Kubernetes Platform for a Code-Generation SaaS

Project Overview

The client runs a code-generation developer platform serving a fast-growing base of engineers. When I came in, the entire product was running on self-managed EC2 instances. The setup was expensive, hard to operate, and — most damaging — unable to absorb the traffic spikes that came with growth.

I led the transition from that EC2-based footprint to a containerized architecture on Amazon EKS, designed around scalability, high availability, cost efficiency, and observability. The goal was simple: make the platform hold up under peak demand while cutting the operational drag the team was carrying.

Key Challenges

The starting state had four structural problems:

• No scalability — the infrastructure couldn’t handle sudden spikes in demand, causing bottlenecks and outages
• High operational cost — self-managed EC2 meant paying for instance provisioning, data transfer, storage, and constant management overhead
• Weak observability — no real log analytics, search optimization, or visualization, so the team couldn’t derive insight or troubleshoot quickly
• Limited high availability — a single-AZ footprint left the platform exposed to hardware and network failures

Solution / AWS Architecture

I designed the target architecture around AWS managed services so the team could stop babysitting infrastructure and focus on the product.

Storage

Static frontend assets — images, stylesheets, and JavaScript — moved into dedicated S3 buckets for fast, highly available content delivery. S3 also became the primary backup target: Velero manages regular, automated backups of application data and cluster resources so critical state can be restored after a failure.

EKS Orchestration

Amazon EKS runs the containerized workloads. Managed, auto-scaling node groups size the cluster to actual load, which optimizes both resource usage and cost instead of paying for idle capacity around the clock.

DevOps Automation

GitHub Actions drives CI/CD, taking a commit to production automatically. Terraform provisions every AWS resource as code, so environments are consistent and reproducible rather than hand-built. SonarQube runs in the pipeline for security and quality scanning before anything ships.

Observability

I instrumented three layers of visibility:

• CloudWatch for real-time application performance and utilization metrics
• AWS X-Ray for distributed tracing, integrated with OpenTelemetry
• ADOT for Prometheus-based metrics on ingress requests

Together these gave the team centralized logging, real-time analytics, and the tracing they’d been missing.

High Availability and Disaster Recovery

RDS Multi-AZ provides fault tolerance with automatic failover. AWS Backup handles automated backups for fast recovery, and Velero backs up in-cluster resources — deployments, persistent volumes, and more. The deployment spans multiple availability zones and regions to keep the service running through localized failures.

Security

IAM enforces least-privilege access controls. KMS encrypts data at rest and in transit. Pod-level security controls protect workloads inside the cluster.

Cost

Auto-scaling ties spend to actual demand — the cluster scales up under load and back down when it’s idle, eliminating the standing waste of always-on EC2.

Results

The migration delivered measurable gains across reliability, speed, and experience:

• Log search time down 70% through centralized logging and analytics
• Downtime reduced 50% via real-time log analytics and proactive issue detection
• Deployments in under 5 minutes from commit, sustaining 10-20 production releases per week
• MTBF up 25% for greater system stability
• MTTR down 45% for faster recovery from incidents
• Latency from 200ms to 40ms on content delivery

Key Learnings

1. Managed services buy back focus — moving to EKS with managed node groups let the team stop operating servers and start shipping features
2. Observability is the foundation, not an add-on — centralized logging and tracing cut troubleshooting time more than any single infra change
3. Automate the whole path — Terraform plus GitHub Actions turned deployment from a risk into a routine, unlocking 10-20 releases a week
4. Scale with demand, not fear — auto-scaling handled the spikes that used to cause outages while cutting the cost of idle capacity