Google Cloud infrastructure for products that grow fast
Google Cloud is at its best when you lean on what it does well: containers that scale from zero on Cloud Run, Kubernetes that mostly runs itself on GKE, and BigQuery for data that does not fit on one machine. We build GCP environments around those strengths, with a clean project hierarchy, keyless access and every resource in Terraform, so the platform grows with your traffic and your team.
From the first project to petabytes
New environments, takeovers of existing ones, and migrations from AWS, other hosts or your own servers.
Organization & Projects
Folders per environment or team, projects per workload, organization policies as guardrails, and audit logs exported to a locked project.
Networking
Shared VPC owned by a host project, Private Service Connect to managed services, Cloud NAT, and firewall policies defined once at the organization level.
Compute
Cloud Run services and jobs, GKE Autopilot or Standard for larger platforms, Compute Engine managed instance groups with Spot VMs where interruptions are fine.
Databases
Cloud SQL with high availability and read replicas, AlloyDB for heavier PostgreSQL, Spanner for global consistency, Firestore for app data, Memorystore for caching.
Data & Events
Pub/Sub for decoupled services, Dataflow for streaming and batch, BigQuery partitioned and clustered for analytics, Cloud Storage with lifecycle rules.
Edge & Delivery
Global external load balancing with a single anycast IP, Cloud CDN for static and cacheable content, Cloud Armor rules against abuse and bots.
Identity & Security
Service accounts per workload, Workload Identity Federation instead of key files, Secret Manager, customer-managed encryption keys where required, Security Command Center findings triaged.
CI/CD & Observability
Cloud Build or GitHub Actions into Artifact Registry, Cloud Deploy for progressive rollouts, Cloud Monitoring and Logging with SLOs and alerting that respects sleep.
Cost Optimization
Billing export to BigQuery, labels on everything, committed use discounts, rightsizing recommendations applied, and budgets with alerts per project.
The Google Cloud calls we make most often
The decisions that come up on almost every GCP project, and how we usually make them.
| Decision | Our default | When we choose otherwise |
|---|---|---|
| Running services | Cloud Run: containers that scale to zero and to thousands of instances, no cluster to run. | GKE for many services, sidecars, GPUs or Kubernetes tooling; Compute Engine for software that needs a host. |
| Relational database | Cloud SQL for PostgreSQL with high availability and point-in-time recovery. | AlloyDB for heavy read or mixed analytical load; Spanner for multi-region writes at very large scale. |
| Async work | Pub/Sub with dead-letter topics and push to Cloud Run. | Cloud Tasks for rate-limited calls to one target; Workflows when steps must run in order with retries. |
| Analytics | BigQuery with partitioned, clustered tables and query cost limits. | PostgreSQL when the data is small and queried by the app itself. |
| Access for CI and other clouds | Workload Identity Federation: short-lived tokens, no key files. | Practically never. Exported service account keys are the first thing we remove. |
Moving to GCP one reversible step at a time
From AWS, a hosting provider or your own servers, with no big-bang weekend.
Map
Each workload matched to its Google Cloud equivalent, with the dependencies, data volumes and costs on both sides written down.
Foundation
Organization, folders, projects, Shared VPC, identity and logging built in Terraform before the first workload moves.
Replicate & run in parallel
Databases replicated with Database Migration Service or native tools, services running on both sides, cutover rehearsed on staging.
Shift & retire
Traffic moved in steps, rollback ready until the numbers hold, then the old environment switched off and its bill with it.
Google Cloud questions, answered
What teams ask before they hand us their GCP organization.
Cloud Run or GKE?
Cloud Run first for most web services, APIs and workers: containers that scale from zero to thousands of instances with no cluster to operate. GKE when you run many services with shared networking and policies, need sidecars, GPUs or custom scheduling, or want Kubernetes tooling. GKE Autopilot removes most node management; Standard gives full control when you need it. More on our Kubernetes and platform engineering page.
Which Google Cloud database should we use?
Cloud SQL for PostgreSQL or MySQL covers most products. AlloyDB when PostgreSQL needs more throughput or analytical queries on live data. Spanner when you need relational consistency across regions at very large scale. Firestore for document data in mobile and web apps, Memorystore for caching. We choose on access patterns and growth, not on what is newest.
How do you set up projects and access on Google Cloud?
An organization with folders per environment or team, separate projects per workload and environment, a Shared VPC owned by a host project, and organization policies as guardrails. People get access through Google groups, services through dedicated service accounts, and CI and other clouds through Workload Identity Federation, so no service account keys sit in files.
Can you build data pipelines into BigQuery?
Yes. Events go into Pub/Sub, Dataflow or lighter Cloud Run jobs transform them, and BigQuery stores them partitioned and clustered so queries stay fast and cheap. Batch loads come from Cloud Storage on a schedule. We add data quality checks and cost controls on queries, because an unbounded BigQuery scan is the quickest way to a surprising bill.
Can you migrate us to Google Cloud from AWS or our own servers?
Yes. We map each workload to its Google Cloud equivalent, build the target as code, replicate databases with Database Migration Service or native replication, run both sides in parallel and move traffic in steps. Rollback stays ready until the numbers hold, and the old environment is switched off only after that.
How do you reduce a Google Cloud bill?
Billing export to BigQuery with labels per service and team, then the usual wins: rightsizing with the recommender, committed use discounts for steady load, Spot VMs for interruptible work, Cloud Run scaling to zero, Cloud Storage lifecycle rules, and limits on BigQuery bytes scanned. Budgets with alerts keep it there.
Is your Google Cloud ready for ten times the traffic?
Tell us what you run and what worries you. We review the organization, load-test what matters, and give you a fixed quote for what to fix first.
Get a fixed quote