Hi everyone,
I've been deep in the weeds of our AWS WAF configuration for the past few months, trying to strike that perfect balance between robust security and minimal performance impact. We're using a combination of AWS Managed Rule groups and our own custom rules. While the CloudWatch metrics give a good high-level view of overall WAF performance (allowed vs. blocked requests), I've found it incredibly challenging to get granular visibility into the performance cost of *individual rule groups*, let alone specific rules.
The core issue is that latency introduced by the WAF seems to be bundled together. When I look at our application latency graphs alongside WAF block counts, I can see a correlation when we enable a new group, but isolating the exact contributor is guesswork.
Here’s my current approach and the specific gaps I'm hoping we can discuss:
* **CloudWatch Metrics (`AWS/WAFV2`):** I rely heavily on `AllowedRequests`, `BlockedRequests`, and `CountedRequests`. These are essential, but they measure volume, not the processing time or CPU impact each rule group adds to the request evaluation.
* **Sampled Requests Logs:** Enabling these has been the most helpful step. By analyzing the `terminatingRuleId` and `nonTerminatingMatchingRules`, I can see which rules are firing. However, this still doesn't tell me if a particular rule group with many non-terminating rules is adding 10ms or 100ms of evaluation time. The logs lack performance timing per rule.
* **Experimentation:** The only true method I've had is to enable/disable rule groups one at a time in staging and measure the delta in overall request latency. This is time-consuming and not feasible for ongoing monitoring.
What I'm really looking for is a way to build a dashboard that could show something like:
* **Rule Group Performance Impact (Estimated):** A ranked list showing which groups are the most "expensive" in terms of evaluation time.
* **Request Flow Analysis:** For a slow request, being able to trace how much time was spent in WAF evaluation vs. application logic.
**My questions for the community:**
1. **Monitoring Tools:** Are you using any third-party tools or custom-built solutions (perhaps using Lambda to analyze logs) that help attribute latency to specific WAF rules?
2. **Best Practices:** Have you established internal benchmarks for acceptable performance impact from certain types of rules (e.g., SQLi detection vs. IP reputation)?
3. **AWS Services:** Has anyone found a way to leverage AWS X-Ray or another service in conjunction with WAF to get this telemetry? The integration doesn't seem straightforward.
I believe if we can better measure the cost of each security rule, we can make more informed decisions about our configurations and prioritize optimizations. I'd love to hear about your workflows and any data you've collected.
~jenny
Let the data speak.
Sampled logs are the only thing that gives you real breakdowns. But you're still inferring cost from latency samples, which isn't the same as a direct metric.
For a true performance cost, you need to isolate and measure. The brute force method is to create a dedicated WAF for a test endpoint, attach only one rule group, and benchmark it. Then cycle through them. It's a pain, but correlation graphs won't tell you which specific regex in a managed group is causing the drag.
Have you looked at the `PassedRequests` metric for rules with `Count` action? It can sometimes hint at evaluation load, but it's still fuzzy.
show me the bill
You're right that sampled logs only give you inference, not measurement. The isolation method you described is the gold standard, but the overhead of cycling through groups individually is often prohibitive in production.
One practical middle ground I've used is to enable a single new rule group during a known low-traffic period and compare latency distributions before and after, using a high-percentile metric like p99.9. This still doesn't isolate the cost within a group, but it can pinpoint which *entire group* is the problem. For managed groups, that's often as granular as you can get without AWS providing deeper instrumentation.
The `PassedRequests` for `Count` rules is indeed fuzzy, as it conflates evaluation cost with request volume. A more telling signal is a rise in latency variance, not just the mean, when a rule group is active. It suggests the engine is hitting complex regex evaluations on certain request patterns.
brianh
You've hit the nail on the head with the sampled logs being the most helpful tool available. That's been my experience too. The gap between volume metrics and true performance cost is the real headache.
A trick I've used is to create a custom dashboard from those sampled logs, specifically parsing the `terminatingRuleId` and `nonTerminatingMatchingRules` fields. If you chart the frequency of each rule appearing in those logs over time, especially during a latency spike, you can sometimes see which rules are evaluating most often. It's still inference, but it's a stronger signal than just block counts.
Have you tried applying any rate-based rules to your custom groups? I've found they can be a surprising source of latency in high-traffic scenarios, more so than some of the managed SQLi rules.
Test everything.
The p99.9 comparison during low-traffic windows is a decent approach, but it assumes your traffic mix is homogenous. In my experience, even in a 'quiet' period, a single complex request triggering a deep regex evaluation can skew that percentile, making you blame an entire group for what is one expensive rule.
You mentioned the rise in latency variance. That's the critical signal most dashboards bury. Plotting the standard deviation of WAF evaluation time alongside your p99 can show you whether a group is adding consistent overhead or creating unpredictable, spiky latency. The latter is far more damaging to tail latency and user experience than a flat, predictable millisecond.
P99 or bust.
The sampled logs aren't a solution. They're a data source. The core problem is AWS's bundled metrics.
You're measuring volume and inferring cost. That's the vendor lock-in playbook. They sell you the problem (managed rules) and a separate, incomplete toolset (CloudWatch) to diagnose the side effects.
The correlation you see is real, but isolating it requires custom workarounds others mentioned because AWS won't expose per-group evaluation time. They'd have to admit some managed groups are performance pigs. They won't.
—Skeptic