- AWS Auto Scaling Group configuration with CloudWatch scaling policies
- GCP Managed Instance Group autoscaling and GKE node pool autoscaling
- Kubernetes HPA with CPU, memory, and custom metric triggers
- KEDA (Kubernetes Event-Driven Autoscaling) for queue-based scaling
- Database read replica setup and application-level read/write splitting
- PgBouncer connection pooling to handle burst database connection load
- Queue worker scaling with Celery, Sidekiq, or BullMQ on Kubernetes
- Load testing to validate scaling behaviour before production traffic
- Capacity planning reports with growth projections and scaling cost estimates
- Stateless application design review for horizontal scale compatibility
Performance
Scaling
Horizontal and vertical scaling strategies.
Scaling is the answer to load that exceeds what a single server can handle — but it must be designed in, not bolted on. Horizontal scaling (adding more instances) is preferred over vertical scaling (larger instances) because it is cheaper, more resilient, and allows zero-downtime capacity changes. Hellenic Technologies designs applications and infrastructure for horizontal scale from the start, avoiding shared mutable state on individual servers.
Auto-scaling groups react to demand automatically. On AWS, Auto Scaling Groups scale EC2 instances based on CloudWatch metrics — typically CPU utilisation, request count per target, or custom application metrics published to CloudWatch. We set conservative scale-out thresholds (60-70% CPU) and aggressive scale-in cooldowns to prevent thrashing, and maintain a minimum fleet size that handles baseline traffic without cold-start delay. Kubernetes Horizontal Pod Autoscaler provides equivalent functionality for containerised workloads.
Database scaling presents different challenges. For read-heavy workloads, read replicas distribute SELECT queries across multiple database instances, reducing load on the primary. We configure application-level read/write splitting or use PgBouncer/ProxySQL to route queries automatically. For write-heavy workloads requiring horizontal scale, we evaluate sharding, CQRS patterns, or migration to distributed databases (CockroachDB, PlanetScale) based on the consistency requirements.
Horizontal scaling services:
