Google Cloud Development Services

Why Google Cloud

Google Cloud Platform (GCP) stands out for data, analytics and developer experience. BigQuery is one of the most capable serverless data warehouses available; Cloud Run makes it easy to run containers without managing servers; GKE is a mature managed Kubernetes service; and Firebase gives mobile and web apps authentication, databases and hosting out of the box.

Companies already using Google Workspace, Google Ads and Google Analytics also benefit from smooth integration between their marketing data and analytics on GCP.

Problems we solve

• Analytics queries too slow or expensive on the current platform
• Containerised apps that need simple, scalable hosting
• Mobile apps that need a reliable backend quickly
• Marketing data spread across Google tools with no single view
• AI projects needing managed infrastructure

What’s included

Cloud Run: a sweet spot for many products

For many web products and APIs, Cloud Run offers an excellent balance: you package the app as a container, and Google runs it, scales it to zero when idle and up to many instances under load, with HTTPS, custom domains and revisions built in. Combined with Cloud SQL or Firestore, it gives small teams production-grade infrastructure with very little operational work.

BigQuery for analytics

BigQuery separates storage from compute and charges for data scanned or reserved capacity, so you can analyse large datasets without managing servers. We design partitioned and clustered tables to keep query costs low, connect sources such as Google Analytics 4, Google Ads, your app database and SaaS tools, and build dashboards in Looker Studio or other BI tools.

Google Cloud services we use most

Project structure and security

Google Cloud organises everything into projects within folders and an organisation. A clean structure — separate projects for production, staging and development, plus shared projects for networking and logging — makes billing, permissions and audits much simpler.

• Organisation policies restricting regions, public IPs and service account keys
• Least-privilege IAM roles granted to groups, not individuals
• Workload Identity instead of downloaded service account keys
• Centralised audit logs exported to a protected project
• Budgets and alerts on every project

Cost management on Google Cloud

Committed use discounts, right-sizing recommendations and Cloud Run’s scale-to-zero can reduce costs substantially, but BigQuery deserves special attention: poorly written queries scanning whole tables can be expensive. We partition and cluster large tables, set per-user query limits and review the most costly queries regularly.

Google Cloud compared with AWS and Azure

All three major clouds can run almost any workload. The right choice usually depends on your team’s skills, existing contracts and a handful of services that matter most to your product.

A simplified comparison; we recommend based on your specific workload and team.

If another cloud suits you better, we say so — see AWS development and Azure development.

A reference architecture for a typical web product

• Cloud Load Balancing and Cloud CDN in front, with Cloud Armor for web application firewall rules
• Cloud Run services for the API and web front end, scaling automatically with traffic
• Cloud SQL for PostgreSQL with private networking, automated backups and point-in-time recovery
• Pub/Sub and Cloud Tasks for background jobs and event-driven processing
• Cloud Storage for uploads and static assets, with signed URLs for private files
• Secret Manager for credentials and Artifact Registry for container images
• Cloud Logging, Monitoring and Trace for observability, with alerting policies tied to user-facing symptoms

This architecture suits most SaaS products and internal platforms. Workloads with heavy batch processing, specialised networking or very high, steady traffic may be better served by GKE.

Moving to Google Cloud

1. Assess1–3 weeks

   Inventory applications, data, dependencies and costs; group workloads into migration waves.

2. Prepare the foundation1–2 weeks

   Organisation, folders and projects, IAM, networking, logging and billing set up with infrastructure as code.

3. Migrate in wavesWeeks to months

   Move lower-risk workloads first, using Database Migration Service or replication for data.

4. OptimiseOngoing

   Right-size resources, adopt managed services and apply committed-use discounts where usage is stable.

Timelines are typical and depend on the size of the estate. Our cloud migration service covers this process across providers.

Common Google Cloud mistakes

• Putting every environment in one project, making permissions and costs hard to separate
• Granting broad basic roles (Owner, Editor) instead of narrower predefined roles
• Using service account keys downloaded as files instead of workload identity
• Leaving BigQuery queries unbounded, scanning entire tables on every dashboard refresh
• Running Cloud SQL with a public IP when private connectivity is available
• Forgetting to set minimum and maximum instances on Cloud Run for latency-sensitive services

Questions to settle before you start

• Will Google Workspace remain your identity source, or do you use another provider?
• Do you have data residency requirements that limit the regions you can use?
• Which workloads are steady enough to benefit from committed-use discounts?
• Who on your team will own the platform after launch, and what support do they need?

Answers to these shape the foundation. For analytics-heavy products, our data engineering team builds BigQuery pipelines alongside the application.

Firebase and Google Cloud together

Firebase is built on Google Cloud, so many products start with Firebase Authentication, Firestore and Hosting for speed, then add Cloud Run services, Cloud SQL or BigQuery as needs grow. Because both live in the same project and billing account, this growth path avoids a disruptive migration. We plan the data model and security rules early so moving heavier workloads to Cloud Run or Cloud SQL later is straightforward.

For mobile products, Firebase also provides crash reporting, remote configuration and push messaging that integrate with the same project.

We also set up billing exports to BigQuery so spending can be analysed by project, service and label alongside product metrics.

Related services

See also data engineering, cloud migration and our comparisons with AWS and Azure.

This page is part of Cloud & DevOps Services — see the full overview for related services, approach and pricing.