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Kubernetes Implementations → Scalability & Cost Optimizations

Powering a multi-region social platform with Kubernetes on AWS
Starting Point
Our client, a fast-growing social networking platform, had users in Europe, North America,
and Asia-Pacific. Their legacy deployment relied on fixed-size EC2 fleets per region, causing
significant underutilization during off-peak hours and performance issues during traffic spikes
— typically in the evening and late night per region.
The Challenge
They needed a solution that would:
- Dynamically scale workloads across regions based on demand.
- Optimize infrastructure costs without compromising reliability.
- Standardize deployment and operations across three AWS regions.
- Ensure high availability and observability during user peak hours.
Our Approach
We introduced a Kubernetes-native architecture using Amazon EKS and implemented best practices for cost-aware scalability:
- Provisioned multi-region EKS clusters with separate node groups for baseline and burst workloads.
- Integrated Karpenter and Cluster Autoscaler for smart scaling based on real-time resource needs.
- Used Spot Instances for non-critical workloads to cut costs by up to 70%.
- Built GitOps-driven CI/CD pipelines with GitLab CI, enabling region-specific and global deployments.
- Integrated Prometheus, Grafana, and Loki for full-stack observability and log aggregation.
- Applied network policies and IAM roles for service accounts (IRSA) to enforce least- privilege access across regions.
Tech Stack
AWS (EKS, IAM, S3, Route53, Karpenter, Spot Instances), GitLab CI, Prometheus, Grafana,
Loki, Helm + Helmfile, Terraform
The Outcome
- Reduced compute costs by up to 55% through dynamic scaling and spot usage.
- Achieved automated scaling per region that aligns with user behavior patterns (e.g., 3x increase in pods during peak hours).
- Delivered a uniform Kubernetes platform that improved DevOps velocity and reduced deployment time by 60%.
- Improved resilience and uptime with fault-tolerant regional failovers.