SVC-03 · Cloud, Platform & Reliability
Production infrastructure built for measurable reliability.
AWS, Azure and Kubernetes platforms where capacity, latency, observability and resilience are measured targets — not adjectives in an architecture diagram.
Capabilities
Reliability you can put a number on.
- 01
Kubernetes & containers
Resource requests and limits from measurements, autoscaling that tracks the right signal, rollouts that can be stopped.
- 02
AWS & Azure platforms
Terraform-managed infrastructure, multi-AZ design, managed data services chosen for operability and cost.
- 03
Observability
Traces, metrics and logs that answer operational questions — Prometheus, Grafana, OpenTelemetry, ELK, Splunk.
- 04
Performance & capacity
Load testing, latency budgets, capacity models and headroom targets tied to real traffic patterns.
- 05
Resilience
Failure-domain design, dependency timeouts, failover rehearsals and runbooks that have been executed at least once.
- 06
Delivery & deployment safety
CI/CD with ArgoCD and Jenkins, progressive delivery, rollback paths and environment parity.
Where AI fits
Inference is a workload. It scales like one.
Rate limits, concurrency ceilings, queueing and provider outages show up in AI systems exactly where they show up in any other distributed system. The difference is that each retry now has an inference cost attached.
That is why the AI Systems Audit treats scaling and reliability as first-class findings, and why infrastructure work often follows one.
Know your headroom before the traffic does.
Tell us what the platform runs and what keeps you up at night.