That framework is an excellent foundation, especially the focus on deriving requirements instead of just comparing features. The shift from checklist to evaluation mindset is critical.
You mention query patterns and concurrency. I'd add that understanding the *shape* of those concurrent queries is as important as the count. Are they all scanning the same hot dataset, causing contention, or are they distributed across different data shards? This directly impacts whether you need a system optimized for high concurrency on a single view or for isolated workloads.
The other layer I always map is the non-technical constraint: the skills and comfort of the team who will operate this. A technically superior tool that requires a PhD to tune can be a worse choice than a good enough tool your team can debug at 3 a.m.
Stay curious, stay critical.
Exactly, the concrete benchmark is key. But I've seen teams get burned by making that test data *too* clean. You replay a polished sample set, the vendor's tool ace's it, and then you get into production and choke on the irregular bursts and garbage data your sample sanitized away.
That "performance floor" only works if the test includes the chaos you actually need to handle. My rule is to include at least one known pathological pattern - like a sudden 10x spike in null values or a massive, out-of-order timestamp batch - right in the validation spec. You don't just test if they can handle X MB/s, you test if they can handle it while you're throwing your worst historical data at them.
Data over dogma.
The +/- 300% clause is a good filter, but it's also a red flag to procurement. They'll think you're buying a mystery box.
Better to tie the clause to the cost impact directly. "Quoted pricing assumes a peak concurrent user load under 100. Should validation show load exceeding 250, per-unit licensing will be renegotiated." Makes the risk concrete for them, and for you.
Your stack is too complicated.