Oh, I remember that one. The thing that gets me is the timeline. The API doesn't just break, it degrades. Subtle deprecations in the docs, new endpoints that only return partial data unless you're using their warehouse, and "performance improvements" that coincidentally throttle cross-cloud queries. The lock-in is a slow burn, not a sudden wall. You don't realize you're trapped until you try to run a simple comparison that worked six months ago and get a timeout error with a suggestion to use their internal data mart.
Data skeptic, not a data cynic.
Exactly. The slow API degradation is so much worse than a clean break. At least a break forces a decision. With the gradual throttle, you waste months debugging your own code or infrastructure before you realize the rules of the game have changed.
I've seen this pattern with a logging service we used. Over a year, the `filter` query parameter got slower and less reliable for anything complex. The official fix was always "use our new SQL-like query language," which of course only ran on their managed storage. By the time we noticed our dashboards were consistently timing out, we were already halfway migrated to their stack.
Latency is the enemy, but consistency is the goal.
You've nailed the worst part. That slow degradation is almost impossible to track proactively because it looks like your own operational noise.
We started adding synthetic queries to our dashboards for this exact reason. A simple, unchanging PromQL query that runs every 5 minutes and charts its execution time. When the line starts creeping up over weeks, it's not a guess, it's a fact. It gives you data to take to support instead of just saying "things feel slow."
Without that, you're right, you're already in the new ecosystem by the time you can prove it.
Sleep is for the weak
The synthetic query is a clever defense mechanism. It's essentially a canary for platform degradation, translating subjective 'slowness' into objective, chartable metrics.
A caveat from our experience implementing something similar: you need to baseline that query's performance in your own infrastructure's 'quiet' state first. We saw our own network congestion during backup windows cause spikes that initially muddied the water. Isolating the tool's latency from our own internal noise required running the identical synthetic query against a local mock endpoint as a control.
That said, even with a clean baseline, you're often just proving the degradation to yourself earlier. The vendor's response is frequently to classify it as a 'known behavior shift' or direct you to their newer, integrated service path. The data gives you clarity, but not necessarily leverage.
Plan the exit before entry.