Designing and deploying AI infrastructure in the cloud is no longer a niche challenge. Developers, startups, and enterprises all face the same ques...
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Why even bother with RunPod or CoreWeave when AWS gives you everything in one place?
If you’re fine with hyperscaler pricing and lock-in, then sure, AWS covers it all. But once workloads scale, specialist GPU clouds can cut costs by 30–50%. For teams with budget pressure, that difference matters.
On-prem is still the only sane option for regulated industries. Clouds change APIs every year.
On-prem makes sense for some, but it’s not always realistic. Hardware refresh, cooling, and ops staff add up fast. For many, a private cloud setup with strict networking and customer-managed keys achieves compliance without owning racks.
I get that, but regulators don’t care about “customer-managed keys” if the infrastructure is still outside your control. Once auditors step in, they’ll push for physical data residency. How do you convince them a GPU cloud is compliant?
That’s exactly where governance comes in. You need documented controls: where data is stored, how it’s encrypted, who has access, and how logs prove that. In practice, we’ve seen regulators accept GPU cloud setups if workloads run in-region, data never leaves the VPC, and compliance frameworks (ISO, SOC, GDPR) are mapped. It’s not trivial, but it’s possible with the right architecture.
Our team started with managed models on Vertex AI, then moved some heavy batch jobs to a GPU cloud. The hybrid approach really does make sense once traffic grows.
That’s the sweet spot: start managed, then offload heavy jobs where it’s cheaper. Keeps both compliance and cost under control.
Great article, thank you!
You're welcome!
We tested L40S for background jobs and it was perfect. Way cheaper than H100s for workloads that don’t need low latency.
Exactly!! not every task needs the top GPU. Mixing tiers is one of the simplest ways to save costs without hurting performance where it matters.