GCP gives you strong primitives — GKE, BigQuery, Cloud Run — but a scalable, cost-sane setup doesn't happen by default. We design and run Google Cloud infrastructure with the same engineers who wrote the Terraform, so architecture decisions and day-to-day operations stay connected.
Google Cloud rewards teams that understand its specific services — the wins in cost and reliability come from knowing GCP, not cloud infrastructure in general.
Architecture and operations handled by engineers who hold current Google Cloud certifications, not generalists learning the console as they go.
Committed use discounts, right-sized GKE node pools, and BigQuery query costs reviewed as part of the engagement — not a separate audit you have to ask for.
Terraform-managed environments so your infrastructure is reviewable, reproducible, and not dependent on one engineer's console changes.
Phased moves onto Google Cloud with rollback plans at each stage — no cutover that risks your production traffic.
Cluster architecture, autoscaling, and workload isolation set up for the traffic patterns you actually have, not a default template.
Scoped to what your infrastructure needs — not a fixed package of services you'll pay for and never use.
New GCP environments designed from scratch, or existing ones reviewed for reliability, security, and cost gaps.
Moving workloads from on-prem or another cloud provider onto GCP with a phased, tested cutover.
Kubernetes clusters on Google Cloud — provisioning, autoscaling, CI/CD integration, and ongoing operations.
Line-by-line review of your GCP bill with concrete recommendations, not just a dashboard.
Data warehousing and pipeline architecture on BigQuery and Cloud Storage for teams running analytics at scale.
It depends on your team's existing skills, your data/analytics needs, and where your users are. GCP tends to win on Kubernetes-native workloads and BigQuery-style analytics; AWS has the broadest service catalog; Azure fits well if you're already on Microsoft's stack. We'll walk through your specific requirements rather than default to one.
Yes. We start with an assessment of your current setup, then plan a phased migration — usually lower-risk services first, with rollback points at each stage — rather than a single high-risk cutover.
Yes. Most cost reviews find savings in oversized GKE node pools, unused committed use discounts, and inefficient BigQuery queries. We give you a prioritized list of fixes, and can implement them as part of the engagement.
Yes, that's a core part of our GCP work — cluster architecture, autoscaling, workload isolation, and CI/CD integration for teams running containerized applications on Google Cloud.
Tell us about your current Google Cloud setup — we'll review architecture, cost, and reliability and come back with concrete next steps.