Editorial note:
This customer story has been anonymized to protect their competitive position, but the details are real and increasingly common among AI teams scaling on GPU infrastructure.
One fast-scaling AI platform powered millions of creative media jobs each month.
The product was taking off—but their infrastructure wasn’t keeping up.
They’d architected their system to run across multiple GPU providers for flexibility—but staying ahead of shifting model needs and fluctuating demand meant they often needed fast access to dozens (or more) GPUs on short notice. They still planned to keep working with their existing providers—but when those couldn’t deliver, they needed another option they could actually rely on.
“Some weeks we could get what we needed. Other weeks, nothing was available and everything slowed down.”
Finding the right model — and getting it fast.
We started by helping them benchmark a few GPU models to see what actually worked for their workloads. Their platform wasn’t experimental—it was powering live user jobs at scale. But their workloads were still evolving, and they hadn’t yet landed on the best fit. Their other providers weren’t helping with that part—just quoting prices.
Price-to-performance was key. After testing several options, they landed on 4090s as the best match for their diffusion workloads—delivering the right balance of speed and cost.
The challenge then became getting them. 4090s were scarce and prices were high across the market, but we had the inventory—and got them running in days.
“We didn’t even know which GPUs made the most sense for us. You helped us figure it out quickly and actually had them available when we needed them.”
Why they stayed.
Other providers remained part of their stack. Like most fast-scaling AI teams, they had built their system to run across multiple cloud GPU providers. They’d architected their infrastructure to be provider-agnostic—able to shift workloads based on availability and pricing.
But when timelines got tight or usage spiked unexpectedly, they stopped waiting for answers elsewhere. Instead of refreshing dashboards or filing support tickets, they came straight to us.
They kept coming back because we made it easier to move fast—and easier to get it right.
Since that first engagement, they’ve reserved hundreds of GPUs through the platform. They landed on 4090s after benchmarking, and over time, their total usage grew to more than 700 GPUs. For a lean team under constant pressure to deliver, getting fast, honest answers—and hands-on help—made the difference.
“Other providers were fine until we needed something fast. You were the only one who actually had it.”
If this sounds familiar, let’s talk.
We work with AI teams building everything from generative tools to custom inference pipelines. Most already use other providers—and that’s normal. In this industry, supply constraints and week-to-week variability mean no one provider can always deliver.
So when credits run out, demand spikes, or supply dries up, teams like this one don’t wait around. They call us.
The first thing we do is get clear on what you need—model, region, timing, budget. Then we give you an honest answer. Usually within 48 hours. Either we can get you what you need, or we’ll tell you straight if it’s not available.
“You helped us figure out what GPUs actually made sense—and then had them ready.”
For this team, that meant 4090s. For others, it might be 5090s or H100s. The key is fit: performance, price, readiness. We’ve benchmarked options, helped teams navigate tradeoffs, and supported migrations when software wasn’t ready.
Some teams use us for overflow. Some for experimentation. Some for scale. What they all get is speed, clarity, and support they can count on.
Book a 15-minute consult and we’ll walk through your model, your priorities, and what’s available right now.
