What Google Cloud is
Google's rented computing. Smaller than AWS and Azure in breadth, and stronger in two specific areas: data analysis and containers, both of which come out of how Google runs its own systems.
technologies / google cloud
Google Cloud where the data tools decide it.
Chosen when the analytics and data work are the centre of the project, or when your organisation already runs on Google Workspace.
why google cloud
When we suggest Google Cloud.
It's the one we recommend for a specific reason rather than by default.
Data
Analytics at scale
BigQuery handles reporting over large datasets without you running a database cluster to do it.
Containers
Straightforward deploys
Cloud Run takes a container and serves it, scaling to zero, which suits services with uneven traffic.
Identity
Workspace sign-in
If your staff already sign in with Google, internal tools get access control almost free.
Networking
Fast and global
A strong global network, which shows up as latency for users far from your servers.
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What we build with Google Cloud
Data pipelines and warehousing
BigQuery is the reason most of our Google Cloud work exists. Getting your data into it and keeping it current is the job.
Analytics that a person can actually use
A warehouse plus something to look at it with, so the numbers arrive in front of the people who make decisions rather than in a query console.
Containers on managed Kubernetes
Google's Kubernetes has been the most polished for years. Worth it when you genuinely need Kubernetes and overkill when you don't.
Machine-learning features in a product
Vision, language and translation as an API, wired into an existing application rather than built from scratch.
Serverless for spiky workloads
Cloud Run for services that are idle most of the day and busy occasionally, where paying per request beats paying per hour.
the essentials
What Google Cloud actually is.
Written for someone deciding, not for someone who already knows. Skip it if you do.
Why anyone picks it
BigQuery, usually. It answers questions over enormous datasets in seconds with no cluster to manage, and there's no straightforward equivalent elsewhere. If your problem is analytical, that one service can decide the platform.
The other reason
Kubernetes. Google originated it, and their managed version is the least painful way to run it. That matters only if you actually need Kubernetes, which fewer teams do than believe they do.
Where it's weaker
Fewer services, a smaller partner ecosystem, and a reputation for retiring products that makes some organisations wary. Worth weighing if you want a single provider for everything rather than the best tool for one job.
Data
Reports are assembled by hand every month
The clearest case for a warehouse. The work usually pays for itself in recovered hours before it pays for itself in insight.
Cost
A BigQuery bill that surprises you
Almost always queries scanning far more than they need. Partitioning and clustering fix it and take days, not weeks.
Complexity
Kubernetes for three services
A common over-reach. Cloud Run does the same job with a fraction of the operational burden, and we'll say when that's the honest answer.
questions
Straight answers.
Why would we choose Google Cloud?
Mainly for data. If reporting over large volumes is central, BigQuery is a genuine advantage. If your staff already use Google Workspace, internal access is simpler too.
Is it as mature as AWS?
For the services most projects need, yes. AWS has a wider catalogue, though most of that catalogue is irrelevant to any single project.
Can you split across clouds?
We can, and usually advise against it. Two clouds means two sets of everything to operate, and the resilience gained is rarely worth it below a certain size.
Should we use Google Cloud for everything?
Only if data analysis is central to what you do. Otherwise use it for the part it's best at and host the application wherever is cheapest and simplest.
Is BigQuery expensive?
It can be, and it's nearly always the query rather than the storage. Structured properly, most workloads cost far less than people expect.
Tell us what you want to build or improve.
A few lines are enough to start. You get a reply within two working days.