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Legacy-to-Cloud Migration: How SBI Modernized Its Infrastructure
Dedicated teams
Financial services

Switch partnered with SBI , a Fairfax Financial company, to migrate its core platform from a legacy, VM-based setup to a modern cloud-native architecture on Google Cloud Platform. The migration involved re-architecting dozens of microservices to run on Kubernetes and moving all associated data with minimal risk and near-zero downtime. The result was a scalable, resilient cloud foundation that cut production deployment time by 90%.

The Client

SBI (Southbridge Insurance) is an innovative insurance company within the global Fairfax Financial Holdings network. They have a strong regional presence and a local office in Uruguay with over 25 years of experience in delivering simple, accessible, and customer-centric insurance solutions.

In 2019, Fairfax Financial referred SBI to Switch, marking the beginning of our partnership. Since then, Switch has been partnering with SBI to drive an end-to-end transformation of its technology and data capabilities. As a result, SBI achieved faster deployments, improved operational efficiency, reduced cloud costs, and established a strong foundation for more agile, data-driven growth.

The Challenge

The platform, composed of 65 interconnected microservices and supported by multiple backend teams, was containerized but relied on Docker Swarm—an orchestration technology that no longer met modern standards for automation, resilience, and support.

The challenge was to migrate the entire ecosystem to a cloud-native architecture on Google Cloud Platform (GCP), adopting Kubernetes as the new standard, while ensuring seamless communication across services via message queues, preserving continuity across multiple environments (development, testing, staging, and production), and executing the migration of over 50GB of data with minimal risk and near-zero downtime.

The Solution

Switch led the transformation of SBI’s core platform by migrating it from a constrained, legacy setup into a modern, cloud-native environment on Google Cloud. This involved rethinking how the application was deployed and operated, enabling a more scalable, flexible, and resilient architecture. The team ensured a seamless transition across all environments—development, testing, staging, and production—while maintaining full operational continuity. By redesigning the deployment model and validating every component end-to-end, SBI adopted a more reliable, future-ready platform without disrupting its day-to-day business.

At the same time, Switch executed a structured and low-risk migration of SBI’s data and infrastructure to GCP, improving both performance and operational efficiency. By moving critical databases to managed and optimized environments, SBI reduced operational overhead while increasing availability and scalability. The phased migration strategy, combined with careful planning and validation, minimized downtime and ensured a smooth cutover.

The Results

SBI's entire infrastructure now resides in Google Cloud, improving the performance, scalability, and cost efficiency of its operations.

This unified, secure, and scalable cloud foundation enables SBI to accelerate innovation and simplify its technology operations, driving faster production deployments, reducing code review times, and improving overall productivity by shifting resources from infrastructure management to higher-value initiatives, while enhancing the use of monitoring and management tools.

In addition, our Cloud & DevOps team implemented a solution for SBI's non-production environments to optimize cloud service usage and costs. This resulted in a 40% reduction in their cloud spending.

By combining deep cloud expertise with a business-first approach, we accelerated time-to-value, reduced complexity, and delivered measurable impact from day one. Our team brought proven patterns, hands-on experience, and a clear focus on outcomes, ensuring every decision aligned with SBI’s strategic goals.

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Project Insights
65

Microservices migrated

90%

Reduction in production deployment time

+50GB

Databases were migrated

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