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Google Cloud Apps and Data Platforms

Containers on Cloud Run and GKE, analytics in BigQuery, mobile backends on Firebase and AI with Vertex, built as code and sized to your traffic.

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main.py
Sample code

Google Cloud development for data-led products

Google Cloud Platform is known for strong data and container services. BigQuery makes large-scale analytics simple and fast, Cloud Run runs containers without cluster management, GKE is a mature managed Kubernetes service, and Firebase gives mobile and web apps authentication, databases and messaging in minutes. Vertex AI and Gemini models add machine learning and generative AI on the same platform.

Google Cloud is a good fit for analytics-heavy businesses, Kubernetes-based platforms, Firebase-backed mobile apps and teams already using Google Workspace. AWS offers a broader catalogue and suits teams that want maximum service choice, while Azure fits Microsoft-centric organisations. Indian regions in Mumbai and Delhi help with data residency, and we advise based on your workload rather than habit.

Nexzem builds on Google Cloud with Terraform, separate projects per environment and service accounts with narrow permissions. We favour Cloud Run for most services because it scales to zero and keeps operations light, move to GKE only when the workload needs it, and set up BigQuery pipelines so your team can trust its reports.

Read Google Cloud, the way we write it

A short, idiomatic sample. Scroll and the editor types each part while the note beside it explains why it is written that way.

main.py
Sample code
import functions_framework
from google.cloud import firestore
db = firestore.Client()
# Runs on Cloud Run functions and scales from zero with traffic
@functions_framework.http
def orders(request):
# Firestore: a serverless document database, nothing to patch
docs = db.collection("orders").order_by("created", direction=firestore.Query.DESCENDING).limit(20).stream()
return {"orders": [d.to_dict() | {"id": d.id} for d in docs]}
# One command builds the container and deploys it

  1. line 6-8

    Runs on Cloud Run functions and scales from zero with traffic

  2. line 9-12

    Firestore: a serverless document database, nothing to patch

  3. line 13

    One command builds the container and deploys it

What we build with Google Cloud

Applications and data platforms on Google Cloud with Cloud Run, GKE, BigQuery and Firebase.

  1. 01

    Cloud Run Services

    Containerised APIs and web apps on Cloud Run that scale with traffic and down to zero, with custom domains, secrets and CI deployment.

  2. 02

    GKE Platforms

    Managed Kubernetes clusters for multi-service platforms, with autoscaling, workload identity, ingress and observability configured from the start, plus upgrade policies that keep nodes patched.

  3. 03

    BigQuery Data Warehousing

    Data pipelines from apps, ads, CRMs and databases into BigQuery, modelled for reporting in Looker Studio or other dashboard tools.

  4. 04

    Firebase App Backends

    Authentication, Firestore, Cloud Functions, push messaging and hosting for mobile and web apps that need to launch quickly.

  5. 05

    Vertex AI and Gemini Features

    Document understanding, chat assistants and predictions built on Vertex AI and Gemini models, with evaluation and cost tracking.

  6. 06

    Migration to Google Cloud

    Moves from on-premise or other clouds to GCP, including database migration, DNS changes and staged traffic cutover, planned so customers notice little or no downtime.

  7. 07

    Cost and Security Reviews

    Reviews of IAM, service accounts, network rules and billing, followed by fixes that reduce risk and remove waste.

Why teams pick Nexzem for Google Cloud

The checks every engagement has to pass before we call it done.

.github/PULL_REQUEST_TEMPLATE.md4/4 checked

  • - [x] Lighter operations

    Cloud Run removes cluster management for most services, keeping running costs and effort low.

  • - [x] Analytics you can trust

    Well-modelled BigQuery data gives teams consistent reports.

  • - [x] Fast mobile launches

    Firebase gives apps auth, data and messaging without building a backend first.

  • - [x] AI on the same platform

    Vertex AI and Gemini sit next to your data, reducing integration work.

Google Cloud vs AWS vs Azure

Google Cloud stands out in data and analytics, with BigQuery as a serverless data warehouse that scales without capacity planning. It also has deep Kubernetes heritage through GKE, a simple and capable serverless container platform in Cloud Run, strong AI offerings through Vertex AI and Gemini models, and a global private network. Some virtual machines automatically receive discounts for sustained use, alongside committed use discounts.

AWS offers the broadest range of services and the largest ecosystem, while Azure integrates most tightly with Microsoft identity and software. Our AWS vs Google Cloud and Azure vs Google Cloud comparisons cover the trade-offs, and our Firebase vs Supabase comparison helps when choosing an app backend. For data-heavy products and teams that value simplicity, Google Cloud is often an excellent fit.

Many organizations end up multi-cloud for good reasons, such as running analytics on BigQuery while core applications live elsewhere. That works well when identity, networking and data transfer costs are planned upfront rather than discovered on the first bill. Egress fees in particular can surprise teams moving large datasets between providers.

How we set up a Google Cloud organization

Google Cloud organizes resources into an organization, folders and projects. We create folders for environments and business units, projects per application and environment, and apply organization policies that restrict regions, block public IPs where not needed and prevent service account key creation. Access is granted to groups rather than individuals, following least privilege.

CI pipelines authenticate through workload identity federation instead of downloadable service account keys, removing a common source of leaked credentials. Shared VPC centralizes networking, audit logs flow to a dedicated logging project and Security Command Center highlights misconfigurations. Everything is defined in Terraform and changed through reviewed pull requests.

  • Folders and projects separated by environment.
  • Organization policies as guardrails.
  • Group-based IAM with least privilege.
  • Workload identity federation for CI, no key files.
  • Centralized audit logs and security monitoring.

Building data and AI on Google Cloud

BigQuery is often the center of a Google Cloud data platform. Data arrives through batch loads, streaming with Pub/Sub and Dataflow, or exports from Google products such as Google Analytics 4, which can send raw event data directly to BigQuery. Looker or Looker Studio provides dashboards, and Vertex AI connects models and Gemini to the same governed data.

Query costs depend on data scanned or reserved capacity, so table design matters. Partitioning by date, clustering on common filters, avoiding select-all queries and setting per-user or per-project limits keep bills predictable. Teams should also monitor the most expensive queries each month and optimize them, since a few dashboards often account for most of the spend.

  • Partition and cluster large tables.
  • Choose on-demand or capacity pricing based on usage patterns.
  • Set quotas and alerts on query spending.
  • Use authorized views and policy tags for sensitive columns.

How Google Cloud projects run

$ git log --graph --oneline main..delivery

  1. 72c7f1d

    feat: assess workloads

    We review apps, data sources, traffic and compliance needs.

  2. cb16c23

    feat: design architecture

    Projects, networking, service choices and cost estimates agreed upfront.

  3. eb2ff63

    feat: build as code

    Terraform and CI pipelines deliver infrastructure and services together.

  4. 22d0352

    merge: launch and monitor

    Cutover with monitoring, alerting and budget controls in place.

What teams build with Google Cloud

  • Containerized API on Cloud Run

    A product team deploys its API as containers on Cloud Run, scaling from zero to high traffic automatically, with managed HTTPS, revisions for instant rollback and Cloud SQL for data, all without managing any servers.

  • Marketing analytics in BigQuery

    Google Analytics 4 events, ad spend and CRM data are combined in BigQuery, giving marketers attribution and cohort reports in Looker Studio that the standard analytics interface cannot produce on its own.

  • Platform on GKE for many services

    A company running dozens of microservices standardizes on GKE with autopilot clusters, shared deployment templates and policy controls, so teams ship independently while the platform team manages security and upgrades.

  • Firebase app growing into Google Cloud

    A mobile app built on Firebase adds Cloud Run services for complex business logic, BigQuery for analytics and Cloud Tasks for scheduled jobs, extending the same project instead of migrating to a different platform.

  • Document processing with Google AI

    Invoices, forms and contracts are processed with Document AI and Gemini models on Vertex AI, extracting fields and summaries into structured data, with low-confidence results routed to staff for review.

Where Google Cloud sits in your stack

The tools we pair it with, layer by layer. Select a layer to see what it is responsible for.

Technologies often used with Google Cloud

All 32 technologies

More in Cloud & Web3

Google Cloud development FAQs

Something else on your mind? Ask a consultant and get a reply within one business day.

Why choose Google Cloud over AWS or Azure?

Google Cloud stands out for BigQuery analytics, Cloud Run simplicity, GKE and Firebase. AWS has the broadest catalogue, and Azure fits Microsoft-heavy organisations. We recommend based on workload, contracts and skills.

What does Google Cloud development cost?

Effort depends on application and data complexity, migration scope, environments and automation depth. GCP usage is billed separately and estimated during design. You receive a fixed quote after a free consultation.

Is Firebase enough for our app backend?

For many MVPs and moderate apps, yes. If you need complex queries, heavy reporting or strict relational data, we pair Firebase with Cloud SQL or a custom API on Cloud Run.

Do we need Kubernetes?

Often not. Cloud Run covers most services with far less operational work. GKE makes sense for larger multi-service platforms with specific networking or scheduling needs.

How do you secure GCP projects?

We use separate projects per environment, least-privilege IAM, workload identity, Secret Manager, VPC controls where needed and audit logging.

How do you control BigQuery costs?

We design partitioned and clustered tables, avoid queries that scan whole tables unnecessarily, use materialized views for repeated aggregations, choose between on-demand and capacity pricing based on usage, and set quotas and alerts. Monthly reviews of the most expensive queries keep costs from creeping up.

What is workload identity federation?

Workload identity federation lets external systems, such as GitHub Actions or other clouds, access Google Cloud using short-lived tokens tied to their own identity, instead of downloadable service account keys. It removes long-lived secrets that are easily leaked and is the recommended approach for CI pipelines.

We work with clients across the USA, UK, Australia, UAE, New Zealand and India.

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