When we reach for it
Analytics-heavy platforms and machine-learning workloads, and teams who want managed Kubernetes with the least operational friction.
Google Cloud engineering
Strongest where data and machine learning are the workload, with BigQuery and a Kubernetes service run by the people who wrote Kubernetes.
What we reach for first
When we would argue against it
The enterprise service catalogue is narrower than AWS’s, and that occasionally shows up late in a migration. We check the specific services a workload needs before recommending the platform, not after.
What it looks like in delivery
Typically chosen for one decisive reason — usually the data warehouse — with the rest of the estate following it rather than the other way round.
Where this appears on the site
Nothing on this site names it yet
We work in Google Cloud, and no case study or service page currently published on this site prints it in its stack. Rather than describe an engagement you cannot check, this space stays empty until one does. Ask us and we will talk you through it directly.
Also in cloud and infrastructure
Working in Google Cloud?
Tell us what it is running, what it costs you today, and what you need it to do next. A senior engineer will tell you what we would keep and what we would change.