Scaling, Reliability and Observability Stay fast and stay up when traffic spikes

Growth is only good news if the platform keeps up. We find the limits before your customers do: load tests that mimic real behaviour, profiling of slow endpoints and queries, and capacity planning based on data rather than guesses.

Scaling work usually combines several layers: a CDN and edge caching in front, ALB or NLB with autoscaling groups or Kubernetes autoscaling behind, application level caching with Redis, read replicas and connection pooling for databases, queues to smooth spikes, and rate limiting to protect the core.

Reliability is engineered with service level objectives. We define what good looks like for availability and latency, instrument the system with OpenTelemetry, build Grafana or Datadog dashboards your team actually reads, and set alerts that page for real problems only.

When incidents happen you will have runbooks, structured logs and distributed traces to find the cause in minutes, and a review process that turns each incident into a fix rather than a repeat.