Google Cloud vs AWS: Why We Chose GCP

TL;DR AWS remains solid for legacy workloads. But for startups building with AI, Google Cloud delivers more accessible TPUs, a more mature Kubernetes, and a startup credits program that buys 2 years of freedom to fail. We migrated 100% in 2025 — and the trigger was speed, not price.

No one gets fired for choosing IBM. And today, no one gets fired for choosing AWS. It's the default cloud, solid, reliable. We used AWS for years. But in 2025, we did the unthinkable for many: we migrated 100% of our infrastructure to Google Cloud.

It wasn't for price (though it got cheaper). It was for AI innovation speed.

The GPU Bottleneck

Try renting an H100 GPU on AWS today. You'll either join a waitlist or pay an absurd spot price. On Google Cloud, TPUs (Tensor Processing Units) aren't just "rented video cards". They're processors designed from scratch for AI matrices.

In our internal benchmarks training an 8B-parameter Llama-3 model:

−62%

Training time
4h (AWS) → 1.5h (GCP)

−64%

Cost per run
US$ 22 → US$ 8

Wait queue
TPU v5e available on demand

The difference is not marginal. It's exponential.

Both paths, side by side

Market standard

🟠 AWS

The "nobody-gets-fired-for-choosing" cloud. Unbeatable for migrating legacy Java, SAP, Oracle. Mature ecosystem, broad certifications, abundant documentation.

GPU H100: waitlist, volatile spot pricing.
EKS requires a dedicated DevOps for the control plane.
Startup credits exist, but are modest.

AI-first

🟢 Google Cloud

Cloud native for AI/ML workloads. TPUs designed for matrices, GKE Autopilot as Kubernetes' home turf, and the most aggressive startup program in the market in 2025–2026.

TPU v5e available on demand, 40% cheaper.
GKE Autopilot: drop in the container, the rest is automatic.
Vertex AI + Gemini integrated into the same billing.

Kubernetes: The Creator's Home

Google invented Kubernetes. And, honestly, you can tell. GKE (Google Kubernetes Engine) is light-years ahead of EKS in terms of automation and "autopilot".

On EKS, we needed a dedicated DevOps engineer just to manage the control plane, updates and nodes. On GKE Autopilot, we literally drop the container in and it runs. Less config, more code.

Startup Credits: The "Nudge"

Let's be honest about money. Google's startup program is aggressive. We received credits that covered our infrastructure for 2 years. That allows you to fail. It lets you test bigger models without fear of the monthly bill.

⚠️ When to stay on AWS If your workload is a 2010 Java monolith with EC2-specific dependencies, migrating to GCP will cost more than the gains. AWS is great for legacy — the mistake is using it as the default for AI greenfield projects.

Verdict

If you're building the future — with microservices, serverless and generative AI — Google Cloud is no longer the "alternative". It's the standard.

Premier Partner Migration

Thinking about leaving AWS for GCP?

We run the cost diagnostic, design the migration route per workload, and plug in Google's startup credits program. No lock-in, no billing surprises.

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