Companies reduce per-token costs while achieving 3x latency improvements by migrating from other engines, making cost-effective inference viable for high-volume applications like enterprise AI search or agentic workflows.
Alternatives
Together AI
Choose Together if you need broader GPU cluster capabilities and batch processing infrastructure in addition to serverless inference.
vLLM
Choose vLLM for an open-source, self-hosted inference engine if you prefer full infrastructure control over managed latency optimization.
OpenAI API
Choose OpenAI if proprietary state-of-the-art capabilities and ease-of-use outweigh cost considerations and vendor lock-in concerns.
FAQ
What does Fireworks AI do? +
Fireworks AI is a managed inference and fine-tuning platform for open-source AI models. It eliminates GPU infrastructure management, delivers 4x lower latency than competitors, and enables rapid model customization through fine-tuning.
How much does Fireworks AI cost? +
Fireworks uses pay-as-you-go pricing starting at $0.10/M tokens for small models up to $2.17/M tokens for large models. On-demand GPUs start at $2.90/hr, and fine-tuning starts at $0.50/M tokens. New users receive free credits.
What are alternatives to Fireworks AI? +
Together AI offers broader GPU infrastructure; vLLM provides open-source self-hosted inference; OpenAI/Anthropic offer proprietary models with simpler APIs but higher costs and less control.
Who uses Fireworks AI? +
AI-native companies and enterprises including Cursor, Perplexity, Uber, DoorDash, Notion, and Shopify use Fireworks for code generation, conversational AI, search, and agentic workflows.
How does Fireworks AI compare to Together AI? +
Both offer serverless inference and fine-tuning for open-source models, but Fireworks emphasizes lower latency (4x faster) and multimodal support, while Together focuses on broader GPU cluster infrastructure. Together generates $150M+ ARR; Fireworks is earlier stage.
Tags
LLM inference
open-source models
fine-tuning
low-latency
serverless
GPU infrastructure
multimodal AI
developer tools
enterprise AI
model serving