Global payment processing operates at a different level of technical rigor than most software domains. Transactions have to complete reliably, across geographies, at volume, with the latency and security posture that financial services demands. There is no tolerance for inconsistency at scale, and the consequences of infrastructure failure are immediate and visible.
This client had built a solid payment processing business on a legacy system that had served them well. But the system could not keep pace with where the business was going. Transaction volumes had grown. Geographic reach had expanded. The existing architecture had been extended and patched to handle incremental requirements, but a fundamental re-engineering was unavoidable. The question was how to build something that could genuinely support global scale, not just work today.
A Cloud-Native Foundation
The platform was built on AWS and Kubernetes as the core infrastructure layer. Fully managed services were used wherever they were available: EKS for container orchestration, RDS and DynamoDB for data persistence, Cognito for authentication, Kafka for event streaming. This was not a preference for complexity. It was a deliberate choice to reduce operational surface area. Every managed service is one fewer thing to operate, patch, and monitor independently.
Kubernetes provided the consistency layer across environments. Services that worked in development worked in production because the deployment model was the same. The differences between environments were managed through configuration, not through manual operational steps that introduced variability.
Infrastructure-as-code through Terraform and Terragrunt meant that the platform was reproducible. Standing up a new region was not an operational project. It was running a pipeline. The same was true for disaster recovery scenarios where speed of recovery was measured in minutes, not hours or days.
Developer Self-Service at Scale
One of the most consequential investments in the program was in the developer experience. When 90-plus applications need to be built on a shared platform, the onboarding and consistency problem is significant. Every team reinventing its own logging, metrics, secrets, and authentication approaches introduces variability and cognitive overhead that compounds over time.
VergeOps addressed this with a best-practice Java/Spring Boot reference application that became the standard starting point for every new service. Teams building on the platform began from a foundation that already handled the infrastructure concerns correctly. The separation of concerns was deliberate and deep. Developers were not expected to understand how logs were collected, how metrics were emitted, how secrets were retrieved at runtime, or how service mesh authentication worked. They wrote the business logic. The platform handled everything else.
Automated build and deployment pipelines through GitLab, ArgoCD, Argo Rollouts, Istio, and Liquibase handled database migrations, zero-downtime deployments, and rollback scenarios. Shipping a service was fast and consistent regardless of which team was doing the shipping.
What We Built
Best-Practice Reference Application
A standardized reference that served as the foundation for 90+ services. Every team started from the same proven baseline, ensuring consistency in logging, metrics, secrets handling, and authentication across the entire application estate.
Fully Automated Deployment
End-to-end pipeline automation through Terraform, GitLab, ArgoCD, Argo Rollouts, Istio, and Liquibase. Zero-downtime deployments, automated database migrations, and consistent delivery across all environments and regions.
Global Scale, Security, and Observability
Deployment across six regions with automated failover. Managed firewalls and WAF for perimeter security, Istio for service mesh and mutual TLS between services, autoscaling Kubernetes deployments, automated secrets management that delivered sensitive data to applications without developer involvement, and full APM observability through Splunk.
The Outcome
Today, the platform handles large-scale global payment processing with the reliability, latency, and security posture the business requires. Six regions deploy consistently from the same infrastructure definitions. Teams building services on the platform ship with speed and confidence because the infrastructure concerns have already been solved for them.
The developer self-service model has had lasting effects on how the engineering organization works. Onboarding a new service is fast. The consistency of the platform means that debugging and incident response work the same way across every service. Operational knowledge is not siloed in a specialized platform team that everyone else depends on. It is embedded in the platform itself.
Facing a Similar Challenge?
Large-scale platform re-engineering programs require deep experience in both cloud architecture and the organizational dynamics of large engineering teams. VergeOps has worked on engagements of this complexity across financial services and other demanding domains. If your organization is navigating a similar challenge, we would be glad to discuss it.