In the ever-evolving world of artificial intelligence, few moments define an era like the release of ChatGPT. But while language models captured headlines, another revolution was quietly brewing—one centered not on words, but on images, videos, 3D, and audio. At the heart of this transformation is FAL, a generative media platform co-founded by Burkay Gür, designed specifically for developers who want to build with cutting-edge AI models—without the infrastructure headaches.
This isn’t just another tech success story. It’s a startup story forged in curiosity, immigration struggles, technical grit, and a deep belief that the next big wave in AI won’t be about text—it’ll be about media.
And FAL is positioning itself to be the engine behind it.
From Turkey to Silicon Valley: The Making of a Founder
Burkay Gür’s journey didn’t start in a Palo Alto garage or a Stanford dorm. It began in Turkey, where he received a rigorous education in math and science—a foundation that would later serve him well in the U.S. tech scene.
When he moved to America in 2007 for college, the cultural shift was stark. “Facebook had just come out,” he recalls. “I wasn’t as tapped into the culture.” While academics came easily, navigating the unspoken rules of internships, career planning, and professional networking proved far more challenging.
Like many immigrants, Burkay also faced the complex realities of the U.S. immigration system. He started his green card process early—an all-too-common experience that often ties talented individuals to large corporations simply to maintain legal status. For years, this reality shaped his career path, including an early stint at Oracle working on what he describes as “fairly boring things.”
But everything changed around 2015, when deep learning began its ascent. Burkay dove headfirst into the field, eventually joining Coinbase during its formative years—when the company was just 40–50 people strong. There, surrounded by passionate builders in the crypto space, he learned a crucial lesson: alignment with mission matters.
“Everyone was a crypto head,” he says. “You wouldn’t find anyone who wasn’t insanely excited about what they were building.” That energy became a blueprint for his own future venture.
The Birth of FAL: A Niche Market with Explosive Potential
Fast forward to the early days of the pandemic. Burkay and his long-time friend and future co-founder rented a house in Palm Springs. With time on their hands and ambition in their hearts, they began exploring ideas for a new company.
They didn’t have a grand plan yet—but they knew two things:
- They wanted to work on something they loved.
- They wanted to solve a real technical problem.
That problem? Hosting generative AI models is hard—especially for images, video, and audio.
While large language models (LLMs) like GPT were grabbing attention, Burkay saw a parallel opportunity in multimodal AI. “We felt similarly about image models,” he explains. “As models get better, quality increases, resolution improves, and controllability grows.”
But there was a catch: these models—often based on diffusion architectures—are computationally intensive, slow to run, and notoriously difficult to deploy at scale. Most developers lacked the infrastructure expertise (or budget) to host them reliably.
Enter FAL.
“We host models that can generate images, videos, 3D, audio… Typically, these models are very hard to host. So the problem we solve is hosting these models as APIs, which makes it very easy to consume for developers.”
FAL wasn’t just another API wrapper. The team built an in-house inference engine optimized specifically for diffusion models, achieving 2–3x better performance than standard solutions. Why does that matter?
“Latency kills creativity. Latency kills productivity.”
In a world where developers expect near-instant feedback during iteration, waiting minutes for a single image or video render is a dealbreaker. FAL made real-time generative media not just possible—but practical.
Strategic Focus: Why FAL Bet Big on Images and Video
In the post-ChatGPT gold rush, countless startups pivoted to LLMs. But FAL made a deliberate choice: stay focused on generative media.
“We could have run inference for LLMs too,” Burkay admits. “But focusing on image and video is going to be an important differentiator.”
This strategic discipline paid off. By narrowing their scope, FAL developed deep expertise. Their early customers—many working on similar fine-tuning tasks for image generation—gave consistent, actionable feedback. This allowed FAL to refine their platform with surgical precision.
“If you focus on a specific market, you get to work with your users in a closer manner. You understand their problems better.”
Today, FAL powers generative workflows for industry giants like Adobe, Canva, Shopify, and Perplexity. And with a $90 million annualized run rate, the company is proving that niche doesn’t mean small—it means scalable.
The $125M Series C and the Road to Unicorn Status
In a testament to investor confidence, FAL recently closed a $125 million Series C round, catapulting its valuation to $1.5 billion. This wasn’t just a financial milestone—it was validation of their thesis: the infrastructure layer for generative media is critical, urgent, and massively underserved.
“We’re very prepared to scale,” Burkay states confidently. And with good reason. FAL’s infrastructure is already built for the “ChatGPT moment for video”—a tipping point they believe is imminent.
“I don’t think we’ve hit it yet… but if you look at your Instagram or TikTok feed, a third—or even half—of the videos are already AI-generated.”
From synthetic influencers to dynamic ad creatives, AI-generated video is spreading rapidly, albeit in “slow motion.” But Burkay predicts a breakthrough is coming—possibly this year—with models capable of real-time editing, character interaction, and cinematic-quality output.
When that happens, FAL wants to be the default platform for every developer building in this space.
Beyond Hype: The Realities of Building in AI
One of FAL’s most refreshing traits is its anti-hype stance. In an industry rife with cherry-picked demos and inflated claims, the team takes a rigorously empirical approach.
“There’s a lot of cherry picking happening in models… We take the model, run it ourselves, and test whether it actually does what it promises.”
Only after validation do they optimize it for speed, cost, and reliability. This commitment to real-world performance over marketing fluff has earned FAL trust among serious builders—not just hobbyists.
Moreover, FAL champions day-zero monetization—a philosophy increasingly vital in today’s capital-conscious climate.
“With AI, people are willing to pay right away… Monetization should be a priority from day zero.”
Unlike Web 2.0 startups that chased user growth for years before considering revenue, AI-native companies like FAL can—and must—validate demand immediately through paying customers. This creates a tighter feedback loop, faster iteration, and healthier unit economics.
Culture, Speed, and the Power of Small Teams
For the first two years, FAL operated with just six people. Burkay believes this was essential.
“You want the smallest team that can write, experiment, and make decisions fast.”
Speed isn’t just a metric—it’s a core value. “Think of moving fast, take that and multiply it by 100,” he urges. In a field where models evolve weekly, hesitation is fatal. But FAL embraces reversibility: “If we’re wrong, we can always revisit our decision.”
This agility, combined with deep technical specialization, allows FAL to ship “day zero releases”—making new models available to developers the moment they drop.
Their ultimate vision? To become the infrastructure layer for the next generation of creative AI.
“We want all the builders using this technology to do it through FAL.”
The Future: Real-Time Generative Media Is Coming
Looking ahead, FAL is preparing for a world where video generation happens in seconds—not minutes. Their infrastructure is being architected for real-time interactivity, where users can tweak scenes, direct AI characters, and render studio-quality content on demand.
This isn’t science fiction. Tools like Sora, Runway ML, and Pika are already pushing boundaries. But without robust, scalable backends, these innovations remain confined to demos.
FAL aims to change that.
By abstracting away the complexity of model hosting, optimization, and scaling, they’re empowering developers to focus on what they do best: creating.
Final Thoughts: A Startup Story Rooted in Purpose
FAL’s rise isn’t just about technology—it’s about timing, focus, and founder-market fit. Burkay Gür didn’t chase trends; he leaned into his passions (AI + creativity) and solved a painful, overlooked problem.
His immigrant experience taught him resilience. His time at Coinbase showed him the power of mission-driven teams. And his love for generative AI fuels his daily drive.
“If I wasn’t doing this, I’d probably be playing with these models myself. I love this technology… It’s literally the most fun thing I could be doing.”
In an era of AI fatigue and skepticism, stories like FAL’s remind us that real innovation still happens at the edges—in niches that grow explosively, in teams that move relentlessly, and in founders who build not for exits, but for impact.
The ChatGPT moment for video may not have arrived yet—but thanks to platforms like FAL, it’s closer than ever.
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