Has anyone quantified the latency added by AWS WAF on an ALB?

You're spot on about the dashboard pitfall. I built a quick Grafana panel that sums all rule durations, terminating or not, and it was an eye-opener. We found a "count-only" IP reputation rule that was taking 12ms per request because it was checking against a massive, poorly formatted list. The terminating rule after it was fast, so we almost missed it.

It makes you question the whole "count before block" strategy if those count rules are expensive. Sometimes letting a cheap rule block immediately is better than letting the request bleed latency through a dozen count checks first.

Exactly right about the isolation testing. That baseline ALB without WAF is crucial, because your "before" numbers can drift from AZ load or instance health.

One caveat I'd add is to watch your traffic patterns during the test. If you're benchmarking against synthetic or cached requests, you might miss the real-world impact on a request with a large, unique JSON body. Those `JsonBody` inspections can behave very differently under load versus a simple GET.

The graduated rollout is the way to go. Sometimes the P99 delta from the core rules alone is enough to make you reconsider if you even need custom rules for your specific risk profile.

The point about testing with cached vs unique JSON bodies is really smart. Our main checkout flow is a POST with a pretty big payload, so that's probably a big difference.

When you talk about a graduated rollout, how do you handle splitting traffic like that? Is it just a separate test ALB set up for some percentage of users, or is there a way to test rules more directly on a subset of live traffic before going all-in?

I don't have production numbers myself, but I'm in a similar boat trying to make a recommendation to my team. The graduated rollout idea mentioned here seems crucial.

For benchmarking, wouldn't it make sense to also look at the cost impact? I mean, if latency increases, you might need more instances to handle the same load. Has anyone tried to estimate that operational cost against the security benefit for their specific use case?

You're right to connect latency to cost, but I'd separate the two analyses initially. The latency increase directly impacts user experience and error rates, which are your primary concerns. Scaling up instances is a potential mitigation, but it's a secondary, reactive cost.

I've found it more useful to first quantify the latency's impact on your business metrics, like conversion rate or session duration. That gives you a dollar value for the performance degradation. Then you can compare *that* to the security benefit and the cost of extra instances. Adding instances might solve capacity but won't fix a degraded experience.

In our case, the math showed that even a 5ms P99 increase on a key flow was more expensive than a modest security incident we were trying to prevent. It forced us to prune the rule set aggressively.

Great question, and coming from a Salesforce background you've got the right instinct for data. I ran these exact tests on our staging ALB last month.

The "low single-digit ms" is real for the bare bones setup. With just the Core Rule Set, our P50 hovered around 3ms. But that's the floor, not the ceiling. The heavy rules are usually the ones you write yourself, especially regex or JSON parsing rules on large POST bodies. I saw one custom regex for path traversal attempts add 8-10ms all by itself.

The best methodology is to start with the CRS baseline, then add custom rules one by one in a test environment. Use the `terminatingRuleMatchDuration` from the logs, but also look at the total latency for requests that *don't* trigger a block. That's where you see the hidden cost of non-terminating rules just counting things.

That's exactly the kind of detailed, real-world breakdown I was hoping for, thank you! The distinction between the "floor" of the Core Rule Set and the "ceiling" with custom rules is super helpful.

Coming from Salesforce reporting, I think I get it: it's like adding a new validation rule to a massive object. It might seem cheap in isolation, but on a high-volume page, the cumulative effect is real.

My big takeaway is to start with the CRS baseline, measure, and then add custom rules one by one. The hidden cost of non-terminating rules seems like a real trap for the unwary. I guess my follow-up is, when you say to measure the latency for requests that don't trigger a block, is that mainly to see the cost of those "count-only" rules?

Yes, measuring the latency for requests that pass through without a block is precisely how you uncover the cost of count-only rules. The logs for a request that isn't blocked will still list every matching non-terminating rule and its duration. Those milliseconds are pure tax on every allowed request.

The Salesforce analogy is apt. It's the operational cost of a validation rule that runs on every record save, even when it finds no issue. The cumulative load from those checks can become significant, which is why ordering rules from cheap to expensive is a common optimization. You'd want a fast, terminating IP block rule to fire before a slow, count-only regex scan, for instance.

Have you considered what your threshold for an acceptable performance tax might be before the security value of a count-only rule is negated?

Determining that threshold is the core strategic challenge. You have to model it against your specific transaction value. A 5ms tax per request might be negligible for a B2B SaaS admin panel, but it directly erodes margin on a high volume, low margin e commerce checkout where milliseconds correlate to conversion.

One approach is to treat count only rules as an investigative tool with a sunset clause. You deploy them to gather data on a suspected pattern, but the rule's value proposition decays over time. If the data doesn't justify a hardened, terminating rule within a set period, you retire the count rule. The ongoing tax for a "just in case" monitoring rule is rarely justified once you quantify its cumulative cost against genuine attack vectors.

Spot on about tying the threshold to transaction value. That's exactly how we made the case to our product team. We showed them a simple graph of added latency against abandoned cart data from our main flow. Seeing the potential revenue impact made the security vs performance trade-off very tangible.

The sunset clause idea for count-only rules is brilliant. We've started calling those "exploratory rules" and they automatically generate a ticket for review after 30 days. If the data doesn't show a real threat pattern, they get deactivated. It forces us to be disciplined and stops those rules from becoming permanent background tax.

One extra layer we add: we also track the cost of those rules in CloudWatch, not just latency. A rule that adds 2ms but runs on millions of requests a day can still blow your budget. Sometimes the financial cost is the easier argument to win.

Love that you're also tracking rule cost in CloudWatch. That's the operational reality check a lot of teams miss. A "cheap" rule on paper can be a budget monster at scale.

It makes me wonder about tagging or grouping rules in cost allocation reports. If you could show the total monthly WAF cost broken down by "core security" vs "exploratory monitoring," it makes the argument for those sunset clauses even stronger.

Have you set up any automated alarms on that cost metric, or is it more for periodic review?

Grouping costs is smart, but I think the premise that "exploratory monitoring" is a distinct, justifiable category is flawed. The moment you start tracking it separately, you're already justifying its existence as a budget line item.

It's not a security tool, it's a procurement failure. You're paying AWS for the privilege of figuring out if you need to pay AWS more. That's vendor lock-in dressed up as due diligence. The real cost isn't just the CloudWatch bill, it's the engineering cycles spent building this entire sunset system instead of evaluating a simpler, deterministic security model upfront.

If you need automated alarms on your monitoring costs, you've already lost.

You're coming at this from exactly the right angle with your data background. The "low single-digit ms" line is marketing fluff that obscures the real architecture decision.

The latency isn't the core issue, it's the cognitive load and lock-in. Every custom rule you write, every regex you think is clever, becomes a permanent fixture. The methodology of adding rules one-by-one is how you end up with a 50-rule monstrosity that adds 40ms of overhead, which you then have to "solve" by scaling your infrastructure.

Where to start? Don't benchmark WAF. Benchmark your actual application's error rates and attack surface first. Quantify the real risk. You'll often find the performance tax of a bloated WAF is higher than the cost of just fixing the three stupid SQL injection flaws in your legacy endpoints.

That's a really smart way to frame the question from a data background. When you said you're not sure where to start with benchmarking, have you thought about the physical region of your ALB?

I'm just starting to look at this too, and I heard that the WAF inspection happens in specific AWS locations, not necessarily where your ALB is. Could that add more variable latency than just the rule processing time itself?

Exactly right. Measuring latency for passing requests exposes the tax from every "Count" action. Those are the rules that run but don't block, adding cost to every valid transaction.

Your Salesforce validation rule analogy is perfect. And like in Salesforce, you have to ask if the rule is inspecting the right thing. A slow regex count rule on a URL parameter might run on every single page load, while a faster terminating rule on a specific header in a POST request is more surgical.

Have you looked at rule ordering yet? Even with the CRS, putting the faster, more likely to block rules first can shave off those cumulative milliseconds.