Google Cloud Platform Services
GCP architecture for data, analytics, and machine learning workloads, plus Kubernetes on the platform that originated it.
- 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
- Google Cloud
- BigQuery
- GKE
- Vertex AI
- Terraform
- Per-query
- Analytics pricing model
- GKE
- Mature managed Kubernetes
- IaC
- Terraform-defined
What google cloud platform covers
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 we deliver
Key features and capabilities of our google cloud platform work.
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.
How the engagement runs
A clear sequence with a visible output at every step — so you always know where the project stands.
- 01
Discovery & scoping
We map requirements, users, integrations, and constraints, then agree scope and a fixed quote before work starts.
- 02
Architecture & design
Technical architecture and user flows are agreed and prototyped before implementation begins.
- 03
Build & review
Development in reviewable increments, with a staging environment you can see and comment on throughout.
- 04
Launch & support
Performance and security checks, documented handover, deployment, and an agreed support window after go-live.
Technologies & tools
Chosen per project for fit, maintainability, and your existing stack.
- Google Cloud
- BigQuery
- GKE
- Vertex AI
- Terraform
Industry applications
Sectors where we most often apply this service.
- E-Commerce & Retail
- SaaS & Technology
- Healthcare
- Real Estate
- Finance & FinTech
- Education
- Travel & Hospitality
- Professional Services
What's included at each level
Every engagement is scoped to your needs; these tiers show how scope typically grows.
| 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 |
Google Cloud Platform: common questions
Is BigQuery cost-effective?
For large datasets with intermittent analytical load, usually yes — you are not paying for idle warehouse capacity. Costs escalate with unpartitioned tables and unbounded queries, so we implement partitioning and query controls as part of the build.
Why GCP over AWS for data work?
BigQuery and Vertex AI are genuinely strong, and for data-heavy or ML-heavy workloads the tooling is more integrated. For general application hosting the difference is much smaller, and team familiarity matters more.
More from Cloud & DevOps Solutions
Ready to talk about google cloud platform?
Tell us what you need. We reply within one business day with questions or a call slot — and a fixed, written proposal after.