Our experience after the first year: Costs doubled with traffic growth.

That's a scary thought, a vendor's financial incentive working against you. 😬

It reminds me of our old SaaS subscription where we paid per "active user." Their overly-broad definition of "active" kept our costs high. We had the same feeling of fighting their system instead of benefiting from it.

So for WAFs, is the only real fix to move away from "mitigated traffic" billing entirely? To a flat fee or something based on your own infrastructure?

It's interesting you mention pulling weekly reports for analysis. I'm curious about the granularity of data you were able to get from those API pulls.

Specifically, when you analyzed the legitimate bot traffic that was inflating your "mitigated" volume, were you able to break it down by bot user-agent or source IP at that weekly level? Or did you have to go digging in their main dashboard logs for that level of detail? I'm wondering if the API data is detailed enough to build an effective allowlist strategy from, or if you still need to manually cross-reference.

That's the key first step for cost control, tagging the rules. It gives you the 'why' behind the bill.

When we did it, we found a huge chunk came from just three overly-broad rules catching API noise. Trimming those back cut 30% off our 'mitigated' count the next month.

But as others said, it's a continuous job. That report pull becomes a weekly ritual, otherwise the creep starts again.

Great point about proactive rate limiting. We thought about that but haven't tried it yet.

>proactive rate limiting or challenges for verified good bots

Do you have a good way to verify "good" bots before they hit the WAF? I worry about accidentally blocking search engines or monitoring tools if we get the signals wrong.

You verify bots the same way you verify everything else. Define your own allowlist based on known-good IP ranges and proven user agents, then enforce it at the edge, before the WAF. If your search engine traffic disappears because you blocked Google's IPs, you'll notice. Their logs are public.

Then you're just paying for real traffic.

Ouch, that's rough. I had a similar surprise with a different SaaS tool last year, but not doubling like that!

You mentioned a weekly report pull script. Did you find the Imperva API easy to integrate with your monitoring stack? Or was it a fight to get the data formatted right? I'm looking at automating something similar for our setup.

The API itself was fine, typical REST with decent docs. The real fight was the sheer volume of data points and the lack of a proper time-series format. We had to write a lot of transformation glue to get it into our Prometheus stack.

If you're building something similar, cache the expensive endpoints. Their `usage` endpoint can be slow, and you don't want your script timing out.

What monitoring stack are you plugging into? That'll dictate how much wrangling you'll need.

Yeah, we hit the same data volume issue. Pulling raw logs to find the source of a cost spike was like drinking from a firehose. It forced us to pre-aggregate by rule and source IP before it even hit our data warehouse, otherwise the queries were unusable.

>What monitoring stack are you plugging into?

We ended up piping it to BigQuery. The nested JSON structure from the API actually maps okay to that, but we still do a nightly flatten-and-transform job. It's a bit heavy, but at least we can run complex joins against our own traffic logs now to find mismatches. I'd be curious if Prometheus was any easier for this specific use case.

Prometheus was a struggle for this, honestly. Its pull model and time-series focus meant we had to pre-compute the aggregates you mentioned before the scrape, or risk cardinality explosion. We ended up using a separate log processor to digest the firehose into a few key metrics *before* exposing them to Prometheus.

BigQuery's ability to handle that nested JSON and run those ad-hoc joins is the real win, even with the nightly ETL job. The trade-off is latency for flexibility. Have you looked at using their streaming insert for nearer-real-time analysis on cost spikes, or is the nightly batch sufficient for your review cycle?

You're right about the latency-flexibility trade-off. We use a similar nightly batch to Snowflake. For us, the 24-hour lag hasn't been an issue because the real cost driver is sustained traffic patterns, not single-hour spikes.

The bigger problem we found was ensuring the ETL logic kept up with changes in the vendor's API data structure. Had a month where a nested field changed and our aggregates undercounted a major bot source, which hurt.

Have you run into that with BigQuery? It's the hidden cost of building on someone else's data export.

Yes, the schema change risk is real and often overlooked. We mitigate it with a lightweight validation script that runs right after the nightly pull. It checks for new fields, changed data types, or unexpected null rates against a stored baseline and alerts us before the main ETL job starts. This catches most vendor-side changes.

Your point about sustained patterns being the cost driver is key. That's why our validation focuses on aggregates, not raw logs. A broken field that drops a major botnet from the count will skew totals immediately, which is the signal we need.

That's a smart approach! I've been bit by schema changes in other APIs before, and catching it before the ETL runs sounds much better than finding out days later. Your validation script - is it just checking for structural changes, or does it also run some basic sanity checks on the numbers themselves? Like, if bot traffic suddenly reads zero, that's an obvious flag even if the schema looks fine.

I'm curious, how do you define the baseline for "unexpected null rates"? Is it just a manual snapshot from a known good period, or something more dynamic?

Both, actually. The structural check is the first layer. It uses a simple JSON Schema diff against a version we commit when we update the pipeline. But you're right, a valid schema with nonsense data is worse. So we run a second layer of heuristic checks against the aggregates from the new data before ingestion.

For null rates, we calculate a rolling 7-day average for each critical field. The validation flags if today's null percentage deviates by more than two standard deviations from that mean. It's a simple statistical process control chart, basically. We also have absolute sanity thresholds, like you mentioned - total bot traffic under 5% would trigger a manual review immediately, schema change or not.

The rolling baseline is key because our own traffic patterns shift. A static snapshot from last month would constantly throw false positives during product launches or marketing campaigns.

That initial "sticker shock" moment is so relatable. We went through the same thing with a different vendor, and it's always the legitimate bot traffic that sneaks up on you.

Your point about reviewing rules in "monitor" mode is spot on. We found a similar cost-saving lever, but with a slight twist. We created a policy to only apply our most aggressive (and expensive) DDoS mitigation profiles to specific, high-value endpoints, like our login and checkout flows. Everything else gets a lighter touch. It cut a surprising chunk from our bill without adding meaningful risk.

Tagging the rules was a lifesaver for us too. It made those weekly reports from the API so much more actionable, because we could immediately see which specific rule was costing us the most per mitigated request. Did you find that your tag-based reports helped you prioritize which rules to tune first?

Yeah, the tag-based reports were crucial for us too. It immediately showed that a single rule blocking a common scraping pattern was costing almost half our monthly bill.

That made it an easy decision to dial that one rule back to monitor mode first. But it raised a question for me. How granular do you get with the tagging? We started by just tagging the security team's rules vs. the app team's rules, but I wonder if we should tag by endpoint or threat type instead for even better cost attribution.