Cutting cloud spend with AI ops

Most cloud bills have a long tail of waste hiding in plain sight: idle compute, over-provisioned databases, forgotten test environments, egress on stale CDNs, premium tiers nobody benefits from.

We use an LLM-driven workflow to ingest the last 90 days of cost-and-usage data, cross-reference it with deployment manifests, and produce a ranked list of optimizations with expected monthly savings.

The top items are almost always the same: right-size VMs and managed DBs, shut down non-production environments outside work hours, switch hot storage to cool/archive where access patterns allow, and consolidate accounts to qualify for higher reserved-instance tiers.

A typical engagement pays for itself in the first month. Combined with a small set of FinOps guardrails (budget alerts, tagging policy, scheduled reaper jobs), most teams hold those savings indefinitely.