Choosing Between AWS, Azure, and GCP: A Founder's Practical Framework
I get asked this question by founders more than almost anything else: “Which cloud should we build on?” The honest answer is that the feature comparison matrices rarely matter as much as three things nobody puts in a slide deck: where your compliance requirements point you, where your team’s existing muscle memory is, and where your actual workload shape fits best.
Start with compliance, not preference. Our healthcare imaging platform runs on Azure, not because Azure is “better” in the abstract, but because the DICOM/MRI processing pipeline needed private endpoints and healthcare-grade security postures that Azure’s tooling made straightforward to implement and, critically, straightforward to audit. If your industry has a dominant cloud for compliance reasons, that decision is often made before you open a pricing calculator.
AWS wins when your workload is unpredictable and queue-heavy. Our messaging infrastructure — the one moving 5M+ texts a month — lives on AWS because nothing else matches its maturity in the specific combination we needed: EC2 auto-scaling, SQS-style queueing patterns, and a decade of battle-tested networking primitives for exactly this kind of bursty, high-throughput traffic. When in doubt on general-purpose SaaS infrastructure, AWS’s breadth means you’re rarely fighting the platform.
GCP earns its place around data and AI tooling. When a project leans heavily on data pipelines, BigQuery-style analytics, or certain ML tooling, GCP’s developer experience in that specific lane is genuinely ahead. We don’t default to GCP, but we don’t rule it out either when the workload is data-shaped rather than transaction-shaped.
Multi-cloud is a tax you should only pay on purpose. I’ve seen founders adopt “multi-cloud strategy” as a badge of sophistication rather than a response to an actual requirement. Running production workloads across AWS, Azure, and GCP simultaneously — which we do at NerdDevs — only makes sense because each client engagement brought its own constraint that pointed to a specific cloud. It’s not a strategy we’d recommend defaulting into; it’s operational overhead you should only carry when the alternative (forcing every workload onto one cloud) costs you more.
Your team’s existing depth is a legitimate input, not a cop-out. A team that knows AWS IAM cold will ship a secure system on AWS faster than an equally skilled team learning Azure’s permission model from scratch under deadline pressure. Migration cost is real. Don’t let a marketing comparison talk you out of the platform your engineers can already operate safely.
The framework, in short: let compliance rule out options first, let workload shape narrow what’s left, and only then let team familiarity make the final call. Everything else is noise.