When does building SAST make sense?

Running Semgrep or CodeQL with custom rules makes sense for teams with security engineering depth who want detection tuned to their codebase's specific patterns and architecture, where generic scanners miss real issues.

When does buying SAST make sense?

Buying makes sense when compliance reporting, audit-ready dashboards, and pre-built rule sets mapped to CWE and PCI/SOC 2 frameworks matter — and when AI triage tools built into commercial platforms can cut the false-positive handling burden that dominates SAST program costs.

What are the main SAST vendors?

Representative vendors include Checkmarx, SonarQube (SonarSource), Snyk, Semgrep. B4 Pro scores the full set.

Is SAST still relevant now that AI coding assistants scan code during development?

Yes, but the role is shifting. AI coding assistants catch vulnerability patterns at write time, before commit, which changes the post-commit gate from a primary detection tool to a compliance checkbox. Both layers serve different purposes for different requirements.

Dev & Engineering · Engineering, IT & AI

Should you build or buy SAST?

SAST (Static Application Security Testing) software scans source code, bytecode, or binaries for security vulnerabilities without executing the program — catching issues like SQL injection, insecure deserialization, and hardcoded credentials before code reaches production.

The build-vs-buy decision for SAST turns on how much compliance reporting and rule breadth matters relative to what self-hosted OSS engines with custom rules can provide; the trajectory is shifting as AI triage tools reduce the dominant cost driver — false-positive handling — but commercial compliance packaging still carries weight.

Domain: Dev & Engineering
Function: Engineering, IT & AI
Industries: Cross-industry

Last assessed June 2026 · re-scored quarterly via The Continuum.

Build it, buy it, or bridge?

	Build it	Buy it	Bridge (buy, then extend)
Cost shape	Semgrep OSS or SonarQube Community at near-zero license; staff time dominates	Checkmarx and Veracode start at $40K+; no meaningful free tier at enterprise scale	SonarQube Community + AI triage tool reduces per-finding labor without full rebuild
Time to value	Days to integrate Semgrep into CI; weeks to tune custom rule sets	Pre-built compliance rule sets active within days; developer portal immediate	Start with community edition; add AI triage to cut false-positive backlog
Differentiation captured	Custom rules tuned to your codebase patterns catch real issues generic scanners miss	Generic vulnerability classes don't vary by company; standard rules cover most cases	Vendor rule sets as baseline; custom Semgrep rules for architecture-specific checks
AI feasibility today	CodeQL and Semgrep used as production SAST engines with custom rules; mainstream pattern	AI triage built into commercial tools reduces false-positive noise significantly	Layer AI triage onto proven OSS scanning rather than replace either
Who it fits	Teams with security engineering depth who want architecture-aware detection	Teams needing audit-ready reporting, compliance rule sets, and vendor accountability	Engineering teams reducing false-positive burden without replacing compliance tooling

The B4 call

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When building SAST makes sense

Building a SAST program on top of OSS engines — running Semgrep or CodeQL with custom rules — is a documented and deployed pattern at security engineering teams. CodeQL is open source and the engine behind GitHub Advanced Security. Semgrep provides custom YAML-based rules that teams write to detect patterns specific to their codebase's architecture and conventions. The case for this approach is that custom rules catch real issues that generic scanners miss: SQL injection in your ORM conventions, unsafe deserialization patterns unique to your data models, or infrastructure misconfigurations specific to your cloud setup. Static analysis is also moving into AI coding assistants — GitHub Copilot and Cursor surface vulnerability patterns during development, before commit — which changes what post-commit SAST needs to do. Where the build case is strongest: teams with security engineering depth, an appetite to maintain custom rule sets, and workflows where the CI gate is a compliance requirement rather than the primary detection mechanism. The failure mode is writing rules once and letting them drift; custom SAST requires ongoing investment to stay relevant.

When buying SAST makes sense

Buying SAST from Checkmarx, SonarQube, or Snyk earns its keep when compliance reporting and audit-readiness matter as much as finding vulnerabilities. Commercial SAST tools provide pre-built rule sets mapped to CWEs and compliance frameworks (PCI, SOC 2, OWASP Top 10), developer portals with workflow tooling, and a vendor name you can point to in a security questionnaire. For teams without dedicated security engineers, the breadth of coverage matters: commercial tools have accumulated decades of rule refinement across CVE databases and real-world breach patterns that would take years to replicate. AI triage built into commercial tools also addresses the dominant labor cost in SAST programs — false-positive handling. One documented AI triage deployment reduced triage time by 91%, which is the real leverage point. The buy case is weakest for teams who find they're paying enterprise prices for a handful of rule sets they could replicate in a week with Semgrep, and strongest for organizations where audit evidence and vendor accountability are requirements.

Static analysis is moving from a standalone tool category toward a layer inside AI coding assistants. GitHub Copilot, Cursor, and similar tools surface vulnerability patterns during development, before code is ever committed, which makes the traditional post-commit SAST gate feel like a compliance checkbox rather than a genuine security control. For SOC 2 or PCI compliance purposes, that checkbox still matters, but the question of what tool fills it is more open than it was.

Semgrep and SonarQube Community Edition both run as self-hosted engines with custom rule sets, and teams with security engineering depth use them in production. Checkmarx and Veracode carry pricing that reflects decades of enterprise positioning, and utilization of their advanced features tends to trail what the license covers. Buying earns its keep when you need audit-ready reporting, pre-built compliance rule sets, and a vendor you can point to in a security questionnaire. The build case gets interesting when your team has the appetite to write custom rules tuned to your codebase patterns, which tends to catch real issues that generic scanners miss.

Representative vendors

SnykSonarQube (SonarSource) and 3 more, scored in B4 Pro

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Frequently asked

What is SAST?: SAST software scans source code, bytecode, or binaries for security vulnerabilities without executing the program — catching issues like SQL injection, insecure deserialization, and hardcoded credentials before code reaches production.
When does building SAST make sense?: Running Semgrep or CodeQL with custom rules makes sense for teams with security engineering depth who want detection tuned to their codebase's specific patterns and architecture, where generic scanners miss real issues.
When does buying SAST make sense?: Buying makes sense when compliance reporting, audit-ready dashboards, and pre-built rule sets mapped to CWE and PCI/SOC 2 frameworks matter — and when AI triage tools built into commercial platforms can cut the false-positive handling burden that dominates SAST program costs.
What are the main SAST vendors?: Representative vendors include Checkmarx, SonarQube (SonarSource), Snyk, Semgrep. B4 Pro scores the full set.
Is SAST still relevant now that AI coding assistants scan code during development?: Yes, but the role is shifting. AI coding assistants catch vulnerability patterns at write time, before commit, which changes the post-commit gate from a primary detection tool to a compliance checkbox. Both layers serve different purposes for different requirements.

The B4 Index scores every software category on two axes, strategic differentiation and AI feasibility, to classify it Build, Buy, Bridge, or Beware. See the full methodology.

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