Database instance rightsizing tracker for engineering managers

An engineering manager or platform team lead gets the AWS bill and sees the RDS line item growing. They ask the team why, and the honest answer is 'we scaled up because it was slow and we couldn't figure out why.' Users say this plainly: 'no automation around performance tuning and cost' in the same breath. The scaling decision was made under pressure and never revisited. Nobody owns the question of whether the instance is still appropriately sized three months later.

This gap exists because the people who feel the cost pain — managers, finance, platform leads — are not the same people who have access to query-level performance data. The people who do have that access — DBAs, backend engineers — are not accountable for the bill. This buyer-user split means nobody is doing the cross-referencing. AWS Cost Explorer shows the spend but not the utilization pattern that justifies or condemns it. CloudWatch shows CPU but does not tell you that 60% average CPU on a db.r6g.4xlarge is entirely caused by two unindexed queries that could be fixed in an afternoon.

Users complain about 'limitations on troubleshooting performance history' — they cannot go back and understand what was happening on the database the week before they scaled up. That historical gap means scaling decisions are made with forward-looking fear, not backward-looking evidence, and they are almost never walked back.

This is a business because cloud database spend compounds. An over-provisioned instance renewed month after month, across multiple environments, adds up to tens of thousands of dollars a year at a mid-size company — and the engineers closest to the data have no tool that connects performance waste to dollar cost in a way that is presentable to anyone who controls the budget.

What to build

Pull RDS CloudWatch metrics and cost data via AWS APIs, identify instances where average CPU and memory utilization have been consistently below 40% for 30+ days, correlate with query log patterns to distinguish genuine headroom from masked inefficiency, and generate a monthly rightsizing report with specific downgrade recommendations and projected annual savings — formatted for an engineering manager to share with finance.

Where to start

Start with companies using AWS Organizations who have multiple RDS instances across dev, staging, and production — the low-hanging fruit is flagging over-provisioned non-production instances where the risk of a bad recommendation is near-zero, which builds trust before touching production.

The hard part

The recommendation is only trustworthy if it accounts for traffic spikes and peak load — a manager who downgrades an instance based on your report and then gets paged at 2am will never trust the product again, so the spike detection logic has to be conservative and explainable before you ship.

How it makes money

Monthly flat fee based on number of RDS instances monitored, starting at $200/month for up to 20 instances — positioned as saving its own cost within the first month for any customer with meaningful over-provisioning.