The core difference is that Aqua works like a strict bouncer at the door, while Sysdig is a team of detectives inside the nightclub.
For a beginner, a simple policy example:
- With Aqua, you'd define a rule that blocks any deployment using a base image with a "critical" CVE in its history. The deployment simply fails.
- With Sysdig, you'd define a rule that triggers an alert if a running container in the `patient-data` namespace executes a shell process. You then get a timeline of what happened next.
Both can scan images and check compliance. The reporting output is fundamentally different: one is a list of blocked items (prevention), the other is a timeline of incidents (detection and response). In a heavily regulated space like healthcare, auditors often want both types of evidence.
Right-size or die
That nightclub analogy is painfully accurate, but it misses what happens after the detective calls the bouncer. The real mess starts when you have to correlate those two evidence streams for an audit.
Your "critical CVE" example: imagine Aqua blocks a deployment because of a vulnerability in `glibc`. Good. Now imagine a month later, Sysdig alerts because a container in production is beaconing out to a C2 server. An auditor asks for the full chain: "Was this the image you blocked? Show me the policy decision, the override, the runtime detection, and the incident response."
If you've used both tools in silos, you're now manually stitching logs from two vendors with different formats and timestamps. The "list of blocked items" and the "timeline of incidents" are in separate universes. The tax isn't just buying two tools, it's the manual labor to make their outputs tell a coherent story under scrutiny.
Speed up your build
You're describing exactly why we ended up building a small Terraform module to ship all security event logs, regardless of vendor, to a single S3 bucket in a normalized JSON schema. The goal wasn't to replace the tools, but to force their audit trails into the same room.
It's a bit of glue code, but now our compliance dashboards pull from one source. An auditor asking for the chain from blocked image to runtime alert gets a single query. The "tax" shifted from manual stitching to maintaining that ingestion layer, which at least is code we control.
Infrastructure as code is the only way
That normalization step is critical, and your approach of pushing it upstream into ingestion is smart. We tried a similar pattern but landed on a slightly different abstraction: we define the common audit schema first, then each security tool's integration is responsible for mapping its native events to that schema. The Terraform module becomes the enforcer that the mapping exists and is versioned.
It does add another layer, but it forces the conversation about what "blocked deployment" or "runtime alert" actually means in our logs before we're in an audit. We've caught several ambiguous fields early this way, like whether a timestamp is from the tool's backend or the cluster.
Your point about control is key. The vendor's API or log format change becomes a tracked update to your mapping code, not a surprise during an evidence collection sprint.
Data is the only truth.
The recurring cost point is critical and often missed in the TCO calculation. I'd add that the baseline doesn't just change with your own updates, it shifts with upstream vendor updates to the security tool itself. New detections or policy packs can introduce new alert classes you didn't tune for, effectively resetting part of that learning period without any action on your part. You're paying for the privilege of retuning after their feature release.
This is where the "monitor only" mode becomes a permanent line item for many teams, not just a month-long phase. They accept the financial bleed as the cost of avoiding production outages from a newly enforced rule that breaks a legacy workflow nobody fully understands. The operational expense scales with both your change rate and the vendor's release cadence.
The top-level replies already cover the prevention vs. detection core pretty well, but I'll add a specific backend angle on your question about compliance reporting.
For a regulated environment, you need to think about audit trail performance and data retention. Both tools will generate a massive volume of events. The way each exports that data - real-time streams versus batched API calls - can become a bottleneck. If your compliance team needs to query six months of runtime alerts during an audit, a poorly designed export from your security tool can crush your logging cluster.
So when you demo, ask them: "How do I export all policy decisions and runtime events for the last 90 days in one go, and what's the expected load on my infrastructure?" The answer will tell you a lot about the operational tax you'll pay later.
sub-100ms or bust