Platform record
We buildwith AWS andGoogle Cloud
Two platforms, and 4 current certifications held by the engineers who deliver. The person planning your architecture is the person who works in it.
Where each platform carries the work
Working in both means the workload goes where it belongs. This is the reasoning we walk through before any architecture is committed. Where an estate already has justified investment in one platform, we work inside it.
Why each one is placed there
Managed service breadth
AWSPreferred for wide managed-service coverage and mature account isolation.
Google CloudStrong, with particular depth in data and machine learning services.
Analytics at scale
AWSCapable across warehousing and streaming workloads.
Google CloudPreferred where BigQuery removes warehouse operations from the critical path.
Container orchestration
AWSSuitable where the estate is already standardised on AWS networking.
Google CloudPreferred where Kubernetes portability is a primary requirement.
Applied machine learning
AWSUsed for integrated inference inside existing AWS applications.
Google CloudPreferred for model training, tuning, and serving through Vertex AI.
Infrastructure as code
AWSCloudFormation for auditable, repeatable environment definitions.
Google CloudDeployment tooling aligned to the same review and approval discipline.
Compliance boundary control
AWSAccount and organisation boundaries map cleanly to programme separation.
Google CloudProject and folder hierarchy used to the same effect.
In a platform, never captured by it
We work inside both platforms
These are working partnerships, not procurement relationships. We hold current certification in both clouds and build on them daily, so the platform teams and their roadmaps are part of how we plan.
Certified engineers on the work
Architecture and DevOps certifications sit with the engineers doing delivery, so platform decisions are made by the people who will operate the result.
Portability protected by design
Infrastructure as code, container boundaries, and documented interfaces keep the estate reviewable, so a partnership never becomes a dependency you cannot audit.
Amazon Web Services
Enterprise cloud infrastructure
We work in AWS where the priority is breadth of managed services, mature account and identity boundaries, and infrastructure defined as code that auditors can read.
- Certification
- AWS Certified Solutions Architect – Associate
- Certification
- AWS Certified DevOps Engineer – Professional
AWS Lambda · Amazon S3 · Amazon EC2 · AWS CloudFormation · Amazon RDS · Amazon CloudWatch
Google Cloud Platform
Data, analytics, and applied machine learning
We work in Google Cloud where analytics scale, container orchestration, and machine learning tooling carry the weight of the work.
- Certification
- Google Cloud Professional Cloud Architect
- Certification
- Google Cloud Professional Data Engineer
BigQuery · Google Kubernetes Engine · Vertex AI · App Engine · Cloud Functions · Cloud Storage and Bigtable
Capability counts once it ships
Tell us the workload and the constraints around it, and we will say which platform we would put it on and why.