Hey everyone! 👋 I'm new here and just starting to evaluate SCA tools for our team. We're heavily invested in AWS Lambda and a serverless architecture.
I've seen a lot of vendor demos, but I really want to hear from teams actually using these tools in production. For those of you managing serverless apps, what's working well? I'm especially curious about how tools handle Lambda layers, dependencies packaged in deployment artifacts, and keeping up with the rapid updates in that space.
We're currently juggling resource planning and need something that fits into our CI/CD pipeline without slowing us down. Any honest reviews or comparisons with Black Duck would be super helpful!
I manage a mid-sized SaaS team that runs a 100+ Lambda function setup on AWS, with our backend entirely serverless. We went through this evaluation last year.
**Lambda layer scanning**: Most tools just scan the packaged artifact, but Snyk's plugin actually unpacks and inspects Lambda layers separately. It added about 90 seconds to our build time. Black Duck at the time just gave us a generic warning about the layer archive itself.
**Pricing and scaling**: For our volume, Snyk came in around $45k annually for our engineering team size, which felt high. We later found Mend (formerly WhiteSource) offered a consumption-based model tied to scans, which for our bursty CI/CD pipeline was closer to $28k. The quotes for Black Duck were enterprise-only and required a sales call.
**CI/CD integration**: Snyk's CLI worked smoothly in our CodePipeline setup. The biggest time sink was tuning the policy rules to avoid build breaks for low-severity issues in dev. With Mend, we had to write a small script to handle the output format for our internal dashboards, maybe half a day's work.
**False positives / noise**: This was our main issue. We had a lot of nested dependencies in our Python Lambdas. Snyk was accurate about 85% of the time. Black Duck flagged significantly more "potential" issues that turned out to be false positives in our serverless context, which created overhead for the team.
I'd recommend Snyk if your main priority is accurate, actionable findings directly in developer workflows, even at a higher cost. Go with Mend if you need to control costs on a per-scan basis and have the bandwidth for some lightweight output customization. To decide, tell us your team's tolerance for build-time increase and your internal process for triaging vulnerability tickets.
Welcome! That's a great, specific starting point for your evaluation.
You're right to focus on Lambda layers and packaged artifacts. Many tools treat them as a black box, which defeats the purpose. I'd add that you should test how each candidate handles *transitive dependencies* pulled in from your layers at runtime, not just the direct ones. Some scanners miss that layer entirely (pun intended).
On the CI/CD slowdown concern, definitely ask vendors for their average scan time for a deployment package similar to your size. The difference between a 2-minute scan and a 45-second scan adds up fast across hundreds of functions.
Keep it real
You're absolutely right about asking for average scan times, but that vendor-provided number is often a best-case scenario on a clean, isolated sample. The real latency penalty comes from state: caching behavior and delta analysis. A tool that does a full recursive scan every time might quote 45 seconds, but if it's a true incremental scan recognizing unchanged layers from a previous build, your pipeline might see sub-10-second checks most of the time. The opposite is also true - a 'fast' scanner that invalidates its cache on every minor dependency version bump will murder your aggregate build time.
The key question for the original poster isn't just "how long does a scan take," but "how does the scan time scale with partial changes?" Ask for a graph of scan duration vs. percent of artifact changed. A good tool for a serverless CI/CD pipeline will have a near-flat line after the initial baseline scan.
Good call on focusing on vendor demos vs. real use. We use a custom script with Snyk's CLI and Grype in a two-stage scan for our serverless stack. For Lambda layers specifically, we run the scan against the unpacked `python` directory *before* zipping it into the layer, which sidesteps the archive scanning problem entirely.
One thing that tripped us up early was runtime dependencies pulled from S3 during initialization - some tools won't catch those unless you point them at the right `requirements.txt` used for that download step. Might be worth checking if your layers have any of that dynamic loading.
How big are your typical deployment packages? That can really swing the scan time you'll actually see.
Data is the new oil - but it's usually crude.
That pre-zip scanning is a genius workaround! We do something similar by scanning the `node_modules` before building the layer. Saves so much headache.
Good shout on the S3 dependencies too, that's a sneaky one. We had a layer that pulled a config file from S3 which referenced a library version, and our scanner completely missed it until we added that config to the scan path.
Our packages are usually under 50MB, but we have a few monsters with ML models that hit 250MB. The scan time difference is massive, like going from 30 seconds to over 3 minutes. Makes pre-zip scanning even more critical for those.
Automate all the things.