Google Cloud.
Containers, managed data and serverless services on Google Cloud, set up in code.
Talk to us about Google Cloud
Key features
What Google Cloud does well.
BigQuery for analytics
A serverless data warehouse that queries large datasets with SQL, with no cluster to size or run.
GKE Autopilot
Managed Kubernetes where Google runs the nodes and you pay for the resources your pods request.
Gemini Enterprise Agent Platform
Formerly Vertex AI, it gives access to Gemini and other models, with tools for building and evaluating agents.
Project-level IAM
Resources sit in projects and folders, and IAM roles granted at each level pass down to what sits below.
What we build with it
Containers without the cluster work.
Google Cloud’s managed services make it straightforward to run containers and analyse data without operating servers. We use it when a product fits its container and data services, or when your organisation already works with Google Workspace and Google Cloud.
Containerised services
APIs and workers on Cloud Run, scaling with demand from a container image.
Analytics
Reporting data in BigQuery, fed from the product database and other systems.
Managed databases
PostgreSQL on Cloud SQL with backups, replicas and access rules.
When it’s the right choice
Google Cloud suits container-based services and analytics-heavy products, and organisations already using Google Workspace or Google Cloud.
When we’d pick something else
If your team already runs most of its systems on AWS or Azure, adding a second provider brings more accounts and tooling to manage. We would weigh that first.
Our Google Cloud services
How we can help.
Architecture design
We choose between Cloud Run, GKE and managed databases based on how the service runs and scales.
Data and reporting setup
We load product data into BigQuery and set up the tables and access that reporting depends on.
Service migration
We move services from other clouds or servers, running old and new side by side until the move is checked.
Access and cost review
We review IAM grants, service accounts, budgets and usage, then list the changes in order of priority.
How we work
Starting a Google Cloud project.
Review what exists
Code, data, hosting and the people who maintain it, before any Google Cloud decision is made.
Agree the approach
A written scope: what changes, what stays, the risks and how we will know it worked.
Build in reviewable slices
Working increments your team can test, rather than one large delivery at the end.
Hand over or stay on
Documentation and access for your team, or a defined support scope if you want us to keep going.
Where AI fits
AI in cloud & infrastructure work.
Models inside your cloud account
Amazon Bedrock, Azure and Google’s Gemini Enterprise Agent Platform give model access under your own account’s access, logging and billing controls.
AI workflow automation ↗Region and residency checks
Model availability differs by region, so we check where prompts and documents will be processed before choosing a service.
Cloud architecture ↗
Where it stops: Turning on a model service does not make a use compliant. Data protection and sector rules still apply to every prompt and output.
What’s changing
Shifts to plan for.
Dates that affect Google Cloud projects, checked against the official sources.
April 2026
Vertex AI becomes Agent Platform
Google renamed Vertex AI to Gemini Enterprise Agent Platform. Existing workloads carry on, but console names and documentation changed.
Where it fits
Related expertise.
- Cloud architecture Choose and structure cloud services around your product’s data, access and operating needs.
- Infrastructure as code Define environments in version-controlled code, so changes are reviewed and drift is easy to spot.
- CI/CD pipelines Automated builds, tests and deployments, so every release follows the same checked path.
Common questions
Google Cloud questions.
Can you move a service from another cloud to Google Cloud?
Often, especially if it already runs in containers. We map its dependencies, plan the data move and run both until the new setup is checked.
How do you control costs on Google Cloud?
We set budgets and alerts, choose services that scale down when idle where that suits, and review usage once real traffic arrives.
Further reading
- A database for every pull request: preview environments that work How to give each pull request its own database branch, with safe data, tested migrations and automatic clean-up.
- MySQL 8.0 has reached end of life: planning the move to 8.4 What MySQL 8.0’s end of life means, and how to plan, test and roll back an upgrade to MySQL 8.4 LTS.
- One database per tenant: isolating customer data in SaaS When each customer gets its own PostgreSQL database, and what that means for migrations, connections and running costs.
