Google Cloud is the cloud data teams choose: BigQuery for analytics at any scale, Kubernetes as Google runs it, Vertex AI for models, and a developer experience that gets out of the way. It is the right platform when the product is the data — and a fine one for everything around it.
Our Google Cloud consulting services cover foundations and migration, data platforms on BigQuery, GKE and Cloud Run for services, AI on Vertex, and the security, operations and FinOps that make it a platform rather than a project.
Let’s talk Google CloudEngineering services
From an organisation-level foundation to the BigQuery platform your analysts live in, our Google Cloud consulting services cover the data-first cloud end to end.
01
Organisation, folders and projects designed with IAM, VPC Service Controls and shared networking before the first workload, then migrations in waves — with database migration services, rehearsed cutovers and rollback — so the business runs on the old environment until each wave is proven.
02
Warehouses and lakehouses on BigQuery with Dataflow and Dataform pipelines, streaming through Pub/Sub, governance with Dataplex and cost controls on slots and storage — the analytics platform that scales to any question without a re-architecture.
03
Cloud Run for services that should scale to zero and need no cluster, GKE Autopilot where a Kubernetes platform earns its keep — with Cloud Build or GitHub Actions pipelines, Artifact Registry, and the observability to know each service’s cost and latency.
04
Vertex AI for training, tuning and serving, Gemini models through governed endpoints, vector search and grounding on your own data, and the evaluation, safety and cost controls that turn a model into a product the compliance team can approve.
05
Security Command Center, CMEK, private access and audit logging mapped to ISO/IEC 27001 controls; Cloud Monitoring and alerting your team can act on; backups tested by restore; and committed-use discounts, right-sizing and BigQuery reservations reviewed monthly.
Benefits breakdown
Why hire us for Google Cloud, in five lines you can hold us to.
Serverless analytics at any scale means the data team stops sizing warehouses and starts answering questions; we model and govern it so the bill scales with value.
GKE is the most mature managed Kubernetes there is, and Cloud Run removes the cluster entirely where it should — we choose per service.
Vertex AI and Gemini sit next to BigQuery and your storage, so grounding, tuning and serving happen without moving the data out of governance.
Cloud Run, Cloud Build and the CLI make a deploy a small thing — teams ship more often because the platform gets out of the way.
No published case study of ours runs on Google Cloud yet; the delivery standard is the same as our AWS and Azure work, and this page says so rather than implying otherwise.
Tech expertise
Google Cloud is one of the clouds in this stack; here is what we ship on and around it. Every name with a page of its own is a link.
Explore the servicesComparative analysis
What changes when the team actually knows the stack, in six rows.
| Feature | DigiWagon | Other agencies |
|---|---|---|
| Architecture | Landing zones, network boundaries and account structure designed before the first workload, not discovered after the first incident. | A console click-through that nobody can reproduce. |
| Security & compliance | Least-privilege identity, encryption by default, audit trails and evidence mapped to ISO/IEC 27001 controls from day one. | Security as a checklist, after the audit finding. |
| Cost | Right-sized from the start: tagging, budgets, alerts and the reserved-vs-on-demand call made with numbers. | The bill is a surprise every month. |
| Estimation | Real timelines and budget, with the assumptions written down — no plot twist. | Estimate comes with ‘oops, missed that!’ |
| Reliability | Infrastructure as code, tested rollbacks, backups that were actually restored, runbooks your team can follow at 3 a.m. | Snowflake servers and a prayer. |
| Documentation | Diagrams, decision records and runbooks that survive the engineer who wrote them. | The knowledge left with the contractor. |
Industries
Three of the nine industries we build for, with the Google Cloud fit behind each, linked to the industry page.
01
Customer, order and clickstream data unified in BigQuery for merchandising, forecasting and personalisation, with Vertex models served next to the data and marketing tools connected natively.
Google Cloud in Retail & eCommerce02
Telemetry, routing and demand data streamed through Pub/Sub into BigQuery, with forecasting and optimisation models on Vertex and dashboards operations teams actually use.
Google Cloud in Logistics & Supply Chain03
Multi-tenant products on Cloud Run and GKE with BigQuery as the embedded-analytics engine, scaling to zero between tenants’ peaks and billing per tenant from the platform’s own metrics.
Google Cloud for SaaS & TechnologyInsights
Writing from the platform work: from DevOps to platform engineering, building the modern data stack for scalable SaaS products, and sustainable AI by design.
FAQ
Direct answers on Google Cloud versus other clouds, BigQuery, Cloud Run versus GKE, AI on Vertex, cost and what our Google Cloud consulting services include after the migration.
When the product is data-heavy — analytics, forecasting, personalisation, machine learning — and BigQuery, Dataflow and Vertex AI are the centre of gravity; when the team prizes Kubernetes and developer experience; or when Google Workspace and Ads data are core. For Microsoft-centred estates Azure usually fits better, and we say so.
Because it is serverless: no clusters to size, storage and compute scale independently and you pay for what you query or reserve. Modelling and governance still matter — partitioning, clustering, slot reservations, access policies — and that is where we spend the effort, so the bill scales with the value of the questions.
Cloud Run for most services: containers that scale to zero, no cluster to run, deploy in seconds. GKE when you need a real Kubernetes platform — custom networking, stateful workloads, many teams sharing a cluster with policy. Many platforms use both, and moving a service between them is a small task.
Yes. Vertex AI serves Gemini and other models through governed endpoints inside your project, grounding on your own data in BigQuery or vector search, with audit logging, safety settings and cost controls. Your data is not used to train the foundation models. We add evaluation so the product improves measurably.
Labels and budgets on every project from the start, committed-use discounts for steady compute, Cloud Run’s scale-to-zero for the rest, BigQuery reservations or on-demand chosen from real query patterns, storage lifecycle rules and a monthly review with the numbers. Cost is designed with the architecture, not audited after the invoice.
Our published case studies run on AWS and Azure; none yet names Google Cloud, and we would rather say so than imply otherwise. The engineering standard — infrastructure as code, least privilege, tested backups, observability, FinOps — is the same, and the Google Cloud services on this page are ones we have run.
Tell us what the data has to answer and who has to trust it, and we will show you comparable platform work before anything is scoped.
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