GCP foundation setup
Organisation, folders, projects, IAM, shared VPC and organisation policies.
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Google Cloud Platform
Google Cloud is a strong choice for data-heavy products, containers and AI workloads. We design and build GCP environments using GKE, Cloud Run, Cloud SQL, BigQuery and Vertex AI, with the security, networking and billing controls a production setup needs.
What this covers
Typical timeline
Assessments take 1–2 weeks; implementations are planned in phases with a tested rollback path.
Who it is for
We set up organisations, folders and projects with clear IAM boundaries, shared VPC networking and budget alerts, then deploy workloads as code. For Indian users, the Mumbai and Delhi regions keep latency low and data in India.
Discuss your requirementStartups using Google Cloud credits to build their product
Data and analytics teams consolidating on BigQuery
Companies running containers who want managed Kubernetes
Businesses adopting Gemini and Vertex AI in their products
Capabilities
Organisation, folders, projects, IAM, shared VPC and organisation policies.
Cloud Run and GKE deployments with autoscaling and zero-downtime releases.
BigQuery warehouses, Dataflow and Pub/Sub pipelines and Looker Studio dashboards.
Vertex AI and Gemini integrations with private networking and data controls.
Cloud Armor, Secret Manager, VPC Service Controls and Security Command Center.
Budgets, committed use discounts, rightsizing and billing exports for cost visibility.
How we work
You always know what happens next, who is responsible and what you will receive at each stage.
Typical timeline
Assessments take 1–2 weeks; implementations are planned in phases with a tested rollback path.
Workloads, data, costs and goals are reviewed.
Organisation structure, IAM and networking are agreed.
Projects and services are provisioned with Terraform.
Applications and data move in planned waves.
Monitoring, security reviews and cost optimisation continue.
Deliverables
Technology & standards
We recommend tools based on your scale, budget and existing systems, not on what is fashionable. Every choice is explained in the proposal.
Engagement models
A review of your current setup with a costed, prioritised plan you can execute with us or in-house.
Migration, platform build or pipeline setup delivered against a fixed scope and cut-over plan.
Monitoring, patching, cost reviews and on-call support under a monthly SLA.
How pricing works: Cloud work is quoted on the number of environments, services and workloads involved. Your cloud bill stays in your own account; we never mark up provider charges.
Get a quoteFAQs
Both are excellent. Google Cloud is often preferred for BigQuery analytics, Kubernetes and AI workloads, while AWS has the broadest service catalogue. We recommend based on your workloads, team skills, credits and pricing.
Google Cloud has regions in Mumbai (asia-south1) and Delhi (asia-south2), which suit primary and disaster recovery setups for Indian workloads.
We can architect your platform to use credits efficiently and avoid services that become expensive once credits run out. Eligibility and approval are decided by Google.
Yes. We plan and execute migrations from AWS, Azure or on-premise environments with testing and rollback at each stage.
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