Cloud Platforms
Google Cloud, Done Properly
We build, migrate and operate applications on Google Cloud, with particular strength in containers, data and analytics workloads.
Overview
Google Cloud
Google Cloud is well regarded for its container tooling, data analytics and machine learning services. Teams choose it for Kubernetes through GKE, simple container hosting on Cloud Run, BigQuery for analytics, and tight links to Firebase and Google Workspace. It is a strong fit when data or developer experience drives the decision.
We design and deploy applications across these services, from lightweight Cloud Run APIs to larger GKE platforms and BigQuery-based reporting pipelines. We set up identity and access, networking, CI/CD and monitoring, and we help teams moving from other clouds or on-premises data centers plan a realistic migration path with clear milestones.
Context
Why teams choose it
- 01
Analytics at Scale
BigQuery lets teams query large datasets without managing database servers.
- 02
Simple Container Hosting
Cloud Run runs containers without cluster management, which suits many web services and APIs.
- 03
Kubernetes Complexity
GKE manages much of Kubernetes, but designs still need care around networking, scaling and upgrades.
- 04
Cost and Access Governance
Projects, IAM roles and budgets need structure to avoid sprawl as teams grow.
Capabilities
What we build with it
- 01
Cloud Run and App Engine
Containerized and managed web services with automatic scaling.
- 02
GKE Platforms
Kubernetes clusters, deployment pipelines and service configuration.
- 03
BigQuery Data Pipelines
Ingestion, transformation and dashboards using BigQuery and Dataflow.
- 04
Firebase Backends
Authentication, Firestore and push notification services for mobile and web apps.
- 05
Migration to Google Cloud
Assessment and move of workloads from on-premises or other clouds.
- 06
AI and ML Workloads
Model hosting and data preparation using Vertex AI and related services.
Typical projects
- Cloud Run API for a SaaS product
- BigQuery reporting warehouse
- GKE microservices platform
- Firebase-backed mobile app backend
- Machine learning pipeline on Vertex AI
Ecosystem & tooling
- Cloud Run
- GKE
- BigQuery
- Cloud Functions
- Firestore
- Pub/Sub
- Terraform
- Vertex AI
Approach
How we work with it
- 1
Assess
We review your goals and any existing code to decide where this technology fits best.
- 2
Architect
We plan structure, integrations, data flow and hosting before writing production code.
- 3
Build
Features ship in small, reviewed increments with automated checks along the way.
- 4
Release & maintain
We deploy, monitor and keep dependencies and versions up to date.
FAQs
Common questions
Have a question that is not covered here? Get in touch.
When is Google Cloud a good choice?
It often suits data-heavy and container-first teams, and products already using Firebase or Workspace. For other cases, AWS or Azure may fit better, and we will say so.
Can you move our workloads from AWS or on-premises?
Yes. We assess dependencies, plan a staged migration and test before cutover. Some services do not map one-to-one, so planning matters.
Do you work with Firebase too?
Yes. Firebase runs on Google Cloud, and we use it for authentication, realtime data and notifications when it fits the product.
Let's build what's next
Tell us about your idea, product or legacy system. Send us a message and we will help you scope the right next step.