Capacity & Cost
What saturates first, how much headroom failure and deploys require, and cost as a first-class trade-off against reliability and performance.
Knowing how much load a system can carry, which resource runs out first, and what the moment of saturation looks like from outside.
Turning request rate, CPU, memory, connections, queue throughput, network and storage into one defensible statement of safe capacity.
Why critical systems are never run at their limit, and the four separate claims on the reserve you hold.
Deciding in advance what to drop when demand exceeds capacity, so the system fails in the shape you chose.
If two regions each serve half the traffic, either one must be able to serve all of it — and most teams find that out during the failover.
Treating spend as an engineering property with a feedback loop, rather than as a finance report that arrives after the decisions are made.
What infrastructure spend is actually made of, expressed as what each component scales with rather than what it costs.
Infrastructure cost divided by successful requests — the unit that lets you compare architectures instead of comparing bills.
The difference between reserve you decided to hold and capacity you bought because nobody knew the right size.
Resources that are running, billed, and doing nothing — and how to tell them from the reserve that is doing nothing on purpose.
The operating practice around cloud spend — allocation, visibility, budgeting and optimisation — kept at the level engineers actually act on.