How Non-Technical Founders Can Plan Their Cloud Budget for the Next Funding Round

Frequently Asked Questions

01.Should startup cloud credits count as revenue or as a cost saving in a fundraising model?

Neither line should hide them. Our approach is to model the full cloud bill at list prices as a cost, then show credits as a separate, dated funding line that ends on the expiry date. AWS's Promotional Credit terms say credit is valid for a limited time and is forfeited if not redeemed before its expiry date (AWS Promotional Credit Terms, October 2026), so an investor will treat credits as temporary, and your model should too.

02.Can AWS credits pay for Savings Plans or Reserved Instances?

Not the upfront part. AWS's Promotional Credit terms exclude any upfront fee for services such as Savings Plans and Reserved Instances, along with AWS Marketplace, Professional Services, Training, Certification and some support plans (AWS Promotional Credit Terms, October 2026). An all upfront commitment is therefore paid from cash, which shortens runway on the day you buy it. Check the terms in force for your account before purchasing.

03.How much of a startup's cloud spend should be committed in advance?

Our rule of thumb is to commit only the baseline your product has used every hour for the past three months, and leave growth and experiments on demand. On AWS, usage beyond a Savings Plan commitment is charged at regular On-Demand rates (AWS Savings Plans FAQ, October 2026), and each hour's commitment can only be used within that hour (AWS Savings Plans User Guide, October 2026), so under-committing costs a premium on the excess, while over-committing leaves paid commitment unused through a slow quarter.

04.What cloud numbers do investors ask for in due diligence?

In our view the requests cluster around four items: monthly cloud cost split between cost of revenue and research and development, cost per customer or per request over time, the credit balance with its expiry dates, and forecast against actual for recent months with an explanation of each large variance. A founder who can produce these from tagged billing data answers most hosting questions in one meeting.