Guide: Reducing Prolexic costs by tuning thresholds for our specific app.

Agreed, but you're only focusing on volumetric thresholds. The bigger waste is often in the application layer rules. Prolexic's default HTTP anomaly detection is tuned for common web attack patterns, which might include things like aggressive crawler blocking or SQLi heuristics. If you're a headless API service, half those rules are irrelevant noise. You can strip them out entirely and just focus on the vectors that could actually disrupt your service, like malformed JSON payloads or connection exhaustion on your specific endpoints. It's a more surgical, and cheaper, approach.

Spot on about the baseline analysis. That's the step most teams skip because it's tedious. We built a simple dashboard pulling packet rate and connection metrics from our edge routers into Prometheus, then compared it to Prolexic's trigger logs over a month. The mismatch was laughable.

We found their default SYN flood threshold would trip at 10k packets/second, but our normal peak was around 300. Bumping that threshold up saved us from a dozen false mitigations a week, which added up fast.

>Do you really need to trigger on a 5 Gbps SYN flood when your entire daily ingress is 500 Mbps?

Exactly. Our CFO approved the dashboard project once we showed him the cost per false positive.

The Prometheus dashboard is a smart move. I've seen teams try to do this by hand with CSV dumps and it always falls apart after a week.

One caveat: don't just look at the average. You need to graph the 99th percentile over a full business cycle to catch those weird, legitimate spikes. If your quarterly reporting tool runs on the first of the month and triples your normal packet rate for two hours, that's your real baseline.

Your CFO point is key. These conversations always go smoother with a graph showing money wasted per month.

Totally agree on the need to watch for those periodic spikes, that's the kind of thing that gets missed. Building that 99th percentile view over a full cycle is crucial.

But it gets even more specific: you have to separate spikes by customer or even internal service. Our main app's baseline is steady, but we have one big client whose batch jobs cause a 4x traffic spike every Sunday night. If we'd tuned based on the overall app's 99th percentile, we'd still be paying to mitigate "anomalies" that are just Bob from Accounting's weekly upload. We ended up creating separate traffic profiles for our top five clients to set intelligent thresholds.

Graphing the money wasted is the ultimate persuasion tool, though. We called ours the "Lazy Tax Dashboard" and it's the first thing on our infra review deck now.

Your point about volumetric thresholds being a blunt instrument is exactly right. We ran a similar analysis on our internal APIs, but we also benchmarked the latency penalty of the mitigation itself when it triggered.

Even a false positive where Prolexic kicks in for 90 seconds can add 200ms of jitter to our 99th percentile response times, which violates our SLOs. So the cost isn't just the bill from Akamai, it's the hidden performance tax on our users during those unnecessary mitigations. Tuning the thresholds helped our CFO and our performance dashboard.

Absolutely. That 40% savings figure really drives the point home. I've seen similar numbers when teams finally get granular with their baselines.

One thing that made our tuning exercise click was mapping our specific app architecture to the vectors. For example, we don't even *use* UDP-based services, so we could safely disable thresholds for UDP flood and reflection attacks entirely in Prolexic's policy. That alone carved out a huge chunk of "insurance" we were paying for. It sounds obvious, but you'd be surprised how many policies are just left on the vendor defaults.

Your point about working backwards from their defaults is the only way. It turns a black-box cost into a manageable engineering variable.

That 40% savings is really compelling. You mention working backwards from the defaults, but how did you actually get started with the baseline analysis? Did you have to work with Prolexic support to get the right logs, or did you build it all from your own monitoring?

As someone newer to this, I'm worried about missing a real attack while we're tuning. How did you decide what was a safe initial adjustment?

Yes, graphing the 99th percentile is the only way to see the real shape of your traffic. That's where a good time-series database shines over hand-crafted reports.

One nuance we ran into was that our "full business cycle" needed to be longer than we thought. We had a quarterly spike, sure, but we also had a yearly contract renewal period for several big clients that created a massive, week-long traffic pattern we'd never have caught in a month of logs. Miss that, and you're right back to false positives.

That CFO graph is gold. We annotated ours with the exact cost of each major false-positive mitigation event. Seeing a $5k charge for blocking "Bob's weekly upload" really focuses the mind.

The 40% cost reduction you achieved is a solid validation of the approach. Your question about triggering on a 5 Gbps SYN flood is particularly apt because it highlights the disconnect between generic defaults and actual capacity.

One layer deeper than the daily ingress baseline is the *rate of change*. A sudden spike from 500 Mbps to 5 Gbps is an obvious anomaly, but for a growing service, the legitimate baseline climbs. We implemented a simple moving window that recalculates a "normal" range every week, using the 99th percentile of the prior 28 days. This prevents you from having to manually adjust thresholds upward every quarter as organic growth makes yesterday's safe threshold too sensitive.

The real risk, as you alluded to with security theater, is that an over-sensitive policy can itself become a DoS vector if an attacker learns to consistently trigger expensive mitigations. Tuning isn't just about cost savings, it's about reducing your attack surface.

Completely agree on the principle, but I'd push back slightly on calling it "lazy" to start from the defaults. For a lot of teams, especially smaller ones, those defaults provide a critical safety net while they're building the internal monitoring capability you described. The real issue is treating them as permanent.

Your 40% savings is the perfect case study. The leap from "set-and-forget" to "tuned-and-managed" requires that internal data maturity, which you clearly built. My caveat would be that without that maturity, starting from scratch can be riskier than overpaying for a month or two while you instrument everything.

The B2B SaaS vs. media streaming example is spot on. It's the classic vendor one-size-fits-all problem. I've seen similar savings by just disabling entire vectors, like UDP floods for apps that are TCP-only.

That 40% savings figure is the perfect catalyst for action. I've found that kind of concrete number is what finally gets budget approval for the tuning work.

Your point about working backwards from the defaults is exactly right. We took a similar approach, but started by literally listing every single DDoS vector Prolexic could mitigate. We then went through each one and asked, "Does this protocol or service even exist in our stack?" Like user122 mentioned, we found several (looking at you, SSDP and CharGen reflection) that we could just turn off entirely, because there was nothing in our architecture for them to attack. That was the easiest win.

The balancing act between security and cost gets easier once you have those traffic profiles built. We ended up creating a simple checklist for each threshold adjustment: expected traffic pattern, worst-case cost of a false positive, and the actual risk profile of that vector for us. It turned a scary black box into a manageable engineering decision.

That 40% figure is such a powerful testament to this approach. It really does come down to the question you ended with - balancing real risk against a generic policy. I think a lot of folks don't realize that tuning isn't a one-time "set it lower" action, it's an ongoing alignment with your actual business rhythm.

Your B2B SaaS versus media streaming example is perfect. It reminds me of teams that leave the "DNS flood" thresholds cranked up when they're not even running authoritative DNS servers. It's paying for a guard dog for a house you don't own.

The one caveat I'd add is that while building your own baselines is crucial, it's worth keeping a slightly wider safety margin for new, unforeseen growth channels. I've seen a sales push or a new integration partner create a legitimate traffic pattern that looked exactly like an attack because the baseline was too narrowly defined. But that's a good problem to have - it means you're growing 😊

You hit the nail on the head with the lazy remark. That initial "set and forget" posture is understandable for a brand new deployment, but letting it ride for months is what bleeds budgets. I'd add that you also need to loop in your finance team early with that kind of baseline analysis.

A B2B SaaS traffic profile is worlds apart from a gaming platform. One trap we avoided was forgetting about scheduled business processes. Our own "predictable" API calls included a nightly batch job that pulled a week's worth of analytics for a client, which looked exactly like a low and slow attack if you only looked at the hourly graph. Had to baseline over a full week to catch that rhythm.

That 40% savings is fantastic, but I'm curious if you've started pressure-testing those new thresholds with any kind of controlled simulation? It's the final step that gives you the confidence to truly own the policy instead of just renting it.

Calling it "lazy" might be too harsh for teams just starting out, but it's spot on for anyone past the first billing cycle. The vendor's defaults are a starting point, not a destination.

Your media streaming versus B2B SaaS comparison is the entire argument. If you don't run UDP services, paying to mitigate UDP floods is pure waste. It's not just tuning thresholds, it's turning entire vectors off.

That 40% savings is the proof. Finance teams need to see that graph.