Google Cloud — what we run on it

01//Work on Google Cloud

• Take the bill back

  01

  Infrastructure cost reduction

  Committed use discounts priced against a month of real usage rather than a sales call, GKE and instance right-sizing off Cloud Monitoring and Recommender data, BigQuery on-demand versus slot commitments decided from query history instead of a guess, and budgets on a billing export that shout before the invoice does.

• Know where it breaks

  02

  Architecture review

  A read of the estate against the Google Cloud Architecture Framework, with Active Assist and Recommender findings re-ranked by what they would cost you if they came true — not by the priority label the console prints.

• Safeguard data & assets

  03

  Security & compliance

  IAM built from roles and groups instead of a project-wide owner handed out on day one, organization policy constraints so the next project starts compliant rather than being fixed later, VPC Service Controls around the data that matters, secrets in Secret Manager instead of pipeline variables, and Security Command Center findings triaged rather than collected.

• Get off what holds you

  04

  Migration & platform exit

  Discovery in Migration Center with dependencies mapped, then the target bill modelled before anything moves: VMware estates into Google Cloud VMware Engine or onto native instances, databases through Database Migration Service — cut over in waves, every one of them with a way back that has been rehearsed.

• Elevate delivery

  05

  Platform build & delivery

  A landing zone in Terraform — projects, folders, org policy, Shared VPC and billing that still make sense on day two. GKE with autoscaling that has been load-tested rather than assumed, pipelines that promote one image out of Artifact Registry instead of rebuilding it per stage, and Cloud Monitoring wired up before the first production deploy, not after the first incident.

• Turn ideas into systems

  06

  AI/ML enablement

  Vertex AI endpoints reachable only from inside your own network, Gemini prompts and context that stay within your perimeter, BigQuery ML where the model belongs next to the data instead of behind an export, cost per request measured, and an evaluation set that tells you when a model quietly stopped working.

• //Compute & containers

  • —Google Kubernetes Engine
  • —Cloud Run
  • —Cloud Functions
  • —Compute Engine
  • —Artifact Registry
  • —Managed instance groups

• //Networking & delivery

  • —Cloud Load Balancing
  • —Cloud Armor
  • —Cloud CDN
  • —Virtual Private Cloud
  • —Private Service Connect
  • —Cloud DNS

• //Data & storage

  • —BigQuery
  • —Cloud SQL
  • —AlloyDB
  • —Cloud Storage
  • —Memorystore
  • —Pub/Sub and Dataflow

• //Identity & security

  • —Cloud IAM
  • —Organization Policy
  • —VPC Service Controls
  • —Secret Manager
  • —Cloud KMS
  • —Security Command Center

• //Delivery & operations

  • —Cloud Build
  • —Cloud Deploy
  • —GitHub Actions
  • —Terraform (google)
  • —Cloud Monitoring
  • —Cloud Logging

• //Migration & AI

  • —Migration Center
  • —Google Cloud VMware Engine
  • —Database Migration Service
  • —Vertex AI
  • —BigQuery ML
  • —Gemini on Vertex AI

03//Our expertise