Google Cloud, for the workloads it actually wins at.
Data and ML-heavy workloads, Kubernetes-native architectures — the cases where GCP's specific strengths are a real advantage over the alternatives.
Why teams choose Google Cloud — and when we agree.
Strong fit for data & ML workloads
BigQuery and Vertex AI are genuinely strong choices when your system's core value is in data or machine learning.
Kubernetes-native by heritage
GKE benefits from Google's own Kubernetes lineage — a real edge for container-native architectures.
Infrastructure as code
Terraform-managed environments, consistent with how we approach every cloud provider.
Honest comparison with AWS/Azure
We'll tell you directly when GCP isn't the better fit for your specific workload.
Google Cloud work we actually ship.
Cloud architecture & migration
Moving workloads onto GCP or designing new infrastructure around its specific strengths.
BigQuery & data pipeline development
Data warehousing and analytics pipelines built for real query volume.
GKE container deployments
Kubernetes-native architectures taking advantage of GCP's container tooling.
Vertex AI / ML infrastructure
Model training and serving infrastructure for applied ML workloads.
What clients ask before hiring us for Google Cloud.
It can be, but AWS or Azure often have larger ecosystems for general-purpose hosting. GCP earns its place specifically on data and ML-heavy workloads — we'll tell you which camp your project is in.
Yes, though we'll first confirm the migration is justified by a real technical or cost advantage rather than recommending it by default.
Got a Google Cloud project in mind?
A 30-minute call with a principal engineer — no salespeople, no slide decks. You will leave with a written perspective on your plan whether we end up working together or not.