Google Cloud Platform

Google Cloud (GCP) Architecture & Migration

PixoBots designs, migrates and runs applications and data platforms on Google Cloud, built on managed services - Cloud Run, GKE, Cloud SQL and BigQuery - so your team ships features instead of patching servers. Our dedicated cloud engineers work with AI tools to draft Terraform modules, Cloud Build pipelines and migration runbooks, then review and test every change, so your GCP platform is ready sooner. We wire in Vertex AI where machine learning or generative AI earns its place.

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Our Google Cloud services capabilities

  • check_circle Landing zone, IAM & network design
  • check_circle Cloud Run serverless containers
  • check_circle GKE (Standard & Autopilot) clusters
  • check_circle Cloud SQL & AlloyDB databases
  • check_circle BigQuery-centred data platforms
  • check_circle Pub/Sub & Dataflow pipelines
  • check_circle Vertex AI & Gemini integration
  • check_circle Terraform, CI/CD & cost optimisation

What we deliver

Assess & plan

We inventory applications, databases and dependencies, then decide per workload whether to rehost, replatform onto managed services or refactor.

Build the foundation

Organisation, folders, projects, IAM, VPC and logging set up as code with Terraform - drafted with AI tools and reviewed by your engineer - so every environment is reproducible and auditable.

Migrate in waves

Workloads move in small, tested waves with database replication and a rollback path, keeping production live throughout.

Run & optimise

Monitoring, alerting, budgets and right-sizing after go-live, so the monthly bill tracks real usage rather than launch-day guesses.

Explore related services

BigQuery Development AWS Cloud Services Azure Cloud Services Kubernetes Consulting Machine Learning Development DevOps Engineering

Where Google Cloud is a strong fit

Google Cloud tends to suit teams whose centre of gravity is data and containers. BigQuery is a serverless warehouse with no cluster to size, Cloud Run runs a container on demand and scales to zero when idle, and GKE is a mature managed Kubernetes service - Kubernetes itself originated at Google. If analytics, event streaming or AI are core to the product, keeping them close to the data on GCP removes a lot of plumbing.

It is not automatically the right choice. If your organisation is standardised on Microsoft 365 and Entra ID, or already has deep AWS expertise, we will say so - and we design with portable building blocks such as containers, Terraform and open databases so the decision stays reversible.

Choosing between Cloud Run, GKE and VMs

Most web applications and APIs start best on Cloud Run: you deploy a container, pay for request time, and avoid cluster operations entirely. GKE earns its overhead when you run many services with complex networking, stateful workloads or need fine control over scheduling - Autopilot mode removes most node management. Compute Engine VMs remain the pragmatic home for software that cannot be containerised yet.

For data, Cloud SQL covers managed PostgreSQL, MySQL and SQL Server; AlloyDB is Google's PostgreSQL-compatible option for heavier transactional and mixed analytical load. Analytical data lands in BigQuery, fed by Pub/Sub and Dataflow for streaming or scheduled batch loads.

Keeping GCP costs predictable

Cloud bills drift through idle resources, oversized instances and unpartitioned BigQuery scans. We set budgets and alerts per project, label everything for cost allocation, use committed use discounts only for steady baseline load, and partition and cluster BigQuery tables so queries scan what they need rather than the whole table.

Pixel & Bots

Pixel-perfect software, delivered at AI speed

PixoBots stands for Pixel & Bots. Our Bots are dedicated developers who work with AI tools: AI takes the repetitive work, a developer reviews every line, and the result is pixel-perfect.

Pixel

Polished UI and clean, tested code - detail is part of the job, not an afterthought.

Bots

Dedicated developers who join your team and use AI for boilerplate, tests and documentation.

Savings

AI-assisted delivery can save more than 50% of development cost compared with traditional development.

How AI-augmented delivery works

Plan your Google Cloud project

Tell us about your goals and we'll get back to you within 24 hours.

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Frequently asked questions

What Google Cloud services does PixoBots work with? expand_more
We work across the core of Google Cloud - Cloud Run, GKE, Compute Engine, Cloud SQL, AlloyDB, BigQuery, Pub/Sub, Dataflow, Cloud Storage and Vertex AI - plus IAM, VPC networking and Cloud Monitoring. We choose managed services first so there is less infrastructure for you to operate.
Can you migrate our applications from AWS or on-premises to Google Cloud? expand_more
Yes. We assess each workload, map it to the right GCP service, replicate databases ahead of cut-over and move applications in small waves with a rollback plan, so the business keeps running during the migration.
Should we use Cloud Run or GKE? expand_more
Start with Cloud Run unless you have a clear reason not to. It runs containers without cluster management and scales to zero; GKE is worth its extra operational work when you run many interdependent services, stateful workloads or need Kubernetes-level control.
How do you add AI to an application on Google Cloud? expand_more
We usually call models through Vertex AI, which gives access to Google's Gemini models and other foundation models with enterprise controls, and ground them in your own data from BigQuery or Cloud Storage. Where a simpler rules-based or classical ML approach is enough, we recommend that instead.
How can we reduce our Google Cloud bill? expand_more
Most savings come from right-sizing instances, shutting down idle resources, scaling services to zero, and partitioning BigQuery tables to cut bytes scanned. Committed use discounts then help for predictable baseline usage once it is understood.