Challenge
A mid-market financial services platform was running a monolithic Java application on aging on-premise infrastructure. Scaling for market-hour traffic spikes required weeks of lead time and manual provisioning. Operational costs were climbing with no performance gains, and the deployment process - manual builds pushed during weekend maintenance windows - was limiting the team’s ability to ship features.
Approach
We designed a phased migration to AWS over 12 weeks, structured to minimize risk and avoid service disruption:
- Containerization - Broke the monolith into 8 containerized services deployed on Amazon EKS with Helm charts for configuration management
- Database migration - Moved from self-managed PostgreSQL to Amazon RDS with read replicas, using AWS DMS for zero-downtime cutover
- CI/CD pipeline - Built automated deployment pipelines with GitHub Actions, including staging validation gates before production rollout
- Auto-scaling - Configured horizontal pod autoscaling tied to CPU and request-rate metrics, with pre-warming rules for predictable market-hour load
- Monitoring - Deployed Prometheus and Grafana for real-time observability, with PagerDuty integration for alerting
Each phase was validated in a staging environment that mirrored production traffic patterns before cutover.
Result
Infrastructure costs dropped 40% within the first quarter post-migration. Deployment frequency increased from monthly to multiple times per day. The platform now auto-scales to handle 5x traffic spikes during market hours without manual intervention. Mean time to recovery (MTTR) for incidents decreased from hours to minutes with improved observability.