Auto-Scaling & Performance

Vertical scaling (scaling up) adds more CPU, memory, or storage to an existing instance, while horizontal scaling (scaling out) adds more instances behind a load balancer. We typically combine both: vertical scaling for stateful databases and horizontal auto-scaling for stateless application tiers to maximize resilience and cost efficiency.

Auto-scaling watches metrics such as CPU utilization, request latency, queue depth, or custom application signals. When a threshold is crossed, scaling policies add capacity; when demand drops, capacity is removed. We configure target-tracking, step, and scheduled policies with sensible cooldowns so your platform reacts quickly without thrashing.

We work across AWS, Azure, and Google Cloud. That includes AWS ALB, NLB, and Global Accelerator; Azure Load Balancer and Application Gateway; and GCP Cloud Load Balancing. We also configure Kubernetes ingress controllers, service meshes, and health checks with multi-zone and multi-region failover.

A CDN serves content from edge locations near your users, cutting round-trip latency and offloading traffic from your origin. We layer browser, CDN, application, and database caching—with correct cache-control headers and invalidation—so most requests never touch your servers, dramatically improving speed and reducing cost.

Yes. Databases are the most common scaling bottleneck. We profile slow queries, add and refine indexes, introduce connection pooling, add read replicas, and place Redis or Memcached in front of hot data. Where appropriate we recommend partitioning, sharding, or managed serverless databases that scale automatically.

We analyze historical traffic, seasonality, and growth trends to forecast demand, then model the compute, memory, storage, and network headroom you need for peak events. This prevents both outages from under-provisioning and wasted spend from over-provisioning, and feeds directly into your auto-scaling thresholds.

We build realistic test scenarios using tools like k6, JMeter, Gatling, and Locust, then run load, stress, spike, and soak tests against staging or production-like environments. We measure throughput, latency percentiles, and error rates, identify breaking points, and validate that auto-scaling responds correctly before you go live.

Done right, auto-scaling reduces cost by removing idle capacity during quiet periods while still handling peaks. We pair scaling policies with right-sizing, spot and reserved capacity, and FinOps monitoring so you pay for what you actually use—typically cutting spend while improving performance and reliability.