Google Cloud vs AWS: Why We Chose GCP
AWS is the safe default choice. But if you're building something with AI, using AWS today is like trying to run F1 with a truck engine.
Equipe Técnica
Engenharia
Choosing Google Cloud over AWS is a strategic infrastructure shift driven by the demand for AI innovation speed, on-demand TPU access, and automated Kubernetes management. For enterprises building with AI, this transition eliminates GPU bottlenecks to drastically reduce model training times and operational costs.
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:
4h (AWS) → 1.5h (GCP)
US$ 22 → US$ 8
TPU v5e available on demand
The difference is not marginal. It's exponential.
Both paths, side by side
🟠 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.
🟢 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.
Verdict
If you're building the future — with microservices, serverless and generative AI — Google Cloud is no longer the "alternative". It's the standard.
Frequently Asked Questions sobre Google Cloud vs AWS: Why We Chose GCP
Why did the company migrate from AWS to Google Cloud? The migration to Google Cloud was motivated by the speed of innovation in AI, not by price.
What are the advantages of Google Cloud’s TPUs over AWS’s GPUs for AI? Google Cloud’s TPUs (Tensor Processing Units) are processors specifically designed for AI matrices, offering better performance and availability compared to AWS’s GPUs.
What is GKE Autopilot and how does it compare to AWS’s EKS? Google Cloud’s GKE Autopilot offers greater automation compared to AWS’s EKS, allowing users to deploy containers with less configuration and more focus on code.
What are the benefits of the Google Cloud startup credits program? The Google Cloud startup program offers credits that can cover infrastructure for up to 2 years, allowing startups to experiment and test larger models without worrying about costs.
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.