For a startup helping foundation models get better at real-world tasks through reinforcement learning, infrastructure flexibility isn't optional—it's existential. HUD builds "gyms" where AI models train on thousands of simulated environments, from using Railway's API to navigating enterprise software, serving everyone from frontier AI labs to academic researchers.

When co-founder Parth Patel launched HUD a year ago, the technical requirements were daunting: handle massive burst traffic during training cycles, support thousands of parallel rollouts, and minimize latency since customers pay $100+ per hour for GPU compute.

"We have burst traffic where I don't need 32 instances active at all times. Most of them are unused, and then when my burst traffic comes, even those 32 might not be enough."

The challenge went beyond scale. HUD needed infrastructure manageable by a three-person founding team while serving enterprise customers demanding SOC 2 compliance. Every millisecond of round-trip time between their API layer and training infrastructure translated directly to customer costs.

"A training run has 1,400 rollouts, and on each you have 15 steps. The round trip latency goes from rollout service to Railway instance, to Kubernetes pod, to Railway instance, to the other server, to the database. Everywhere we can cut latency is hundreds of dollars saved for my customers."

Traditional infrastructure would require dedicated DevOps resources they didn't have. But choosing a limited platform risked hitting scaling walls just as they landed enterprise deals.

Patel chose Railway based on years of personal experience—including a side project that hit #1 on the App Store with 250,000 users in a single day, all running on Railway.

"I'm a pretty big Railway maximalist. I've evangelized the product quite a lot."

HUD deployed all their backend services on Railway, leveraging the platform's simplicity to move fast without infrastructure overhead. The deployment model matched their workflow perfectly: push code, deploy instantly, scale with clicks.

Railway's straightforward nature meant the entire team could contribute without deep infrastructure knowledge. Anyone could look at logs, modify environment variables, or increase instances—critical for a lean startup.

"Railway is a pretty straightforward product. Anyone on the team can realistically use Railway—look at logs, modify environment variables, increase the number of instances."

When Patel's App Store app exploded with quarter-million users in one day, Railway handled it effortlessly—proving the platform could handle HUD's burst traffic needs.

"I just spun up eight parallel instances, maxed out the VCPUs and RAM, and that was it. It handled it like a breeze."

The platform's replica scaling provided immediate relief during training runs, even if not perfectly optimized for extreme burst patterns. While Patel considers specialized solutions for certain workloads, Railway remains the foundation.

I'd much rather focus on delivering customer outcomes than managing Helm charts and dealing with infra nonsense."

Railway enabled HUD to scale from three founders to serving major AI labs and enterprises in just one year, achieving remarkable growth while maintaining a lean infrastructure footprint.

• Zero to enterprise-ready in 12 months, starting with just three co-founders and scaling to serve frontier AI labs, academic researchers, and enterprise customers with SOC 2 compliance.
• 20 million requests handled daily at peak during training cycles, with 10 million on average days, all managed by just three people who touch the infrastructure.
• 100% focus on product development instead of infrastructure, with the founding team free to build training infrastructure for next-generation AI models.
• Enterprise deals closed with SOC 2 compliance achieved while running entirely on Railway's platform.

The simplicity freed the founding team to focus on what matters: building the training infrastructure for artificial intelligence.

"I'm a pretty smooth brain guy when it comes to infra. Offloading that responsibility, especially when we started scaling—we're not spending time rolling out infrastructure and managing it. It's just not the highest EV thing for us to do."

Looking forward, HUD continues to expand their customer base across enterprises, labs, and academic institutions, all while maintaining their lean infrastructure approach on Railway.

"We work with enterprises, labs, academic researchers, and startups. Our throughput is much higher, our overall stack is much more mature."

For a company building training infrastructure for next-generation AI models, Railway provided the perfect balance: simple enough for three founders to manage, powerful enough to serve the world's most advanced AI labs building the future of artificial intelligence.