Get in touch
Cloud Infrastructure + Operations | AI-Powered Cloud Platform | Financial Services

GCP to Azure: Discovery and Infrastructure Replication in Two Weeks

A global insurance client needed to move a significant production environment from Google Cloud Platform (GCP) to Microsoft Azure to align with its enterprise platform standards. The migration required an accurate understanding of the existing environment, precise mapping between cloud services, and validation of the Azure environment before cutover.

Aditi Consulting applied its AI-Powered Cloud Platform approach to accelerate discovery, mapping, and infrastructure replication. Aditi architects directed the work, reviewed the generated infrastructure code, and kept engineering judgment central to the transition.

At a glance

2 months → 2 weeks

Migration timeline

GCP production environment → Azure

Migration scope

Human-led, AI-accelerated

Delivery approach

The Challenge

Moving across clouds without losing control of production

The Challenge

The client needed to understand and document its GCP production environment before rebuilding it on Azure. Services and SKUs had to be mapped accurately between platforms, while applications and data needed a safe path to the target environment.

Traditional discovery, documentation, and cross-cloud mapping would have required months of manual work. The client also needed the new environment tested before cutover and documented well enough for its teams to operate afterward.

The Solution

Compressing the work between discovery and deployment

The Solution

Aditi led the engagement through its Migration & Modernization approach, using AI to accelerate defined tasks while architects directed and validated the engineering work.

  • Discover the source environment: Used read-only access to GCP to extract infrastructure configurations without changing the production environment.
  • Document what was running: Generated documentation of dependencies, configurations, and data relationships.
  • Map between clouds: Used AI-assisted service and SKU mapping to identify Azure equivalents for GCP services.
  • Build and deploy the target environment: Generated Infrastructure as Code for Azure, which Aditi architects reviewed and validated before deployment through GitHub pipelines.
  • Test before cutover: Created a digital twin for end-to-end testing and validation ahead of DNS cutover.
  • Prepare for ongoing operations: Generated architecture diagrams, technical documentation, and operational runbooks for the client.

Discovery, environment mapping, and infrastructure replication were completed in two weeks, compared with a typical process of approximately two months. Testing, validation, and cutover followed.

Business Outcomes

  • ~75% reduction in migration timeline — 2 months compressed to 2 weeks
  • Accelerated evidence collection and documentation — audit-ready from day one
  • Significantly reduced migration risk through digital twin validation prior to cutover
  • Consistent, repeatable infrastructure deployment using Infrastructure as Code throughout
  • Faster service mapping and cross-cloud replication — AI eliminated weeks of manual cross-referencing
  • Architecture diagrams and operational runbooks delivered — client-owned for long-term maintainability
  • Successful decommissioning of the legacy GCP environment on schedule

The two-week comparison covers discovery, environment mapping, and infrastructure replication. Testing, validation, and cutover followed.

What Changed

For this migration, AI-assisted discovery and mapping compressed work that would typically have required extensive manual inventory and cross-referencing.

Aditi’s architects reviewed the target infrastructure before deployment, and the client received documentation and runbooks for operating the Azure environment.

Technology Environment

Technology Environment

Google Cloud Platform (GCP), Microsoft Azure, Infrastructure as Code (IaC), GitHub pipelines, Digital twin environment

Technology Environment

  • Google Cloud Platform (GCP)
  • Microsoft Azure
  • Infrastructure as Code (IaC)
  • GitHub pipelines
  • Digital twin environment

Ready to make cloud transformation less manual?

Let AI handle the repeatable work while your teams stay focused on architecture, governance and the outcomes that matter.
Talk to us