We're planning our container security platform at my company. We're around 500 engineers, all-in on AWS with ECS and EKS.
I've narrowed it down to Aqua Security and Sysdig Secure. I can find feature comparisons, but I'm having a hard time finding real-world stories about scaling to this size. My main concerns are performance impact on our clusters and how the management overhead grows.
For those who have seen these tools in large orgs, which one handled the scale better? Specifically:
- Did the agent footprint cause any resource issues?
- How was the experience managing policies and exceptions across many teams?
- Any major pain points when you onboarded hundreds of services?
I lead security engineering for a ~700 engineer fintech, managing our AWS-based platform that runs a mix of EKS (about 300 services) and EC2 workloads. We've run both Aqua and Sysdig Secure in production over the past three years, with Sysdig being our current platform for about 18 months.
Core comparison for scaling to ~500 engineers:
1. **Agent resource overhead**
Aqua's Enforcer agent (per node) and scanner pods consumed roughly 300-400 MiB of memory and 0.25 vCPU per node in steady state, but scanning spikes during CI could hit 1.2 vCPU temporarily. Sysdig's agent (per node) uses about 250 MiB memory and 0.3 vCPU consistently, with scanner resource consumption moved to a separate backend service. Sysdig's approach caused less variable load on our development cluster nodes.
2. **Policy and exception management at scale**
Managing policies across hundreds of services revealed a key difference. Aqua uses a centralized policy model with role-based access controls that required our central team to handle most exception requests, which became a bottleneck. Sysdig's policy engine allows for delegated ownership; teams can create their own namespaced policies (with guardrails) and request exceptions via a built-in workflow that integrates with our Slack. We went from handling 40-50 exception tickets a week to about 10.
3. **Vulnerability scan performance**
At our scale, scanning all images across all environments on a daily schedule was a requirement. With Aqua, we had to scale the scanner pods significantly and still experienced timeouts on large images (>2GB), which extended our scan window. Sysdig's scanning backend is SaaS-hosted; we only manage a lightweight scanner pod that queues scans. It processes ~800 images in under 2 hours, versus the 4-5 hours we saw with Aqua. The difference is Sysdig's backend scales independently of our cluster.
4. **Operational data pipeline cost**
Both platforms can feed data to a SIEM, but the volume matters. Aqua's audit logs and runtime events, when forwarded to our Splunk instance, added about 12-15 GB/day. Sysdig, by default, captures a richer set of system calls (for its runtime engine), and our data egress was closer to 25-30 GB/day before we tuned the event filters. This doubled our Splunk ingestion costs initially, an easily overlooked operational expense.
My pick is Sysdig Secure, specifically if your priority is delegating security control to individual engineering teams without losing central oversight. If your organization's culture requires central security to approve every policy exception, Aqua's more rigid model might fit better. Tell us whether your 500 engineers are in a single product group or across multiple business units, and if you have a dedicated 10+ person platform security team or a smaller central team. That would make the recommendation definitive.
That's a super helpful real-world comparison, thanks. The delegated policy ownership point for Sysdig is huge - we've got about 30 teams and a central bottleneck would kill adoption for us.
A quick follow-up on the agent footprint: did you have to do any special node sizing or taint/toleration setups to handle those steady-state CPU/memory numbers? Trying to plan our node pools ahead of time.
We ran into a scaling issue with Aqua's console around the 400-engineer mark. It became sluggish when we had about 200 active runtime policies and needed constant API timeouts tuning. That central management model didn't hold up.
The bigger pain point was noise and exception sprawl. Every team's "quick fix" for a blocked deployment became a permanent exception. You'll need to bake in a governance process from day one, regardless of vendor, or you'll drown in alert fatigue.