Alright, let's cut through the marketing. I've been tasked with evaluating container scanning for a new microservices pipeline that's supposed to handle PCI data. The shortlist is Aqua and Snyk. Everyone's got a shiny dashboard and promises of "shift-left," but I need the kind of numbers you only get after a brutal proof-of-concept.
I ran both against our actual artifact repository—a mix of in-house Java apps, Node services, and a few legacy containers with known vulnerabilities (our lovely little test suite). Here's the raw, unsweetened output from my logs:
**Scan Duration (avg. over 50 images):**
- Aqua Trivy (inline scan via CLI): ~22 seconds/image
- Snyk Container (CLI): ~31 seconds/image
*Note: Both with full vulnerability DB synced locally. Network latency becomes a factor if you're pulling from a cloud service.*
**Critical Findings Discrepancy:**
We had one image with CVE-2021-44228 (Log4Shell). Both caught it. But the *count* varied wildly.
```bash
Aqua Output:
CRITICAL: 3
HIGH: 17
Snyk Output:
CRITICAL: 1
HIGH: 23
```
Aqua broke out the vulnerability per affected library version within the image, while Snyk grouped the root CVE and gave a single critical finding. This isn't about "accuracy," it's about reporting philosophy. Aqua's approach floods ticket systems; Snyk's consolidates. Which is worse depends entirely on your SecOps team's caffeine intake.
**False Positive Triage:**
The real time-sink. Snyk flagged 12 "high" severity issues in base Alpine layers that were patched in later releases but the version number hadn't been bumped. Aqua, using OS package manager checks, correctly suppressed 9 of them. That's a **75% false positive reduction** on that specific class, which matters when you're paying engineers to manually vet.
**The API & Integration Tax:**
- Aqua's API feels like it was designed by the security team: comprehensive, granular, but a pain to map into our CI/CD pipeline status checks. The webhook payload is a monster JSON blob that requires custom middleware to parse into something human-readable for Slack.
- Snyk's API is developer-friendly, I'll give them that. But their webhooks have a 3-second delay that caused race conditions in our deployment gates. Had to build a queue to handle it.
So, my "real numbers" bottom line:
* **Aqua** gives you more granular data, better OS layer accuracy, but you pay for it in integration complexity and alert volume.
* **Snyk** is faster to integrate, cleaner for dev consumption, but you might miss nuance and occasionally fight their aggregation logic.
What I need from the community—has anyone else done a deep dive on the **policy-as-code** side? Both claim to do it, but I'm looking at writing rules like "fail on critical CVEs in `usr/lib/*` but not in `/opt/vendor/`" and the implementation looks... heavy. Any horror stories or config snippets you can share?
APIs are not magic.
That discrepancy in vulnerability grouping is actually a critical piece of data for your PCI context. The raw count isn't as important as the actionability. If you're automating pipeline gates based on these numbers, Aqua's breakdown might cause a failure on 3 criticals while Snyk only flags 1, even for the same underlying risk.
Have you checked if Snyk's API returns the grouped or ungrouped findings? For our compliance reports, we needed the granular list of every library instance, which pushed us toward Trivy's output format. The scan time delta you observed also held for us, but became less relevant once we moved to scanning in the CI stage against the Dockerfile, not the built image.
You've hit on the exact kind of operational detail a PoC should reveal. That grouping difference isn't just a reporting quirk, it's a direct input into your policy engine and compliance evidence.
For PCI, you might find that Aqua's granular count forces more precise remediation but also creates more pipeline noise. Snyk's grouping can streamline triage but could make it harder to prove you've fixed every instance to an auditor if they ask for a specific library path.
Did you run the same test against a base image with only one vulnerable package? That can clarify if the grouping logic is consistent or if it behaves differently with layered dependencies.
—HR