Database spend estimator for teams before they deploy

The problem surfaces before a single line of code is written: a small engineering team or a solo developer is deciding which managed database service to use, and they have no reliable way to estimate what they'll actually pay under realistic load. They look at pricing pages, see numbers that mean nothing without understanding what 'storage I/O credits' or 'ACU hours' translate to for their workload, and either guess wrong or default to a name they recognise and overpay for years.

This gap exists because pricing pages are marketing assets, not engineering tools. The vendors have no incentive to build a honest 'here is what this will actually cost you' calculator — any realistic estimate risks losing the sale to a cheaper competitor. And the people who could build this (cloud cost consultants, DevRel teams) have their own misaligned incentives. So engineers do the math on spreadsheets, get it wrong, and complain that 'pricing is complicated and costly' and 'expensive, pricing can be hard to estimate.'

The specific failure is that no current tool lets you input your actual workload characteristics — expected row counts, query volume per second, storage growth rate, read/write ratio — and get a side-by-side monthly cost estimate across RDS, Aurora, Aiven for PostgreSQL, and similar services under those conditions. You can find per-service calculators with limited inputs, but they don't translate workload behavior into billing units, which is where all the surprise costs live.

This is a business because the decision about which database service to use is made repeatedly — every new project, every migration conversation, every 'should we move off this vendor' review — and getting it wrong locks a team into overpaying for 12-24 months before the pain is large enough to justify a migration.

What to build

Build a web-based workload-to-cost modeler where engineers input rows, QPS, storage growth, and read/write ratio and get a monthly cost estimate with confidence intervals for RDS MySQL, RDS PostgreSQL, Aurora, and Aiven, with line-item breakdowns showing which billing dimension dominates their specific workload.

Where to start

Launch with a narrow focus on PostgreSQL-compatible options only (RDS PostgreSQL vs Aurora Serverless v2 vs Aiven for PostgreSQL) — this is the most common decision engineers face right now, the pricing differences are substantial and confusing, and a correct answer here builds enough trust to expand to MySQL and others.

The hard part

Pricing models for Aurora, RDS, and Aiven change often enough that keeping the underlying data accurate is a continuous maintenance burden — if the estimates are stale or wrong, credibility collapses immediately, so you need a reliable update process before launch, not after.

How it makes money

Free for estimates up to three comparisons per month; paid subscription at a flat monthly rate for unlimited estimates, saved scenarios, and cost-over-time projections — targeting the engineering manager or FinOps person who runs this analysis regularly.