Kubernetes Migration for a Fintech Platform

A fintech platform serving consumer financial products in the EMEA region. Approximately three years post-launch, with steady user growth and an engineering team that had reached the point where the original deployment architecture was producing more friction than acceleration.

The platform had launched on a conventional virtual machine deployment with manual operational practices. That worked through the first growth phase. By the time of the engagement, three problems had become structural:

Deployments took hours when they should have taken minutes, and every deployment carried a meaningful risk of partial failure that required manual recovery. Scaling for predictable load events required pre-provisioning of capacity, which sat idle outside the load window and consumed budget that could have funded engineering work. The development team had grown past the point where a single person could hold the full deployment topology in their head, and the operational risk of that knowledge concentration was visible in incident postmortems.

The platform's leadership had identified Kubernetes as the migration target and engaged AlgoCoder to plan and execute the migration without disrupting the live product.

A staged Kubernetes migration delivered over a multi-month engagement.

Stage one — cluster provisioning and baseline operational tooling — Production EKS cluster on AWS, with sized node groups for the platform's workload profile. Baseline observability stack — Prometheus, Grafana, Loki — operational from the cluster's first day. GitOps workflow with ArgoCD for deployment reconciliation.

Stage two — service migration — Services were containerized and migrated one at a time, with traffic gradually shifted from the legacy VM deployment to the cluster. Stateless services moved first; stateful services followed once the operational pattern was proven. Each migration included rollback capability and was timed to avoid the platform's peak load windows.

Stage three — autoscaling and cost optimization — Horizontal pod autoscaling configured per service against meaningful load signals. Cluster autoscaler tuned for the platform's burst patterns. Node group composition adjusted to take advantage of Spot capacity for non-critical workloads.

Stage four — operational handoff — The platform's engineering team was trained on the operational practices required to run the cluster, with runbooks for the failure modes most likely to surface. AlgoCoder remained in a retainer arrangement for the first months of independent operation to support the team through the transition.

The platform exited the engagement with deployments measured in minutes rather than hours, autoscaling that handled load events without pre-provisioning, and an operational topology that the engineering team owned and understood. The migration completed without an outage attributable to the migration itself. The engineering team's deployment cadence increased noticeably, which translated directly into faster product iteration.