Google Cloud solutions
Strongest where the work is data-heavy or the workload is genuinely intermittent.
Cloud platform
Chosen per project
Maintainable handover
Google Cloud is at its most compelling in two situations. The first is analytics: BigQuery handles very large datasets without a cluster to manage, and charges for the data a query actually scans.
The second is intermittent workloads. Cloud Run scales containers to zero between requests, so an internal tool used twice a day costs almost nothing — a materially different economic model from keeping a server running.
What we build with Google Cloud
Analytics and reporting over large datasets
Containerised services with irregular or bursty traffic
Mobile backends built on Firebase
Machine learning workloads using managed tooling
Where it fits — and where it does not
Good fit when
Analytical queries over large volumes of data
Workloads that are genuinely idle much of the time
Teams comfortable with containers
Products already using Firebase for mobile
Consider something else when
Organisations standardised on Microsoft identity
Steady, predictable load where reserved capacity elsewhere is cheaper
Requirements for a niche service another provider offers
Operational data through to reporting
The pattern Google Cloud handles particularly well: moving transactional data into an analytical store so reporting never competes with the live application for resources.
Application
Transactions written to the operational database.
Ingestion
Scheduled or streamed extraction.
BigQuery
Analytical store, separate from production load.
Transformation
Modelled tables and defined metrics.
Reporting
Dashboards reading from the analytical layer.
How we work with Google Cloud
Containers first
Cloud Run as the default, so scaling to zero is available without operating a cluster.
Data architecture
BigQuery for analytics, deliberately separated from the transactional database.
Cost control
Query cost and concurrency limits set explicitly — scan-based billing punishes careless queries.
Identity and access
Service accounts scoped per workload, with no shared credentials between components.
Our typical Google Cloud setup
| Concern | What we use |
|---|---|
| Compute | Cloud Run for containers, GKE where orchestration is needed |
| Database | Cloud SQL for relational, Firestore for document data |
| Analytics | BigQuery with partitioning and clustering to control cost |
| Mobile | Firebase authentication, messaging and analytics |
| Storage | Cloud Storage with lifecycle rules |
| Monitoring | Cloud Logging and Monitoring with alerting policies |
Frequently asked questions
Analytics at scale through BigQuery, and workloads that are idle much of the time through Cloud Run’s scale-to-zero. Those two are where it is most clearly differentiated from the alternatives.
For mobile apps and smaller products, yes — authentication, push messaging and real-time data are genuinely good. As an application grows, the data layer often warrants moving to something with richer querying.
You are charged for the volume of data each query scans. Partitioning, clustering and avoiding "select everything" queries are what keep it inexpensive — without those, costs can rise quickly.
Topics people search for
Services built with Google Cloud
Cloud Solutions
Hosting designed around what your application actually needs, with the monitoring and backups that make it safe to rely on.
AI/ML Development
Machine learning applied where it measurably beats a simpler solution — and honest advice when it does not.
Mobile App Development
Apps built to be used daily — fast to open, sensible offline and maintainable once they are in the stores.
SaaS Development
Building a software product means building a business system around it too — tenancy, billing, onboarding and support tooling.
Tell us what you are trying to build
Describe the problem in plain language and we will tell you what it would take to solve it — the approach, the moving parts and the sensible order to build them in. No obligation either way.