AWS Development Services

A stable AWS cloud foundation reduces risk and speeds delivery. We design multi-account landing zones with policy guardrails so teams can ship fast without bypassing controls. We favor automation over tickets and visibility over trust alone.

Compute choices should fit workload shape and team maturity. We standardize golden images, define supported Kubernetes versions, and document upgrade paths to maximize compute power and efficiency.

Event-first systems reduce coupling and simplify scaling. We model contracts up front, version payloads, and track flows end to end. We avoid one-off glue code by standardizing patterns and shared libraries.

Data choices are opinionated and documented. We define RPO/RTO targets, index strategies, and lifecycle policies. Niche or deprecated engines are excluded from new deployments.

Durability, performance, and cloud costs require explicit tiers. We set lifecycle rules, enforce encryption, and run quarterly disaster recovery tests to validate resilience.

We document routing, isolation, and egress patterns. Shared services VPCs and standardized ingress remove drift. For edge, we pair controls with observability to protect performance.

Security is layered and automated. We define least-privilege roles, rotate secrets, and wire alerts to humans who own remediation. We avoid long-lived keys and unmanaged secrets.

We instrument with OpenTelemetry, trace critical paths, and set SLOs with error budgets. Dashboards mirror runbooks to cut mean time to resolve.

Every change is reviewed, tested, and promoted by pipeline. We enforce contracts in CI and block drift at deploy time. Hand-edited consoles are the exception, not the norm.

We publish schemas, version topics, and document retry and DLQ behavior. Back-pressure and fan-out are designed, not discovered in production.

We treat cost as an engineering signal. Budgets, anomaly alerts, and KPIs sit next to latency and error rates. Rightsizing is routine, not an annual event.