Cloud Cost Optimisation in Noida

Idle non-production resources are the first line. A staging environment sized like production and running all month is common in Noida product teams.

NAT gateways and data transfer are the second. A workload that talks to S3 through a NAT gateway pays for traffic that an endpoint could remove.

Storage and logs are the third. Old snapshots, verbose logs and unattached volumes accumulate quietly. A lifecycle rule and a retention decision usually recover more than a reserved-instance purchase.

We tag or map the bill to applications and environments, then rank the findings by saving and effort. The output separates quick fixes from changes that need an architecture decision.

Commitments are sized only after the idle and storage work. Buying a savings plan on top of waste locks in the waste for a year.

Each recommendation names an owner and a verification step, so the saving can be checked on the next bill.

A ranked fix list with estimated monthly saving, effort and risk; a tagging or showback recommendation; and a short review of the commitments that fit the actual usage.

We also flag the costs that are not worth optimising, such as a small service that would cost more to re-architect than it saves.

The review covers analysis and recommendations. Implementation, architecture changes and ongoing FinOps are separate. Provider bills and third-party licences remain with the provider.

The first pass maps the bill to applications and environments, then checks the settings that usually drift: idle non-production resources, NAT routes, storage lifecycle and log retention. Each finding gets a saving estimate and an owner.

The review does not start with a commitment purchase. It starts with the evidence that a commitment would be sizing.

A second pass checks the savings on the next bill and updates the list. The review is deliberately short: a ranked list, an owner and a date, not a dashboard nobody opens.