Cloud
Google Cloud for data-led workloads
Google Cloud project structure, migration, data platform and cost management.
The problem
Google Cloud is frequently chosen for a specific capability — usually data analytics or Kubernetes — and then run without the project structure, IAM design or billing controls that keep it manageable as usage grows beyond the initial team.
How we approach it
We build the organisation, folder and project hierarchy to match your accountability model, establish IAM at the right granularity, and put billing export and budget controls in place before consumption scales.
For analytics workloads specifically, we focus on the query and storage patterns that drive cost, because BigQuery bills behave very differently from virtual machine bills and the intuitions do not transfer.
What you get
- Organisation, folder and project hierarchy matched to your structure
- IAM designed at appropriate granularity rather than broad roles
- Billing export and budget alerting
- BigQuery cost patterns reviewed against actual query behaviour
- Committed use discount analysis
- Kubernetes platform design where containers are in scope
How the engagement runs
The sequence, and why each stage comes where it does.
Workload review
Current or intended workloads, data volumes and access patterns.
Foundation
Organisation hierarchy, IAM, networking and billing controls.
Migration or build
Workloads deployed or migrated with infrastructure as code.
Cost tuning
Query patterns, storage classes and commitment purchasing reviewed against measured usage.
Technology we work with
Named so you can check the fit against your existing estate.
- Google Cloud
- BigQuery
- Google Kubernetes Engine
- Cloud IAM
- Terraform
Frequently asked questions
Is Google Cloud cheaper than the alternatives?
Talk to us about google cloud
Tell us where you are now and what you are trying to reach. We will scope it honestly, including whether this is the engagement you actually need.