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Educational, Technical

11 best SAST tools for 2025: how to choose the right SAST solution

Timothy Jung

Marketing

Published June 20 2025 · 9 min. read

Static application security testing (SAST) has matured into a cornerstone of modern application security. By scanning source code, bytecode, or binaries before an application runs, these tools help developers detect and remediate issues while they’re still inexpensive to fix.

In 2025, SAST has matured into a core element of DevSecOps pipelines and developer workflows across industries.

That’s because the pressure on development teams has never been higher. AI coding assistants now contribute a significant portion of enterprise code, and while this accelerates delivery, it also introduces more risks per unit of time.

Organizations need static source code analysis tools that not only identify vulnerabilities, but also integrate seamlessly into high-velocity workflows and provide actionable, low-noise results.

And yet, the challenge is choosing the best solution for the job. Each SAST tool takes a different approach, with some prioritizing depth and compliance reporting, while others focus on speed, automation, or seamless integration into developer workflows.

Understanding these trade-offs is key to finding the tool that fits your organization’s environment.

Key takeaways

The best SAST tools combine accuracy, speed, and context so developers can fix issues without breaking flow.
Look for static application security testing tools that integrate into CI/CD and IDEs, with clear remediation guidance or autofix.
Different tools serve different needs, from enterprise compliance to developer-first speed, and choosing the right fit depends on your environment.

Why SAST Still Matters in 2025?

Static application security testing tools remain essential because they address vulnerabilities at the earliest, most cost-effective point in the software development lifecycle. A flaw fixed in development costs a fraction of what it would in production, and SAST enforces this shift-left principle at scale.

Modern applications add another layer of urgency. AI-generated code is accelerating delivery, but it can also introduce subtle risks, from insecure defaults to logic flaws hidden in unfamiliar patterns. Without static analysis tools embedded in the pipeline, those vulnerabilities may slip past manual reviews or dynamic testing.

SAST also plays a critical role in compliance. Regulations and frameworks like PCI-DSS, HIPAA, and ISO 27001 require teams to demonstrate that security is integrated into development. Static application security testing tools provide the audit trails, dashboards, and reports that prove these practices are in place.

For all these reasons, SAST has evolved from a specialized tool into a foundation for enterprise security programs, ensuring that vulnerabilities are identified before they create costly downstream risks.

The Criteria to Evaluate SAST Tools: Accuracy, Speed, Context

Not all static application security testing tools perform equally well. The most effective solutions are those that improve security without disrupting development. When evaluating options, three criteria stand out.

Accuracy: Signal over noise

A SAST tool should consistently identify true vulnerabilities while minimizing false positives. Excessive noise erodes developer trust and slows remediation.

Look for static source code analysis tools that use techniques like data flow, control flow, and taint analysis to improve precision. High accuracy means your teams focus on issues that matter, rather than chasing irrelevant alerts.

Speed: Fit for modern pipelines

Security can’t become a bottleneck in fast-moving CI/CD pipelines. The best SAST tools provide incremental or differential scanning so that each commit is analyzed quickly without reprocessing the entire codebase.

Some even deliver instant feedback directly in the IDE. Evaluate how a tool’s scan performance impacts your build times and whether it can scale with the size of your projects.

Related Content: CI/CD pipeline security best practices

Context: Actionable remediation

A finding is only useful if developers can fix it quickly. Modern static application security testing tools provide detailed remediation guidance, often with code examples or AI-driven autofix suggestions. The most advanced go further, prioritizing vulnerabilities based on reachability and exploitability so teams fix what poses the greatest business risk.

Combined, these criteria form the baseline for choosing the right tool. A platform that delivers accuracy, speed, and context will strengthen security while keeping development velocity high.

11 Best SAST Tools

Static application security testing tools vary widely in strengths. Some are designed for large enterprises with complex codebases, while others focus on speed, automation, and developer adoption.

Below are 11 of the best SAST tools in 2025, organized by the type of environment where they excel.

Enterprise platforms: Depth, scale, and compliance

These vendors are the long-standing leaders, built for regulated industries and heterogeneous tech stacks.

Checkmarx

Checkmarx has been a mainstay in enterprise AppSec for more than a decade, especially in financial services, government, and large technology companies. Its customizability and breadth make it a strong choice for organizations with diverse, regulated codebases.

Ideal use case: Large enterprises that need scale, compliance reporting, and tailored vulnerability queries.
Key differentiator: Custom Query Editor for fine-tuned detection of organization-specific flaws.
Developer experience: Integrates into IDEs and CI/CD pipelines, though the UI can feel heavy compared to newer entrants.
Coverage positioning: Broad coverage across both modern and legacy enterprise languages.

Veracode

Veracode pioneered cloud-based SAST and remains widely adopted in highly regulated industries. Its binary analysis capability adds unique value when scanning compiled applications or third-party components.

Ideal use case: Enterprises that want SaaS delivery with low infrastructure overhead and strong compliance support.
Key differentiator: Ability to scan binaries as well as source code.
Developer experience: Integrates into popular pipelines with quick scan times, but binary scanning can add build complexity.
Coverage positioning: Expansive multi-language and multi-framework support, including mobile and cloud-native stacks.

Fortify (OpenText)

One of the oldest and most established static application security testing tools, Fortify remains a staple in government, defense, and large financial institutions.

Ideal use case: Organizations needing deep language support and flexible on-premises or hybrid deployments.
Key differentiator: Mature rule base with options for SaaS, on-prem, or hybrid models.
Developer experience: Comprehensive reporting and ML-assisted triage, though some teams find it resource-intensive to manage.
Coverage positioning: Broad coverage, including legacy enterprise languages and niche frameworks.

Synopsys Coverity

Coverity is Synopsys’s flagship SAST product, paired with Black Duck for open-source risk. Known for precision and depth, it excels at catching subtle issues in large, complex codebases.

Ideal use case: Enterprises that need highly configurable, policy-driven static source code analysis tools.
Key differentiator: Deep analysis capabilities with customizable checkers for fine-grained accuracy.
Developer experience: Integrates into CI/CD and issue trackers; more configuration-heavy than developer-first tools.
Coverage positioning: Comprehensive coverage across enterprise languages, with strong accuracy in complex applications.

Developer-first innovators: Speed and automation

These tools prioritize developer adoption, fast feedback, and AI-powered fixes.

Snyk Code

Snyk has become synonymous with developer-first security, and Snyk Code extends that focus into SAST. Backed by semantic AI, it delivers real-time feedback where developers work.

Ideal use case: Cloud-native and agile teams that want instant feedback without leaving the IDE.
Key differentiator: Semantic AI engine with autofix suggestions and tight integration with Snyk’s SCA.
Developer experience: Seamless IDE plugins and pipeline integrations make adoption straightforward.
Coverage positioning: Strong support for modern stacks like JavaScript, Python, Go, Java, and C#.

Semgrep

Semgrep is an open-source favorite known for its speed and flexibility. With customizable YAML rules and a strong community, it’s ideal for teams that want to enforce their own standards.

Ideal use case: Security and engineering teams that value customization and fast CI/CD feedback.
Key differentiator: Easily written custom rules to capture organization-specific coding patterns.
Developer experience: Lightweight CLI and pipeline integration; commercial edition adds AI-assisted triage.
Coverage positioning: Strong for modern programming languages with active community-driven rules.

Qwiet AI

Formerly ShiftLeft, Qwiet AI combines speed with accuracy using its Code Property Graph (CPG). It prioritizes findings based on exploitability, reducing developer noise.

Ideal use case: High-velocity DevSecOps teams that want fast scans and risk-prioritized results.
Key differentiator: Code Property Graph engine with reachability analysis and AI AutoFix.
Developer experience: Quick CI/CD integration, though advanced features sometimes require CLI use.
Coverage positioning: Optimized for modern languages and infrastructure-as-code, less focused on legacy coverage.

Platform-native solutions: Seamless integration

For teams already invested in GitHub or GitLab, these built-in SAST tools minimize friction.

GitHub Advanced Security (CodeQL)

Built directly into GitHub, GHAS brings CodeQL analysis into pull requests and workflows, giving developers immediate, in-context results.

Ideal use case: Organizations standardized on GitHub for SCM and CI/CD.
Key differentiator: CodeQL semantic analysis with customizable vulnerability queries.
Developer experience: Findings appear inline in pull requests, with Copilot Autofix offering AI-driven code suggestions.
Coverage positioning: Broad coverage of mainstream languages, tightly tied to the GitHub ecosystem.

GitLab SAST (Ultimate)

GitLab integrates SAST into its DevSecOps platform, with enhanced capabilities in its Ultimate tier.

Ideal use case: Teams already using GitLab who want an all-in-one CI/CD and security experience.
Key differentiator: Native merge request integration with advanced cross-file analysis in Ultimate.
Developer experience: Vulnerabilities surface directly in merge requests; simple to activate within GitLab pipelines.
Coverage positioning: Broad language support via open-source analyzers, with advanced accuracy in supported modern stacks.

Hybrids: Code quality and broader AppSec coverage

Some tools bridge SAST with other capabilities, providing additional value beyond static analysis.

SonarQube / SonarCloud

SonarQube started as a code quality platform and remains widely used for governance, now with expanded security coverage.

Ideal use case: Teams prioritizing both code quality and security in one dashboard.
Key differentiator: Quality Gates that enforce standards and block insecure or low-quality code from merging.
Developer experience: Highly visual dashboards and IDE plugins (SonarLint) for immediate feedback.
Coverage positioning: Dozens of mainstream languages, with security rules strongest in paid tiers.

Mend.io

Expanding from SCA into SAST, Mend.io positions itself as an AI-driven AppSec platform. Its unified view of proprietary and open-source risks appeals to large enterprises.

Ideal use case: Organizations wanting a single vendor for SCA and SAST with strong automated remediation.
Key differentiator: AI-powered remediation engine (“Mend Cure”) for faster fixes.
Developer experience: Solid CI/CD integrations, though some users note the UI feels dated.
Coverage positioning: Wide coverage across enterprise and modern languages, with particular strength in dependency management.

Making the right choice

Together, these tools represent the best static application security testing platforms available in 2025. Choosing the right one depends on whether your priority is compliance depth, developer velocity, or platform convenience.

Comparing SAST to SCA: Use Cases & Complementary Workflows

SAST and software composition analysis (SCA) are often mentioned in the same conversations, but they solve different problems. Treating them as interchangeable can leave major blind spots in an application’s security posture.

SAST: Focus on proprietary code

SAST tools analyze the code your developers write, the custom logic that ties everything together. They catch vulnerabilities such as SQL injection, cross-site scripting, and buffer overflows before the application ever runs. By scanning early, they enforce secure coding practices and reduce the cost of fixing flaws.

SCA: Focus on open-source dependencies

Modern applications are built on third-party libraries, frameworks, and packages. SCA tools analyze those components to identify known vulnerabilities (CVEs), license risks, and outdated dependencies. With open source making up the majority of today’s codebases, this visibility is essential.

Why both are needed

An application could pass SAST scans with clean proprietary code, but still rely on a vulnerable library. Conversely, it could use only secure open-source components but have insecure logic in the glue code. Running both static application security testing tools and SCA together provides the full picture: what’s in your software and how securely it’s written.

Making the case for unified workflows

The most effective programs integrate SAST and SCA scans directly into CI/CD pipelines and surface findings in the same feedback loops. This reduces context switching and ensures developers focus on the vulnerabilities with the greatest impact.

Consolidated dashboards and risk-based prioritization make it easier for AppSec teams to manage and report on application risk at scale.

Go from detection to remediation to evolve beyond SAST

SAST and SCA give teams visibility into both proprietary code and third-party components, forming the baseline of secure development by catching vulnerabilities early and supporting compliance.

As AI-generated code accelerates delivery and expands the attack surface, detection alone falls short. Many static application security testing tools now offer AI-driven fixes, but these often act in isolation, suggesting code changes without awareness of runtime behavior, business impact, or organizational policies. Fixes applied in a vacuum can introduce as many risks as they resolve.

The next step is remediation with context. Mapping the full software architecture, correlating findings with runtime exposure, and aligning fixes with security policies allows organizations to prioritize what matters most and automate resolution safely. Security becomes an enabler of velocity rather than a barrier.

Apiiro’s AutoFix Agent takes this approach. Instead of stopping at code suggestions, it evaluates whether a vulnerability is reachable, exploitable, and business-critical, and only then applies or recommends a fix. The result is fewer false positives, less wasted effort, and faster delivery of secure features.

See what this looks like for yourself. Schedule a demo to learn how AutoFix reduces noise, fixes risks with context, and helps your team ship secure code faster.