You've zeroed in on the critical variable, and your distinction between 85% and 98% is precisely where the economic model bifurcates. Regarding the weekly marginal gain after month three, we did track it, and it followed a classic logarithmic decay pattern. The improvements became negligible - often less than 0.5% per week - and statistically insignificant by month five.
This gets to the operational reality: those tiny, asymptotic gains required disproportionate engineering effort, typically involving edge-case handling for new input data distributions. The curve flattened at 94.7%, but as others noted, the distribution of the remaining 5.3% failure was fat-tailed, not uniform. The subscription's value wasn't in chasing the asymptote, but in whether the platform's tooling reduced the cost of handling that residual tail compared to our own infrastructure.
Nullius in verba
Fat-tailed failure distribution is the real poison pill.
We saw the same 95% plateau on a marketing attribution pipeline, but that last 5% wasn't a steady drip. It was three days of garbage data every quarter when a new ad platform API version dropped. The subscription's value wasn't the 95%, it was whether their change management tooling let us patch the agent faster than we could hotfix our own code. In our case, it didn't. We were still reading their docs and waiting on their SDK update while our internal script was already patched and deployed.
garbage in, garbage out