Google Cloud Platform
GCP architecture for data, analytics, and machine learning workloads, plus Kubernetes on the platform that originated it.
- Per-query
- Analytics pricing model
- GKE
- Mature managed Kubernetes
- IaC
- Terraform-defined
About google cloud platform
GCP’s strongest arguments are data and containers. BigQuery is exceptional for analytical workloads, and GKE remains the most mature managed Kubernetes offering.
BigQuery for analytics at scale
BigQuery separates storage from compute and prices per query, which suits organisations with large datasets and intermittent analytical load far better than an always-on warehouse. Poorly written queries can get expensive, so we implement partitioning, clustering, and cost controls alongside.
GKE for container workloads
Kubernetes originated at Google and GKE reflects that maturity, particularly around autoscaling and upgrades. We still only recommend Kubernetes where the operational overhead is justified by scale or workload complexity.
Vertex AI for ML operations
Where you are running machine learning in production, Vertex AI provides training, serving, and monitoring in one platform, which reduces the amount of MLOps plumbing you have to build and maintain yourself.
What the engagement includes
BigQuery data platform
Partitioning, clustering, and cost controls alongside the warehouse.
GKE Kubernetes
Managed clusters with autoscaling and upgrade strategy.
Vertex AI
Model training, serving, and monitoring in one platform.
Infrastructure as code
Terraform-defined projects, networking, and IAM.
What you get
The measurable results this service is accountable for.
- Analytics at scale without an always-on warehouse
- Mature managed Kubernetes with strong autoscaling
- Integrated ML training, serving, and monitoring
- Query cost controls implemented from day one
- Environments defined in Terraform
A process without surprises
Clear checkpoints at every stage, so you always know what is shipping and when.
- 1
Discovery & scoping
We map requirements, users, integrations, and constraints, then agree scope and a fixed quote before work starts.
- 2
Architecture & design
Technical architecture and user flows are agreed and prototyped before implementation begins.
- 3
Build & review
Development in reviewable increments, with a staging environment you can see and comment on throughout.
- 4
Launch & support
Performance and security checks, documented handover, deployment, and an agreed support window after go-live.
What is included at each tier
Engagements scale with your stage. Every tier includes everything below it.
| What's included | Starter | Growth | Enterprise |
|---|---|---|---|
| GCP architecture review | Included | Included | Included |
| Terraform project setup | Included | Included | Included |
| BigQuery data platform | Not included | Included | Included |
| GKE cluster setup | Not included | Included | Included |
| Vertex AI pipelines | Not included | Not included | Included |
Sectors we work in
We recommend GCP where its data and container strengths genuinely apply, and put query cost controls in place from the start.
Common questions
Is BigQuery cost-effective?
Why GCP over AWS for data work?
More cloud & devops solutions services
Ready to talk about google cloud platform?
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