You’re not overusing the cloud, you’re overspending on it.

Turn on detailed billing/cost explorer and tag resources by team, app, and environment so you can see where growth is coming from. Check for silent drivers: orphaned resources, misconfigured auto‑scaling, unbounded serverless invocations, new managed services, or debugging/analytics jobs left running. Enable anomaly detection and budget alerts so any unusual daily spend is flagged within hours, not at month‑end. Pair this with a FinOps cadence (weekly reviews with engineering) so spend is continuously explained and corrected.

Start with “quick wins”: shut down idle environments at night/weekends, rightsize over‑provisioned VMs/containers, and remove unused load balancers, disks, and snapshots. Add basic auto‑scaling and instance scheduling for non‑prod to avoid 24/7 runtime. Turn on storage lifecycle policies (logs, backups, object storage tiers) to move cold data to cheaper classes. These alone typically yield 10–25% savings in a few weeks when combined with better visibility and alerts.​

Use commitments for workloads that are steady, long‑running, and unlikely to disappear within 1–3 years (databases, core services, baseline clusters). Analyze at least 30–90 days of utilization to identify stable baselines before committing. Keep spiky or experimental workloads on on‑demand or spot so you don’t over‑commit. Generally, cover 50–70% of predictable usage with reservations/commits and leave the rest flexible.​

Run provider and third‑party reports for unattached volumes, idle IPs, unused load balancers, low‑CPU instances, and buckets with only old logs. Enforce tagging so anything without an owner or purpose can be flagged for review and cleanup. Implement automated cleanup scripts or policies for test environments, temporary data, and aged snapshots with a grace period. Make zombie hunting part of a monthly FinOps/ops ritual, not a one‑off project.

Identify components with low average utilization but spiky traffic (APIs, event handlers, batch jobs) and move them to serverless functions or managed event services. Replace self‑managed queues, schedulers, and cron boxes with managed equivalents to cut “always‑on” compute. Break monoliths where it lets you scale only hot paths instead of the entire app. Combine this with auto‑scaled containers for steady cores so you pay mainly for actual usage, not idle capacity.

First, quantify where egress is coming from: cross‑region replication, cross‑cloud links, analytics jobs pulling large datasets, or frequent restores. Co‑locate compute and storage, and minimize chatty cross‑region or cross‑cloud patterns by designing regionalized services and caches. Use CDN, edge caching, or regional analytics clusters so data doesn’t constantly traverse expensive links. For backups/DR, tune retention and replication frequency; not every dataset needs multi‑region, real‑time copies.​