Cloud-native migration for a fintech startup

Migrated a legacy monolith to a cloud-native Kubernetes architecture on AWS, achieving 60% infrastructure cost reduction and 99.99% uptime.

Client: Confidential (Fintech Industry)
Practice: Cloud Architecture & Infrastructure
Stack: AWS EKS · Terraform · ArgoCD
Cost reduction: 60%
Uptime: 99.99%
Faster deploys: 50x

The challenge

A Series A fintech startup was running a monolithic application on bare-metal servers with manual deployments, leading to high infrastructure costs, week-long release cycles, and frequent downtime that was eroding customer trust.

Our solution

We decomposed the monolith into microservices, containerized them with Docker, orchestrated with Kubernetes on AWS EKS, and built fully automated CI/CD pipelines with GitOps-based deployment workflows using Terraform and ArgoCD. The migration was executed with zero downtime using a strangler fig pattern.

Why the monolith had to go

A Series A fintech was running its entire product as a monolith on bare-metal servers, released by hand. In fintech that combination compounds: every deploy touched the whole system, so every release carried the full blast radius, and releases stretched into week-long cycles because nobody wanted to be the one who broke payments. Downtime was frequent enough that customers noticed, and in financial services noticed downtime converts directly into eroded trust. Infrastructure costs were high because capacity was sized for peak and paid for around the clock. The engineering team was spending its time shepherding releases and firefighting instead of shipping product. The client asked us to change the physics of the problem, not just patch the symptoms.

Decomposition strategy

Decomposition started with the domain, not the code. We mapped the monolith's modules against the business (ledger, payments, onboarding, notifications, reporting) and drew service boundaries where the data and the teams naturally separated. Anything transactionally entangled stayed together: splitting a service across a boundary that needs atomic writes trades a deploy problem for a distributed-systems problem, and we refused that trade. Each extracted service got its own datastore where ownership was clean, its own Docker image, and a contract-tested API. The goal was never microservices for their own sake but independent deployability, the property that lets one team ship its service without scheduling around everyone else's release.

Zero-downtime migration via strangler fig

Migration ran on the strangler fig pattern, because a fintech cannot take a maintenance window to cut over. We placed a routing layer in front of the monolith and moved traffic one capability at a time: a new service went live, received a shadow copy of production traffic, and had its outputs compared against the monolith's until the two agreed. Only then did the router shift real traffic, with the monolith's code path kept warm as an instant rollback target. Customers never saw a cutover; there was none to see. Each slice of the monolith went quiet as its replacement proved itself, until the legacy system was serving nothing and could be retired without ceremony.

The EKS platform

The target platform is Kubernetes on AWS EKS. Workloads are containerized with Docker and scheduled across node groups sized for their actual profiles: latency-sensitive services on stable on-demand capacity, batch and asynchronous work on spot instances that cost a fraction as much. Horizontal pod autoscaling follows real load, and cluster autoscaling adds or removes nodes to match, so the client stopped paying peak-capacity prices around the clock the way bare metal forced them to. Requests and limits are set from measured usage, not guesses. Right-sizing, autoscaling, and running spot where it is safe are where the 60% infrastructure cost reduction came from, alongside retiring the data-center hardware entirely.

GitOps delivery

Delivery is now fully automated and declarative. Terraform describes every piece of infrastructure (cluster, networking, IAM, data stores), so environments are reproducible and reviewable in a pull request. ArgoCD watches the Git repositories and continuously reconciles the cluster against what is declared there: merge to main and the change rolls out; revert the commit and the system rolls back. There are no hand-run deployment scripts left, and no deploy knowledge living in one engineer's head. Releases went from week-long, all-hands events to routine merges, 50x faster, and small diffs now ship the day they are written, so each release is small enough to reason about and cheap to undo.

Holding 99.99% uptime

Reliability is engineered, not hoped for. Every service defines liveness and readiness probes, so Kubernetes restarts unhealthy pods and withholds traffic from ones that are not ready. Rolling deployments surge new pods and drain old ones, and a failed health check halts the rollout automatically before it reaches users. Services run replicated across availability zones, so a node or zone failure is absorbed rather than felt. Metrics, logs, and traces feed dashboards and paging alerts tied to user-facing symptoms (error rate and latency) rather than machine noise. That combination is what holds 99.99% uptime: failures still happen, but they are contained, detected, and rolled back before they become outages a customer would notice.

Life after the migration

After the migration, the platform is an asset instead of a liability. The client's engineers ship features; the operational surface, from cluster upgrades and capacity posture to dependency patching and alert tuning, is handled through the same GitOps loop that handles releases, so operational changes get the same review and the same rollback path as code. New services join the platform by adding a manifest, not by provisioning hardware. Cost stays visible because workloads are labeled and metered, so spend maps to services instead of arriving as one opaque bill. The monolith's release calendar is gone; what replaced it is a system where deploying is unremarkable, downtime is rare, and the infrastructure bill reflects what the business actually uses.

The numbers

Cost reduction: 60%
Uptime: 99.99%
Faster deploys: 50x