This post is about per-tenant rate limits for SaaS platforms. It started, as most of our posts do, with an incident that did not go the way we expected.
Lessons for your own runbook
Know who can change DNS at two in the morning. Know your origin IPs and who can rotate them. Know which routes are expensive, and have a rate limit ready for each of them.
Most outages during attacks are not caused by the attack itself but by rushed changes made while under pressure.
Challenges beat blocks
Blocking lists go stale within minutes when attackers rotate through residential proxies. Proof-of-work does not care where a request comes from; it only cares whether the client is willing to pay the cost.
For a real browser that cost is a few hundred milliseconds, once per session. For a botnet sending a million requests a minute it is a million puzzles a minute — and at that point the attack stops being cheap.
What we changed
We moved the decision from a single threshold to a continuous score, added an explanation to every decision and made every rule testable against historical traffic before it goes live.
The result is fewer late-night pages for our analysts and — more importantly — fewer real users challenged by mistake.
What we got wrong
Our first version challenged too eagerly on mobile networks, where thousands of real users share a handful of carrier-grade NAT addresses. Reputation that is shared is reputation that is noisy.
We now weight fingerprint consistency and session behaviour far more heavily than IP reputation for traffic from known mobile carrier ranges.
Compliance is a side effect
Regulators increasingly ask for evidence of resilience, not just promises. An incident timeline with start, peak, vectors and impact is exactly the evidence DORA and NIS2 ask for — and it falls out of good observability for free.
We export incident reports in formats auditors can file without anyone rewriting them.
If you run into any of this in your own environment, our SOC is happy to take a look — even if you are not a customer.