Loading page…
Google Cloud Platform Development
Cloud architecture for workloads that specifically benefit from GCP's data and AI/ML tooling.
Start Your Project
What Google Cloud is
Google Cloud Platform is a major public cloud provider, with particular strength in data analytics (BigQuery) and AI/ML tooling (Vertex AI) compared to the other major clouds. As with Azure, core infrastructure capability is broadly comparable across providers — GCP's differentiators are concentrated in specific areas rather than spread evenly.
It tends to be the right default when a project's workload is genuinely data- or ML-heavy, or when an organisation already has existing GCP investment (BigQuery datasets, an existing Google Workspace identity setup).
Suitability
And when it is not. Choosing the wrong tool costs more than any implementation detail.
Good fit for
- Data-intensive workloads where BigQuery's analytics capability is a direct fit
- AI/ML-heavy projects wanting to use Vertex AI or other GCP-native ML tooling
- Organisations already standardised on Google Workspace or existing GCP infrastructure
Consider alternatives for
- Projects with no particular data/AI workload and no existing GCP dependency, where the choice of provider should be made on its own technical and cost merits rather than a perceived AI-first reputation
In practice
01
Data-intensive applications
Workloads where BigQuery or other GCP-native data tooling is a genuinely better fit than the alternative providers.
02
AI/ML infrastructure
Projects wanting to build on GCP-native ML tooling where that specifically suits the project's requirements.
Considerations
Practical notes from running this in production.
- 01
Our direct production experience is primarily on AWS; GCP engagements are scoped around a project's specific data/AI workload fit and evaluated during discovery, not assumed as a default.
- 02
Service naming and architecture patterns differ enough from AWS that moving between providers needs genuine ramp-up time for a team, not just a mechanical service mapping.
FAQ
When does GCP make more sense than AWS?
When the workload is genuinely data- or ML-heavy and GCP's native tooling (BigQuery, Vertex AI) is a direct fit, or when an organisation already has existing GCP investment. Outside those specific cases, the choice between major cloud providers usually comes down to cost and an organisation's existing operational familiarity rather than a clear technical winner.
How long does a Google Cloud development project take?
It depends on scope. A focused first release is usually weeks rather than months, while larger platforms are delivered in phases. We give a written estimate after a short discovery conversation and agree milestones before work starts.
How is Google Cloud development work priced?
We work either to a fixed scope with agreed milestones or on a time-and-materials basis, whichever suits how well the requirements are known. You receive the estimate and its assumptions in writing before committing.
Who owns the code and the intellectual property?
Ownership terms are agreed in writing before work begins, so there is no ambiguity about what you receive and what you may reuse.
Do you support the product after launch?
Yes. We can continue with maintenance, monitoring and feature work after release, and we scope that support with you rather than leaving you with a handover and nothing else.
By industry
Where we apply Google Cloud. Tiles marked "Guide" open a dedicated write-up; the rest open the industry page.
Let’s talk about your Google Cloud project
Tell us what you are trying to build or fix. We will come back with scope, approach and an honest view on cost and timeline.