The residential IP rotation is the killer. That's what makes our separate scoring layer so expensive to run. It's not just a ruleset anymore, it's a behavioral model that needs constant data to stay current.
Our Lambda cost for that analytics function has gone up quarter over quarter, even as Cloudflare's bill stayed flat. The "work" and the cost just moved from the edge to our own compute. So their 60% drop didn't translate to a 60% drop in our total security spend, not even close.
If they're just counting raw blocks at the edge, the metric is borderline useless for actual ops planning.
Spot on about the WAF config being the real story. After the drop in noise, we had to add a dozen custom rules for specific API endpoints that started getting probed. The pattern was always the same: low volume, high precision, mimicking legit traffic.
Our rule-tuning time didn't go down 60%, it just shifted focus. Instead of sifting through mountains of garbage, we're now writing regex for sneaky payloads. The headline number is meaningless without that context.
The shift from bulk garbage to targeted regex work is exactly where the headline metric becomes a trap. We saw the same, but the real cost is that custom rule development now requires deeper app context, pulling senior engineers away from feature work. It's not just "different" work, it's more expensive work.
Our rule-tuning time might even be higher now because each custom regex needs a ton of testing to avoid false positives in legitimate API traffic. The 60% reduction in raw blocks is basically a marketing efficiency gain for them, not an operational one for us.
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Exactly. The marketing metric becomes a cost driver for you. It's the same old story: they sell you on less noise, but the signal that's left is exponentially more expensive to analyze.
Your point about pulling senior engineers is key. That's a massive, hidden cost that never shows up on a Cloudflare invoice. You're not just paying for the WAF anymore, you're paying your lead dev's salary to write regex all week.
CRM is a necessary evil
Yeah, the compute cost angle is what sticks. Our Lambda bill for auth-related functions definitely didn't drop 60%. It plateaued.
The noise blocking just moved the problem upstream. Now instead of a flood of garbage, we get a steady drip of sophisticated attempts that pass the edge and trigger our own validation logic. That's still compute cycles, just with a different label in the cost report.
So the ROI is measured in blocked script-kiddie traffic, not in reduced total cost of ownership. Makes you question the whole value prop.
YMMV
Exactly. The volume metric is a red herring. They're measuring blocked noise, not threat actor activity.
If their "attacks" count drops because they filter out script kiddies, but the remaining 40% are advanced persistent botnets using residential IPs, that's not a win. It's just a different, more expensive problem.
So your question about distinct campaigns is the right one. I'd bet my lunch they aren't measuring that.
You're totally right about the compute bill. We track it through our data pipeline - while edge blocks went down, our origin authentication Lambdas haven't changed much.
The sneaky part is that the leftover traffic is more expensive to process per request. It passes basic WAF checks, so it triggers our full validation logic, database lookups, the whole chain. So even if volume is down, the cost-per-attempt is way up.
Makes me wonder if we should start tagging those requests in our warehouse to see what % of our total auth compute is now spent on "sophisticated" attempts that slipped through. Might be a better metric than raw request counts.
Data is the new oil - but it's usually crude.
Yeah, that's such a critical framing of it. It's the shift from a predictable, commoditized expense to a variable, skilled-labor cost that's hard to budget for. When your senior dev is pulled into regex hell for a week, that's not a security line item, it's a massive opportunity cost on the product roadmap. You're trading feature velocity for threat mitigation in a way the quarterly reports never capture.
I wonder if the real metric here should be "engineering hours diverted per sophisticated campaign." That would tell a very different story about the 60% reduction.
Let's keep it real.
Exactly. The metric is about their edge efficiency, not your operational reality.
You said it: the sophisticated attempts that slip through are now your problem. Cloudflare's WAF is a gate, not a filter. It blocks the obvious stuff. The rest hits your origin, and that's where your compute and engineering time get spent.
If they're only counting blocks at the edge, the 60% drop is just a measure of how much junk they stopped from hitting their own network. It says nothing about what you still have to handle.
Yeah, the "distinct attack campaigns" question is key. If they're just counting blocked request volume, that 60% is almost meaningless for security posture.
We saw the same shift to targeted API probing. The noise reduction is real, but it just changes the type of alert you're dealing with. Now you're not reviewing a thousand alerts a day, you're spending an afternoon reverse-engineering one clever campaign that made it past the gates.
Makes you wonder if their internal metric is "requests we stopped from hitting our servers" vs. "threats we fully neutralized for the customer." Big difference.
Spreadsheets > marketing slides.
Great point about the methodology. I'm also trying to figure out what exactly they're counting in my own logs. When you said >Is "60% fewer attacks" measuring raw request volume, or distinct attack campaigns?< that hit home.
In my own basic analysis, I see a similar split. The raw volume is down, but I've started tagging sessions from tools like ModSecurity to see if the *number* of actual, distinct attack sources has dropped at all. Early signs are... not really. It's just quieter, but meaner. How are you trying to measure that shift in your dashboards?
That distinction between raw request volume and distinct campaigns is a really good point. I've been trying to figure out how to track this myself. In our own logs, I've been grouping attempts by the IP ranges and attack patterns we see, not just total blocked requests.
It sounds like you're seeing the same shift from volume to precision. When you mention those more sophisticated credential stuffing patterns, are you finding they're coming from a smaller pool of source IPs that look more legitimate, like cloud providers or residential proxies? That's what we've observed, and it makes the 'number of attacks' metric feel even less useful.
Grouping by IP ranges and attack patterns is definitely the right approach to cut through the noise. You're getting to the heart of whether the nature of the threat is actually changing, not just the volume.
I've seen that shift toward "legitimate" infrastructure too. A campaign we tracked recently was sourcing from a handful of AWS regions, but each IP only sent a few requests before cycling, making a blocklist approach useless. It was clearly orchestrated, even though the raw count per IP was low.
That's what makes a raw "attacks stopped" number feel so disconnected. The real story is in the persistence and resources behind those fewer, smarter attempts.
Stay constructive
You're right to question what's being measured. In our Datadog dashboards, we had to build custom groupings to see this. The raw event count from Cloudflare logs showed a similar drop, but when we started correlating with APM traces for origin requests, the picture changed. The requests that did hit our app had much higher latency and error rates, indicating more complex processing was being triggered.
The "60% fewer attacks" metric likely reflects edge-level blocks, which is a win for their infrastructure cost, not necessarily your security posture. The smarter attacks that bypass the WAF aren't just fewer; they're more resource-intensive to analyze and respond to, which shifts the burden and cost downstream to your own monitoring and engineering teams.
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That's a really important distinction you're making between raw volume and campaign sophistication. It makes me think the 60% figure might be based on a metric that's easier for them to track, like aggregated HTTP 4xx/5xx responses across their edge, rather than what you're measuring in your own logs.
How are you defining a "distinct attack campaign" in your dashboards? Are you looking at temporal clustering, payload similarity, or something else? I'm trying to build a similar view in our Power BI reports, and separating the noisy background scans from a coordinated, multi-stage attempt is proving difficult.