CASE STUDY

SaaS Infrastructure Architecture

Engineering a globally distributed, high-frequency trading platform with zero downtime and sub-second replication.

01 — THE CHALLENGE

The Problem

A late-stage B2B FinTech startup had developed a revolutionary algorithmic trading dashboard. However, their monolithic legacy infrastructure was hosted on a single-region cloud deployment.

As their global user base expanded, latency became a critical issue—traders in Asia were experiencing multi-second delays compared to those in New York. Furthermore, a recent regional cloud outage caused massive financial disruption for their users. They required a drastic architectural overhaul to achieve 99.999% global availability, sub-100ms latency worldwide, and absolute fault tolerance.

02 — OUR APPROACH

The Architecture

Our Cloud Engineering team designed an Active-Active multi-region architecture spanning three AWS regions (US-East, EU-Central, AP-Southeast).

We decoupled the monolithic backend into highly specialized microservices, containerizing them with Docker and orchestrating them via Amazon EKS (Elastic Kubernetes Service). Global traffic was routed intelligently using AWS Route 53 with latency-based routing policies, ensuring users instantly connected to the geographically closest healthy cluster.

For the data layer, we migrated their standalone SQL database to Amazon Aurora Global Database, achieving sub-second cross-region replication. High-velocity trading tickers were broadcasted in real-time utilizing a combination of Amazon Kinesis and Redis ElastiCache pub/sub channels. The entire infrastructure was codified using Terraform (IaC), making disaster recovery instant and reproducible.

03 — TOOLS & TECHNOLOGIES

Technology Stack

AWS EKS (Kubernetes)
Amazon Aurora Global
Terraform (IaC)
Node.js Microservices
React Frontend
Redis ElastiCache
Amazon Kinesis
04 — RESULTS

The Outcome

Achieved true 99.999% uptime with zero single points of failure. Global application latency dropped by an average of 350ms, neutralizing the geographic disadvantage for Asian users. Infrastructure costs were simultaneously optimized by 18% through aggressive auto-scaling and spot instance utilization.
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