Looking for SAST that doesn't suck and won't bankrupt a tiny team. Need it for a mix of Python/JS/Go microservices. Cloud-native, deployed via Terraform/K8s.
Primary requirements:
* Under $200/month total
* CLI-first for CI integration
* Low false positives
* Decent Kubernetes/container scanning
Tried Semgrep OSS. It's good for basic patterns but we need more. Their paid tiers seem to jump fast.
Considering:
* Semgrep Pro Engine (if pricing is flat)
* Checkov for IaC + custom Semgrep rules
* SonarQube Cloud (might blow budget)
Current setup:
```yaml
# .semgrep.yml
rules:
- id: flask-debug-true
patterns:
- pattern: app.run(..., debug=True, ...)
message: Debug mode enabled in production
severity: ERROR
```
What are you all using? Need concrete cost numbers and integration pain points. Prefer something we can run in a GitHub Action without a SaaS portal tax.
—cp
—cp
I'm a lead DevOps engineer at a 60-person fintech. We run 30+ microservices across Go and Python on EKS, and I've had SAST in CI/CD pipelines for the last three years.
* **Pricing and Budget:** Checkmarx and Snyk Code were instantly out. Semgrep's "Team" tier runs ~$50/seat/month. For a 5-engineer team, you're hitting your $200/month ceiling before any infra scanning. Checkov is 100% free, but its SAST depth is shallow.
* **CI/CD Integration Pain:** Semgrep's CLI is excellent. One-liner install and `semgrep ci` in the action works. SonarQube Cloud required a persistent token daemon that added 20-30 seconds to pipeline spin-up. Checkov integrates cleanly if you're already scanning Terraform.
* **False Positive Rate:** In our Go codebase, Semgrep Pro Engine's taint analysis cut noise by ~60% vs their OSS rules. SonarQube's default JS rules flooded us with style complaints; tuning took a week. Checkov's container scanning is decent, but its SAST is basically pattern-matching, similar to your custom Semgrep rule example.
* **K8s/Container Context:** This is where the combo approach wins. Checkov is purpose-built for IaC (Terraform, K8s manifests, Helm) and container images. Using it alongside a code scanner is standard. Semgrep *can* scan Dockerfiles, but its IaC coverage is weak.
My pick is Semgrep OSS, supplemented with Checkov for IaC/containers. It's the only way to stay under budget. If you need deeper data flow analysis, ask if the $50/seat Pro Engine cost is firm and if they offer a flat rate for under 10 seats.
Integration is not a project, it's a lifestyle.
I've got hard data on that 60% false positive reduction claim. We ran a two-week benchmark comparing Semgrep OSS, Semgrep Pro Engine, and Snyk Code across our 50-repo Java/Go portfolio. The Pro Engine's taint analysis for Go reduced noise by 57.3% on average, but the variance was huge - some service repos saw 80% reduction, others only 35%. The key was the quality of dependency tracking in our go.mod files.
Your point about Checkov's shallow SAST is spot on. It's effective for manifest and Terraform scanning, but we found its code analysis lacks inter-procedural data flow. We use it strictly for IaC, then feed its output into our central findings database to correlate with runtime data from Falco. This layered approach gives us the context you're mentioning, but it does add pipeline complexity.
—chris
> Checkov is purpose-built for IaC (Terraform, K8s manifests, Helm) and container images.
This. The combo is pragmatic. We ran a similar setup and benchmarked pipeline times. Adding Checkov for IaC added ~45 seconds, but it caught a critical misconfigured network policy Semgrep would've missed. The cost for us was Semgrep Pro for 3 devs ($150) plus free Checkov, so under the $200 cap.
You can't beat free for the infra scan. The trade-off is maintaining two config files and merging findings, but it's worth the time saved on false positives in the code. That 60% noise reduction in Go is real, our Python benchmarks showed a 48% drop.
Semgrep Pro with Checkov for IaC is the sweet spot for your budget and stack. We ran that exact setup for 5 engineers and landed at $180/month. The CLI integration is painless in GitHub Actions.
One caveat: merging findings from two tools was a small headache until we piped everything into SARIF and used the GitHub Code Scanning API as a single pane. The Pro Engine's taint analysis was a game-changer for our Python services, cutting false positives almost in half compared to the OSS rules.
You mentioned avoiding a SaaS portal tax, and that's the real win here. Both tools give you everything via CLI and config files. Have you looked into using the Semgrep App for just centralized findings? It's free for small teams and might save you the scripting effort.
Keep automating!