Incident loss, security overhead, AWS waste, and operational toil — four costs most funded fintechs aren't measuring. Here's how to model all four.
At some point in the next board meeting, investor update, or Series B due diligence process, someone will ask what your payment infrastructure is costing you annually.
Not your AWS bill. Not your engineering headcount.
The real number.
What your infrastructure costs when you include the incidents it causes, the security work it requires, the capacity you're over-provisioning, and the operational burden it creates for your team.
Most CTOs at funded fintechs don't have that number. They have an AWS bill. They have a rough sense of engineering time. But they haven't modelled the four cost categories that sit underneath the headline figures and those four categories are where the real exposure lives.
The gap between what most CTOs think their payment infrastructure costs and what it actually costs is typically 30-50%. For a fintech processing £5M per month across eight payment services, that gap can represent £150,000-£300,000 of unmodelled annual exposure.
This post breaks down the four cost categories, what drives each one, and how to model your own number before your next architecture review or board conversation.
Why Your AWS Bill Isn't Your Real Cost
Your AWS bill is the visible cost. It's the one that shows up in your finance system, gets reviewed in quarterly business reviews, and gets flagged when it spikes.
The real cost of your payment infrastructure is four times larger than your AWS bill suggests because three of the four cost categories don't appear on your cloud invoice.
Expected incident loss doesn't appear on your AWS bill. It appears in your revenue when a gateway failure or unplanned outage causes transactions to fail. Security engineering overhead doesn't appear on your AWS bill. It appears in your engineering team's time when they're doing threat modelling, pen test remediation, and vulnerability triage instead of shipping product. Operational toil doesn't appear on your AWS bill. It appears in on-call burden, manual runbook execution, and deploy complexity that grows non-linearly as your service count increases.
Only AWS over-provisioning waste appears on your bill and most finance teams don't know to look for it because it looks like normal cloud spend.
Ask yourself: When you last presented your infrastructure costs to your board or investors, did you include all four of these categories or just your AWS bill?
→ Model your real annual cost syncyourcloud.io/opex-calculator
The Four Cost Categories — And What Drives Each One
Expected Incident Loss
This is the annual value lost to infrastructure-caused payment failures gateway degradations, unplanned outages, processing errors, and cross-service cascade failures. It is distinct from bank-side soft declines which are outside your control.
The formula is based on published research. The base failure rate for payment infrastructure sits at 0.08% of annual transaction volume, the conservative midpoint of the 0.05%-0.12% range cited in IBM Cost of Downtime research and GoCardless 2021 global failed payments study. A log-scaled complexity multiplier accounts for additional failure modes as your service count grows because each additional payment service adds inter-service dependencies that create new failure paths.
For a fintech processing £3M per month with six payment services, this component alone represents approximately £35,000-£45,000 of annual exposure.
This number grows faster than most CTOs expect as service count increases. A team that adds three new payment microservices in a quarter hasn't just added three services they've added n² new dependency relationships.
Ask yourself: Do you know your payment failure rate? Have you modelled what it costs you annually in lost transaction value?
→ See your incident loss exposure: syncyourcloud.io/opex-calculator
Security Engineering Overhead
This is the annual cost per payment service for threat modelling, vulnerability management, penetration testing, and remediation cycles required to maintain a secure cardholder data environment.
The formula is £8,000 per service based on approximately four days of internal security engineering time per service per year at £534/day (ITJobsWatch UK PCI DSS contract median, April 2026) plus external pen test fees of £2,000-£5,000 per focused service scope. The figure assumes shared tooling and is low-to-mid range — CREST-accredited engagements will cost more.
For a fintech with eight payment services this represents £64,000 of annual security engineering overhead before any remediation work from findings.
This is the cost category most CTOs underestimate most severely. Engineering time spent on security is invisible in most cost models it shows up as slower product delivery, not as a line item.
Ask yourself: How many engineering days per quarter does your team spend on security work across your payment services? Have you costed that at your fully-loaded engineering rate?
→ Model your security engineering overhead: syncyourcloud.io/opex-calculator
AWS Over-Provisioning Waste
Payment teams over-provision AWS capacity by 30-50% to meet regulatory burst tolerance requirements. Flexera's State of the Cloud Report 2024/2025 reports organisations waste an average of 28-35% of total cloud spend annually a finding consistent across five years and 750+ survey respondents. Financial services waste is cited at 30-50% due to regulatory capacity headroom requirements unique to payment infrastructure.
The formula assumes approximately £13,000 per service per year in total AWS cost and a 30% waste rate giving £4,000 of waste per service annually.
For a fintech with eight payment services running on AWS, this represents £32,000 of annual cloud waste capacity sitting idle to satisfy regulatory burst tolerance that could be architected away with the right reserved capacity and auto-scaling strategy.
This is the one cost category that does appear on your AWS bill it just looks like normal spend because nobody has identified and tagged the over-provisioned capacity.
Ask yourself: Has your team done a reserved capacity analysis on your payment services in the last 12 months? Do you know what percentage of your AWS payment spend is idle capacity?
→ Calculate your AWS waste exposure: syncyourcloud.io/opex-calculator
Operational Toil
This is the on-call burden, manual runbook execution, incident response, and deploy toil across your payment stack. It grows non-linearly as service count increases and inter-service dependencies multiply.
The formula is £30,000 base plus £3,000 per service beyond three. The base covers minimum on-call and runbook burden. The per-service increment reflects that each additional payment service adds separate on-call runbooks, inter-service dependency management, and deploy complexity. Grounded in UK SRE and platform engineering salary data — ITJobsWatch median £72,500/year permanent, April 2026 — with a 30-40% toil burden applied.
For a fintech with eight payment services this represents £45,000 of annual operational toil engineering time spent keeping the lights on rather than building product.
This is the cost that compounds most painfully as teams scale. A team that doubles their payment service count doesn't double their toil — they quadruple it, because toil scales with dependencies not just service count.
Ask yourself: What percentage of your engineering team's time is spent on operational maintenance versus new product development? Has that ratio changed as your service count has grown?
→ Model your operational toil cost: syncyourcloud.io/opex-calculator
What the Total Looks Like
For a typical Series A-B fintech processing £3M per month across eight payment services, the four cost categories combined produce an annual risk exposure of approximately £176,000-£220,000.
Their AWS bill for the same infrastructure is typically £80,000-£100,000 per year.
The gap between the AWS bill and the real cost is £96,000-£120,000 costs that don't appear on any invoice but represent real financial exposure from incidents, security work, waste, and toil.
That gap is what most CTOs don't have a number for when their board asks.
The important caveat
PCI DSS compliance cost is deliberately excluded from this model. PCI DSS scope is assessed against your cardholder data environment as a whole not per microservice. Whether you require an SAQ-D self-assessment or a full QSA-led ROC depends on your transaction volume, CDE boundary, network segmentation, and card brand requirements. No QSA firm publishes pricing and no per-service formula exists. Any number in this model would be invented.
For your PCI DSS cost picture use the PCI Gap Analysis tool in the platform to map your actual control gaps, then engage a QSA for a scoping call.
Ask yourself: If you added your incident loss, security overhead, AWS waste, and operational toil to your AWS bill - what would your board see as the true cost of your payment infrastructure?
→ Get your number before your next board meeting: syncyourcloud.io/opex-calculator
Two Inputs. Your Real Number.
The AWS Payment Infrastructure Risk Calculator takes two inputs — your payment service count and your monthly transaction volume — and models your annual risk exposure across all four categories using published industry data.
Every formula is sourced and transparent. The expected incident loss formula cites IBM and GoCardless research. The security engineering overhead cites ITJobsWatch UK contract data. The AWS waste figure cites Flexera State of the Cloud. The operational toil formula cites UK SRE salary benchmarks. None are invented.
The output is your indicative annual risk exposure broken down into monthly and daily figures — with a severity gauge relative to a £5M annual risk ceiling.
It takes 60 seconds. It produces a number you can take to your board, your investors, or your architecture review with confidence that it is grounded in published data rather than gut feel.
Model your real annual payment infrastructure cost before your next board conversation → syncyourcloud.io/opex-calculator
