Google Cloud development
GCP is where data engineering and applied AI feel native: BigQuery for warehouses that just work, GKE for the best-managed Kubernetes anywhere, and Vertex AI for models in production. We build data-first platforms on it.
In short
Google Cloud, at a glance
- Serverless warehousing that scales to petabytes with zero cluster ops.
- Kubernetes from its creators, with Autopilot clusters that run themselves.
- Vertex AI and Gemini models integrate with your data where it lives.
- Your traffic rides the same backbone as Search and YouTube.
Why Google Cloud
Google Cloud in production.
Google Cloud's gravity is data: BigQuery removed the operational burden from warehousing, and the path from raw events to ML features to served models is shorter here than anywhere.
It's also Kubernetes' birthplace. GKE remains the most polished managed K8s, and Cloud Run makes containers serverless. We design GCP platforms that exploit these strengths instead of treating it as generic compute.
BigQuery economics
Serverless warehousing that scales to petabytes with zero cluster ops.
Kubernetes, first-party
Kubernetes from its creators, with Autopilot clusters that run themselves.
AI-native platform
Vertex AI and Gemini models integrate with your data where it lives.
Google's network
Your traffic rides the same backbone as Search and YouTube.
Serverless depth
Cloud Run scales containers to zero, so you pay only for actual requests.
Security pedigree
BeyondCorp zero-trust and default encryption from the company that invented them.
From raw events to served models. Data-first, on Google's network.
How we engineer
with Google Cloud.
How we work
Senior engineers, accountable to outcomes. Every change typed, reviewed and tested.
01Data platform engineering
Lakehouse architectures with BigQuery at the core and governance built in.
- BigQuery modelling
- Dataflow / Pub/Sub streams
- dbt + Dataform
02GKE platform build
Production Kubernetes with Autopilot, GitOps and observability.
- GKE Autopilot
- Config Sync GitOps
- Cloud Operations suite
03Cloud Run & serverless
Container-based services that scale to zero and to peak.
- Cloud Run services
- Eventarc triggers
- Cloud Tasks & Scheduler
04Vertex AI & MLOps
Models from notebook to monitored production endpoint.
- Vertex pipelines
- Gemini integration
- Model monitoring
05Cloud migration
Assessment-led moves from AWS, Azure or on-prem.
- Workload assessment
- Database migration
- Cutover runbooks
06FinOps & optimisation
Committed-use planning and per-team cost visibility.
- CUD strategy
- BigQuery slot tuning
- Cost dashboards
The stack we pair
with Google Cloud.
Data
Compute
AI
Ops
A look at what we've shipped.
A real-time wealth platform built for scale
Re-architecting a legacy investment portal into a modular, real-time platform that holds up under heavy concurrent load.
View case studyA six-step cycle, repeated until it's right.
Transparent, predictable and collaborative. You always know what's shipping next and why.
Discovery
We map the business, users and constraints, then pressure-test the problem before a line of code.
Planning
Architecture, scope, and a sprint roadmap with clear milestones, budgets and success metrics.
Design
Research-led UX and high-fidelity interfaces, validated with prototypes before build.
Development
Senior-led engineering in two-week sprints with demoable increments and continuous review.
Testing & QA
Automated and manual testing, security review and performance hardening before release.
Launch & Care
Confident deployment, monitoring and SLA-backed support that keeps things humming.
Google Cloud questions, answered.
Still unsure if Google Cloud is right for your project? A senior engineer will tell you straight on a free call.
When data and AI are the centre of gravity: BigQuery and Vertex AI shorten the path from raw data to production intelligence dramatically. It also wins for Kubernetes-first platforms, where GKE is the reference implementation.
Operationally, close to it: no clusters to manage, no vacuuming, and scaling happens automatically. The engineering effort moves to modelling and cost-aware query design, which is exactly where we focus.
Cloud Run for most services; it's serverless simplicity with container flexibility. GKE when you need cluster-level control, custom networking or workloads that don't fit the request model. Many platforms use both.
Yes. Assessment first, then per-workload strategy. Data platforms often move first to capture BigQuery's economics; the rest follows incrementally with parallel running and rollback paths.
Very: pipelines, registries, monitoring and Gemini access in one platform. We add the evaluation harnesses and guardrails that turn impressive demos into dependable features.
Considering an alternative stack?
Let's build
Ready to build with Google Cloud?
Book a free 30-minute consultation. We'll pressure-test your idea and map a Google Cloud approach, whether or not we end up working together.
What to expect
What happens after you hit send.
You book in 60 seconds
Share a few details below. No lengthy forms, no sales gatekeeping.
A 30-minute strategy call
You talk to a senior engineer about your actual problem, not an account manager.
A clear path forward
You leave with concrete recommendations and a rough scope, whether or not we work together.
- 100% free
- Senior engineer
- NDA on request
- Reply in 1 business day
Prefer email?
hello@tech4ze.comNepal
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Australia
13 Basnett Street, Kurralta Park, Adelaide, South Australia 5037