DBaaS cost forecasting for engineering teams

The moment engineers realize they have this problem is when they get a surprise bill after a traffic spike, or when a finance team asks them to justify next quarter's database spend and they have no honest answer. They have usage metrics. They have invoices. But nothing connects those two things forward in time with enough specificity to be useful for planning or for architecture decisions.

The gap persists because the managed database vendors have a structural incentive not to make cost too transparent or predictable — opacity keeps customers from optimizing too aggressively or switching. They surface dashboards showing current spend, but they don't model what happens if your data doubles, or if you add three new consumers, or if you switch from provisioned to on-demand capacity. That modeling requires understanding the vendor's own pricing math in detail and combining it with the customer's actual growth trajectory, which is awkward for an incumbent to expose because the answer is often 'you're about to spend a lot more.'

Existing cost monitoring tools give per-service alerts and historical breakdowns, but engineers complain specifically that 'pricing can become unpredictable with high workloads, especially with on-demand provisioning and data transfer costs' — meaning the problem is forward-looking, not backward-looking. Knowing last month's bill doesn't help you decide whether to shard your DynamoDB table today or wait.

This is a recurring business need, not a one-time audit. Every time a team launches a new service, onboards a new customer, or scales a feature, they need to re-run the cost math. Without a reliable way to do it, they either over-provision (wasteful) or under-provision and get surprised. Engineering leads get asked to defend their cloud database bills in quarterly business reviews. That cycle repeats every three months forever.

What to build

Build a web app that ingests AWS Cost Explorer data and CloudWatch metrics for DynamoDB, ElastiCache, and MemoryDB, then models forward cost under user-defined growth scenarios — 'what does my bill look like if read throughput doubles?' — with output formatted for engineering-to-finance communication.

Where to start

Start narrowly with DynamoDB because it has the most notoriously complex and punishing cost model ('if the initial architecture is not set up correctly, it becomes very difficult to avoid scans which are very costly'), meaning engineers are already scared of it and actively looking for help before they get burned.

The hard part

Each DBaaS pricing model has enough edge cases — reserved capacity vs on-demand, data transfer tiers, scan vs query costs — that accurately modeling future spend requires building and maintaining a pricing engine per service, and AWS changes pricing terms without much notice, so your forecasts silently go stale.

How it makes money

Monthly subscription charged per AWS account connected, starting around $49/month for a single account with a usage-based tier above a threshold of tracked services.