Looking at runtime security for Lambda and Cloud Functions. Need to block attacks without adding latency or breaking deployments.
Key factors for "less intrusive":
* **Cold start impact:** How much does the security layer add to init time?
* **Deployment friction:** Does it require repackaging functions, modifying IAM roles extensively, or adding sidecars?
* **Observability overhead:** Does the security tool's logging interfere with function logs?
From my tests:
**Aqua** (with its runtime sensor) often requires wrapping the function. Adds a layer. Can see 100-500ms cold start hit. You deploy it as part of the image/build. Example packaging snippet for a Node.js Lambda:
```yaml
# In your build stage - Aqua often adds a step like this
RUN curl -sSfL https://download.aquasec.com/... | sh -
```
**Prisma Cloud** (Compute) typically uses a more agentless approach, leveraging cloud provider APIs and sidecar containers for containerized functions. Less direct code modification, but can be noisy in the function's console logs.
Quick comparison:
* **Code/Deployment Change:** Aqua usually needs more direct integration in the build. Prisma often relies on post-deploy scanning and runtime hooks.
* **Performance Hit:** Both add latency. Aqua's is more predictable (packaged). Prisma's can be variable (network calls to backend).
* **Blocking Capability:** Both can do real-time blocking. Aqua's tends to be more immediate at the function level. Prisma might involve a webhook delay.
Which one actually caused fewer deployment failures and rollbacks in your experience? Leaning towards the one with the simpler rollback process when it misbehaves.
Senior SRE at a fintech. We run 300+ Lambda functions across three AWS accounts, about half Node, half Python. I deployed Aqua for a six-month POC last year before we switched to Prisma Cloud.
**Cold start hit**: Aqua added 200-400ms consistently in our staging tests. Prisma's API-scanning model added zero, but its sidecar for containers added ~150ms. For pure Lambda, Prisma wins.
**Deployment friction**: Aqua required modifying every serverless framework config to wrap the handler. That was a non-starter for our devs. Prisma attaches via tags and IAM role scanning; we deployed it via Terraform in a week.
**Observability noise**: Aqua's sensor logs go to CloudWatch, but you can filter them. Prisma's CSPM alerts can flood your console if you don't tune the policy severity out of the box.
**Real cost**: Aqua quoted us ~$25k annually for our functions. Prisma was bundled in our existing Palo Alto deal, but list price is opaque; expect $4-6 per function per month at our scale.
My pick is Prisma Cloud for Lambda, purely because it doesn't force you to repackage. If you're heavy on containerized functions (ECS Fargate), the calculus changes, tell us your mix.
Keep it simple
Thanks for sharing these numbers from actual testing, that's really helpful. I was leaning towards Aqua based on their docs, but that cold start impact you measured is pretty significant for our latency-sensitive workloads. The deployment friction is also a big concern - we use serverless framework heavily, and getting our dev teams to update every handler wrapper sounds like a tough sell.
Do you know if Prisma's tag-based approach works smoothly with functions deployed through CI/CD pipelines, like from GitHub Actions? I'm curious if there are any race conditions where a function might run before the tags are fully processed by their system. And have you noticed any gaps in the API-scanning model, maybe with permissions that are granted dynamically at runtime?
We've been on Prisma Cloud for about 18 months, deploying exclusively via Terraform and GitHub Actions. On the race condition question - yes, we saw it early on. A function could be invoked before Prisma's inventory refreshed, leading to a few minutes of "unprotected" state. The fix is to add a small delay or a readiness check in your pipeline after the `terraform apply` or `aws deploy`. Not perfect, but it works.
For your question on dynamic runtime permissions, that's the main gap. Prisma's API-scanning model is looking at the static IAM role attached to the function. If your function code does something like assuming another role using STS, or generates pre-signed URLs with elevated actions, Prisma won't see that. You need to complement it with runtime code audit and maybe CW Logs analysis for those specific cases. For most teams that don't do crazy runtime permission juggling, it's fine.
The tag-based approach is solid with CI/CD, just treat the tags as immutable infrastructure.