Fireworks excelled in scaling their model infrastructure to handle distributed inference effectively, preventing server overload and maintaining customer service during rapid growth.

At Fireworks, we manage the complexity of AI application scaling by utilizing a virtual cloud infrastructure that ensures high quality and reliability in GPU selection.

AI advancements are increasing GPU requirements, as the computational needs for inference are growing significantly.

Decentralized inference providers gained traction as they offered models that met market demand, resulting in a rapid increase in interest that many in the AI space are still unaware of.

A food chain company scaled an AI feature from one shop to a thousand shops in three months using our service, demonstrating the potential for fast scaling in AI applications.

We realized that scaling laws in AI showed predictable improvements with increased data and compute, which encouraged us to focus on building a product for knowledge work as AI matured.

A software development company expanded its AI feature rollout from 100,000 developers to 25 million in three months, highlighting the fascinating speed of scaling in AI.

AI's elasticity allows companies to scale operations quickly, enabling them to generate leads or complete tasks that previously required extensive hiring and staffing.

A company scaled to $12 million in revenue within a year by switching models multiple times, demonstrating that enterprises care more about workflow efficiency than the specific AI model used.