"With video, we're earlier in the competition. There's still a lot of leapfrogging happening," explains Burkay Gur, co-founder and CEO of Fal.ai, in a recent interview with a16z General Partner Jennifer Li. This dynamic, characterized by rapid advancements and fierce competition, stands in stark contrast to the maturing image generation space, where quality has begun to converge. For Fal.ai, this volatile landscape isn't a deterrent but a proving ground, highlighting their strategic focus on an inference platform optimized for speed, performance, and user experience.
Gur and Head of Engineering Batuhan Taskaya joined Li to delve into how they built their "generative media cloud," a story rooted in early infrastructure challenges and a keen eye for emerging opportunities in AI. Their journey began four years ago, initially focused on machine learning pipelines for fraud detection, but a pivotal moment arrived with the explosion of generative AI. "About a year and a half into us starting the company, ChatGPT happened, DALL-E happened, the whole world of machine learning and AI changed," Gur recounts, describing their adaptive pivot.
This shift wasn't merely opportunistic; it was driven by a deep technical curiosity. The team, operating with meager GPU capacity during the AI infrastructure crunch, became obsessed with optimization. Taskaya vividly recalls the early days: "I remember when Sora came out, even in our team people were like, 'Oh my god, OpenAI is like so far ahead that no one's going to be able to catch up.' And then Luma released their model, Runway released their model, Kling released their model, Minimax released... and every release, if you're not the best, you're not releasing generally." He highlighted the struggle to run Stable Diffusion 1.5, which took "10-plus seconds," and the "3,000 person queue" on Hugging Face. This scarcity forced a relentless pursuit of efficiency.
Fal.ai recognized that while large language models demanded vast pre-training, generative media models, particularly video, thrived on specialized workflows and fine-tuning. "The quality was not there to one-shot your generations," Taskaya elaborates, emphasizing the need for editing, upscaling, and background removal — a complex, bespoke workflow. This insight led Fal.ai to build their own workflow product, focusing on "most efficient kernels" and ensuring models could be quickly deployed and optimized across a multi-cloud system.
The company's culture is deeply ingrained with this drive for speed and customer obsession. Gur notes that their early team was heavily engineering-focused, with engineers directly engaging with customer problems. "We want our sales people to be advocates for the customers," Taskaya adds, stressing a service-oriented approach over traditional sales. This direct feedback loop, combined with their deep technical expertise in optimizing every layer of the inference stack, from distributed file systems to multi-layer caching, has allowed Fal.ai to stay ahead. They are not just hosting models but actively ensuring peak performance, continuously adapting to the week-by-week evolution of the AI video landscape.
