Cloud spend in payment systems grows faster than transaction volume when left unmanaged. A structured audit of compute, storage, and data transfer usually uncovers significant savings quickly.
Cloud infrastructure costs for payment systems have a consistent property: they grow faster than transaction volume. The gap is not caused by expensive architecture decisions but by the accumulation of small inefficiencies that nobody has time to audit during a period of rapid growth.
A structured cost audit of compute, storage, data transfer, and service configuration typically uncovers 25–40% in savings without any reduction in reliability or performance. Here is how to run one.
Start with compute right-sizing
The largest single category of overspend is compute instances running at 10–30% average CPU utilisation. These are instances that were provisioned for a peak load that either never materialised or happened once, and the instance was never resized afterward.
Pull utilisation data for every instance over a 30-day period. Identify instances where p95 CPU is below 40% and p95 memory is below 50%. These are candidates for downsizing. For stateless services, also evaluate whether horizontal scaling with smaller instances gives you better utilisation than a few large ones.
Reserved and committed-use coverage
On-demand pricing is significantly more expensive than reserved or committed-use pricing for stable workloads. Most payment platforms have a stable baseline of traffic — the transactions that run 24/7 regardless of campaign or seasonal peaks — that should be covered with reservations.
A sensible target: 60–70% of your baseline compute covered by 1-year reservations, with on-demand capacity for the remainder. Compute Savings Plans (AWS) or Committed Use Discounts (GCP) are typically more flexible than specific instance reservations and easier to manage as instance types evolve.
Related: Observability for Transaction-Critical Systems
Data transfer costs are invisible until they're not
Data transfer between availability zones, between regions, and out to the internet is charged per gigabyte and adds up quickly in systems that move financial data between services. Most teams don't track data transfer costs separately.
Audit data transfer line items in your cloud bill. Look for inter-AZ traffic patterns that could be reduced by colocating services, cross-region replication that's larger than necessary, and API response payloads that include more data than clients actually use.
Storage tiering
Payment transaction data has a natural access pattern: recent transactions are queried frequently, older transactions are queried occasionally, and archived data is queried for audits and regulatory requests. Storing all data in the same hot storage tier is paying premium prices for data you rarely access.
Implement lifecycle policies that move transaction data older than 90 days to standard storage, and data older than 1 year to archive storage. Ensure your audit trail and reporting systems can still access archived data — they should read from archive rather than requiring migration back to hot storage.
Underutilised services
Systems that grew quickly accumulate services that were provisioned for a purpose that no longer exists — a load testing environment that ran once, a feature flag service for a feature that launched a year ago, a database replica for a reporting pipeline that was rebuilt on a data warehouse.
A service inventory audit is mechanical but valuable. For every running service, identify: what is it, what does it serve, when was it last accessed, who owns it. Services with no owner and no recent traffic are candidates for decommissioning.
Related: When to Re-Architecture vs. Stabilize a Payment Platform
The 30% number
Across the infrastructure optimization engagements we've run, the typical finding is:
- Compute right-sizing: 12–18% reduction
- Reserved instance coverage: 8–12% reduction
- Data transfer optimisation: 4–8% reduction
- Storage tiering and service cleanup: 4–6% reduction
The total is additive because these are independent categories. A 30% reduction in a system spending $500K/year on infrastructure is $150K/year — with a payback period on the engagement cost measured in weeks, not months.
If your infrastructure costs are growing faster than your transaction volume, an infrastructure cost audit will identify exactly where the overspend is and what to do about it.
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