12 Best SAST Tools in 2026: Compare Code Security Tools

Finding security vulnerabilities in your code as early as possible is crucial, and Static Application Security Testing (SAST) is a key practice for that. Choosing between the many available options can be challenging, but understanding what makes the best SAST tools stand out will help you protect your applications efficiently. In this guide, the idea is to show what SAST is, why it matters, and how to choose the right solution for your team.

Best SAST tools by use case

Use case	Best option	Why it fits
AI-assisted PR security review	Kodus	Best fit when the team wants security and quality feedback inside pull requests, with custom rules, BYOK, repository context, and MCP plugins.
Code quality + security governance	SonarQube	Good for teams that want quality gates, code smells, vulnerabilities, coverage, and governance across repositories.
Developer-first AppSec	Snyk Code	Useful when the team wants SAST, SCA, containers, IaC, and fixes close to the developer workflow.
Enterprise AppSec platform	Checkmarx or Veracode	Better fit for larger organizations that need broad coverage, centralized policy, and mature AppSec operations.
GitHub-native semantic analysis	GitHub CodeQL	Strong for teams already using GitHub Advanced Security and needing deep semantic analysis with custom queries.
Custom static analysis rules	Semgrep	Good for teams that want fast scans, custom rules, and more control over what gets enforced.
All-in-one AppSec platform	Aikido	Useful when the team wants SAST, SCA, secrets, IaC, containers, and remediation in one platform.

How to choose a SAST tool

Before choosing a SAST tool, separate what your team actually needs: deep vulnerability detection, developer-friendly feedback, custom rules, compliance reporting, low false positives, or full AppSec platform coverage.

Developer workflow: Check whether findings appear in the IDE, pull request, CI pipeline, or only in a separate dashboard.

Signal-to-noise ratio: A SAST tool that creates too many low-confidence findings can slow the team down and reduce trust in the process.

Custom rules: If your team has internal security standards, architecture boundaries, or framework-specific risks, rule flexibility matters.

Coverage: Look at supported languages, frameworks, SAST depth, SCA, secrets, IaC, containers, and whether the tool covers your real stack.

Deployment and pricing: Compare cloud, self-hosted, enterprise, open-source, per-developer, per-LOC, and usage-based pricing models.

List of the Top SAST Tools in 2026

There are many tools on the market, each with its own strengths and weaknesses. Here is an overview of the best-known ones:

1. Kodus

Kodus is not a traditional SAST scanner in the classic sense. It fits this list because many teams now want security and quality feedback inside the pull request, where code is still easy to change. Instead of sending every issue to a separate security backlog, Kodus works as an AI code review layer that reviews PRs, applies team-specific rules, uses repository context, supports BYOK, and can bring external context into the review through MCP plugins.

This makes Kodus useful for teams that want to reduce risk earlier in the development flow. Kody, the code review agent, can comment directly on pull requests, highlight risky patterns, enforce internal standards, and help reviewers focus on issues that actually matter for the change being reviewed.

The important distinction is that Kodus is more review-driven than dashboard-driven. Classic SAST tools are usually centered on scans, findings, policies, and quality gates. Kodus is centered on the PR conversation: what changed, what rule applies, what context matters, and what the developer should fix before merge.

Key features

• Kody Rules: teams can create review rules at file level or pull-request level. Rules can use PR metadata, complete PR diffs, file references, repository references, and MCP functions to validate standards that depend on team conventions, architecture boundaries, security practices, or cross-file behavior.
• Security and quality checks in PRs: Kodus can help catch risky patterns during review, such as unsafe API usage, missing validation, poor error handling, exposed sensitive behavior, weak authorization assumptions, or changes that violate internal engineering standards.
• Repository context: Kody can use repository context and references when reviewing changes, which helps reduce generic feedback and makes suggestions more aligned with how the codebase actually works.
• BYOK and model choice: Kodus supports Bring Your Own Key, so teams can connect their own model provider keys and control model choice, privacy, performance, and cost. It supports providers such as OpenAI, Anthropic, Google Gemini, and OpenAI-compatible providers, with no markup on token usage.
• MCP plugins: Kodus can bring external context into review through MCP plugins. That context can come from tools like Jira, Linear, Notion, Slack, Google Docs, or custom MCP servers, helping Kody validate a PR against linked requirements, tickets, acceptance criteria, business rules, or internal documentation.
• Business logic validation: teams can define rules that compare the PR against product requirements or domain-specific behavior, which is useful for issues that a traditional scanner would not understand from static patterns alone.
• Learning from team feedback: Kody can learn from accepted, rejected, and implemented suggestions. Over time, this helps reduce feedback that looks technically correct but does not match the team’s standards.
• Kody Issues: suggestions that are not implemented can become follow-up issues, so important review feedback does not disappear after a PR is merged.

Pros

The main advantage of Kodus is that it brings security and quality feedback closer to the developer workflow. Instead of waiting for a separate scan result or a dashboard review, the team gets contextual feedback directly where the code is being discussed.

It is also more flexible than many rule-only workflows. Kody Rules let teams turn internal standards into review criteria, including authentication expectations, input validation, error handling, dependency boundaries, naming conventions, public API contracts, or product-specific behavior.

The BYOK model also matters for teams that care about cost control and vendor flexibility. Since Kodus does not add markup on token usage, teams can choose the model provider that fits their needs and manage LLM spend directly.

Another important point is extensibility. MCP plugins make it possible to review code with context that usually lives outside the repository, such as tickets, specs, documentation, internal workflows, or business rules.

Cons

Kodus is not the best fit if the company wants only a traditional SAST scanner with broad AppSec coverage, compliance dashboards, and centralized security policy management in a single enterprise platform.

It also works best when the team is willing to define rules, connect context, and tune the review process. If the goal is only to turn on a generic scanner and handle alerts outside the PR workflow, tools like SonarQube, Snyk Code, Semgrep, Checkmarx, or Veracode may fit that expectation better.

Pricing

The Community plan is free. The Teams plan costs US$10 per developer per month with BYOK. Enterprise is available with custom pricing.

2. SonarQube

SonarQube Cloud and SonarQube Community Build keep showing up in almost every conversation about code quality and security. This happens because Sonar has been part of many teams’ day-to-day work for a long time and solves a very concrete problem: giving continuous feedback on quality, bugs, vulnerabilities, and coverage without requiring a separate AppSec process.

It is not the most specialized SAST on the list, and it does not try to be a platform focused only on application security. Its value is more in the combination of code analysis, quality gates, coverage, merge rules, and integration with the development flow.

For companies that want to combine quality and security in the same governance layer, Sonar still makes a lot of sense. It helps create minimum criteria for code to enter the codebase, especially in teams that need to standardize practices across several repositories.

In recent cycles, the platform has also started talking more to the AI context, with features like AI Code Assurance and AI CodeFix. This puts Sonar in a slightly more current position, especially for teams concerned about the quality of code generated or changed with AI support.

Pros

Sonar’s main value is in the combination of continuous analysis and simple governance to maintain. Quality gates, PR feedback, IDE integration, and support for several languages help put quality and security inside the team’s normal flow.

Another important point is that it serves teams at very different stages. Community Build works as an entry point for those who want to run self-managed at no cost. SonarQube Cloud simplifies adoption for those who prefer not to operate the infrastructure. And the larger editions cover scenarios with stronger governance requirements.

For companies that do not want to treat quality and security as two completely separate purchases, Sonar remains a practical option. It does not cover everything a more complete AppSec stack covers, but it helps a lot in creating a minimum quality standard per project.

Cons

The main caution is AppSec depth. Sonar covers vulnerabilities and security hotspots, but it is not necessarily the most suitable choice when the priority is application security at a more specialized level, such as tools focused on SAST, advanced policies, or more customized analysis.

It is also worth understanding the LOC-based model well, especially in SonarQube Cloud. In organizations with many private projects, large monorepos, or older codebases, this point can show up early in the bill.

Another caution is not expecting Sonar to work as a contextual PR reviewer. It is very good at applying rules, quality gates, and analysis standards, but it does not have the same proposal of understanding repository history, specific product rules, or the team’s architectural decisions.

Pricing

On SonarQube Cloud, the Free plan covers up to 50k private LOC and 5 members. The Team plan starts at $32 per month. The Enterprise plan is custom pricing. SonarQube Community Build self-managed is free.

3. Snyk Code

Snyk Code, which many people still know as Snyk DeepCode, is part of Snyk’s platform. The proposal is to bring security closer to the developer, covering the flow from IDE to CI without leaving everything to a separate stage at the end of the cycle.

It makes sense for teams looking for a SAST tool that is more connected to day-to-day development, but with a purchase driven by AppSec. The focus is not on doing a general engineering review, but on finding vulnerabilities, prioritizing what needs to be fixed, and helping the team resolve it earlier.

Snyk also fits well in companies that want to concentrate SAST, SCA, IaC, and container security in the same platform. For teams that already use Snyk for dependencies or security governance, Snyk Code comes in almost as a natural extension of the flow.

The point that deserves attention is the commercial model. Snyk sells the platform by modules, so the real cost depends on the package adopted, the products included, and usage volume. For small teams, this can become more expensive than it looks when only looking at the entry price. It is also worth looking carefully at where features like AI Fix, advanced governance, and custom rules come in.

Pros

Snyk’s main point is the developer-first experience. Feedback appears early, in the IDE, CLI, SCM, or CI, so the developer can fix some of the problems before they become a ticket or security backlog.

Another important point is the integration with the rest of the platform. For teams that already deal with vulnerable dependencies, IaC, containers, and AppSec policies, using Snyk Code inside the same stack can simplify operations.

The fix side also helps. Features like DeepCode AI Fix and Snyk Agent Fix reduce the distance between finding a problem and opening a path to fix it, which is especially useful in teams with a large accumulated security backlog.

Cons

The caution is not expecting Snyk Code to work as a deeply contextual PR reviewer, at the level of architecture, repository history, and specific product rules. It is more centered on security than on understanding how the team reviews code day to day. Teams that want similar AppSec coverage with a different review workflow or more control over code review behavior may want to compare Snyk alternatives before deciding.

It is also worth looking carefully at the price. Since the platform is modular, the bill can go up as more products, integrations, higher limits, and advanced features come in.

Custom rules exist, but they do not seem to be the simplest entry path for every team. In general, this kind of feature tends to make more sense in larger plans or in teams with a more mature AppSec operation.

Pricing

On the Team plan, Snyk starts at $25 per contributing developer per month, with a minimum of 5 developers. There is a Free plan with limits, and the Ignite and Enterprise plans are custom pricing.

4. Checkmarx One AI

Checkmarx One remains a well-known reference in the enterprise AppSec market. The platform covers SAST, SCA, API security, DAST, containers, IaC, secrets, and newer AI-assisted remediation features.

It is a tool that usually makes sense when the company already has a formal AppSec program, with the need to centralize scanners, policies, triage, and fixes in the same platform. It is not a solution designed for small or very simple adoption. It fits better in organizations that already treat application security as part of engineering governance.

The Checkmarx One Assist layer reinforces this positioning. Developer Assist brings detection and guidance into the IDE, covering SAST, SCA, IaC, secrets, and packages. The idea is to reduce the distance between finding a vulnerability and guiding the fix, especially in companies with a high code volume and a large accumulated security backlog.

In a practical comparison, Checkmarx is not the best option on the list for some teams. It makes more sense for companies that need broad coverage, centralized control, and a more structured AppSec process.

Pros

Checkmarx’s main value is enterprise coverage. It brings several types of analysis together in the same platform, which helps companies that need to deal with proprietary code, dependencies, APIs, containers, IaC, and secrets without spreading everything across separate tools.

Another important point is process centralization. For AppSec teams that need to track findings, prioritize risk, guide fixes, and maintain policies across several repositories, Checkmarx offers a more complete base than tools focused on only one part of the problem.

The AI-assisted layer also helps in the remediation flow. Developer Assist brings guidance closer to the environment where the developer works, which can reduce some of the back-and-forth between AppSec and engineering.

Cons

The main caution is adoption complexity. Consultative sales, bundles, add-ons, and enterprise operation can make sense for a large organization, but become too much for smaller teams or for anyone who wants to test a tool quickly.

It is also a platform where the value shows up better when internal maturity already exists. Without a clearer AppSec process, part of the coverage can become just another queue of findings to triage.

Another point is that Checkmarx tends to be less attractive for squads that want a simple entry point, quick setup, and a direct focus on the PR flow. In those cases, lighter tools may fit better.

Pricing

Checkmarx does not publish an open pricing table. The model is custom pricing, with bundles and packages defined according to the company’s scenario.

5. GitHub Advanced Security (CodeQL)

CodeQL is one of the best-known tools on this list when the conversation is semantic analysis. Instead of just searching for known patterns, it treats code as queryable data. This makes it possible to analyze flow, find vulnerability variants, and create custom queries with a level of depth that few tools offer.

In practice, it makes a lot of sense for companies that are already in the GitHub ecosystem and want to bring code scanning into the normal development flow. PR alerts, query packs, security overview, and integration with GitHub Code Security make the experience simpler for GitHub-first teams.

The point of attention is that CodeQL delivers more when there is technical capacity to go beyond basic use. The tool can run with ready-made scans, but the differentiator appears when the team can work with queries, adjust workflows, and create its own checks over time.

That is why it is not necessarily the simplest option for every team. It is a good tool for those who want depth and already have the maturity to operate this type of analysis, but it may require more effort from teams looking for a quick and low-customization entry point.

Pros

CodeQL’s main differentiator is analysis depth. For vulnerabilities that depend on dataflow, relationships between functions, and a more semantic understanding of the code, it remains one of the most complete options.

Another important point is the integration with GitHub. Code scanning alerts, security overview, dependency review, and Copilot Autofix create a simpler experience for companies that already centralize development on the platform.

The ability to create custom queries also matters. Teams with more AppSec maturity can turn internal patterns, vulnerability classes, and specific codebase cases into their own analyses.

Cons

The main caution is the ecosystem. CodeQL can be used in other contexts, but part of the operational advantage is in the native integration with GitHub. Outside GitHub-first environments, it may require more work to deliver a similar experience.

Another point is the technical curve. CodeQL is very good, but getting more out of the tool requires people who are comfortable with query language, security workflows, and maintaining checks over time.

It is also important not to treat CodeQL as a “plug and play” tool for every scenario. Basic use already helps, but the most interesting part appears when there is a security operation prepared to customize and maintain the analysis.

Pricing

In the GitHub Code Security offering, the public price is $30 per active committer per month. For private repositories, it works as an add-on inside the GitHub Team or Enterprise ecosystem. For public repositories on GitHub.com, some security features are available for free.

6. Semgrep

Semgrep remains one of the best-known options when the conversation is SAST with customization and direct integration with the developer flow.

It became known for combining ready-made rules, custom rules, and lighter adoption than many traditional enterprise platforms. Today, the platform already goes beyond SAST and includes SCA, secrets, reachability, and AI features for triage and remediation.

In practice, it makes a lot of sense for teams that want to adapt analysis to the way they work, without depending only on generic checks. You can use community rules, write your own rules, run it in CI, integrate with PRs, and bring part of the analysis into the normal development flow.

The point of attention is that Semgrep works best when someone on the team takes care of rules, policies, and adjustments. In teams that just want to turn the tool on and never touch it again, part of its potential ends up underused.

Pros

Semgrep’s main differentiator is rule customization. Teams that already know which patterns they want to reinforce can turn that into concrete analysis without depending only on a closed package of checks.

Coverage is also broad. Semgrep offers SAST, SCA, and secrets detection, integrates with IDE and CI/CD, and the documentation mentions support for more than two dozen languages, with cross-file analysis and reachability in some of them.

Another important point is that it serves both smaller teams and more mature AppSec operations well. A team can start with ready-made rules and evolve toward more specific policies as the process matures.

Cons

The main caution is operational. Semgrep delivers more when there is someone maintaining rules, policies, and triage criteria. Without that care, the tool still helps, but it tends to be closer to a common scanner.

Another point is that it is still more centered on AppSec than on general engineering review. To analyze product decisions, repository history, specific architecture, and team memory, other approaches may make more sense.

It is also important to look at plan limits. Features like on-prem SCM support, custom CI/CD integrations, dedicated infrastructure, and no repository or contributor limit appear in Enterprise.

Pricing

Semgrep has a Free plan for up to 10 contributors. On Teams, Code and Supply Chain start at $30 per contributor per month, while Secrets starts at $15 per contributor per month. The Enterprise plan is custom pricing.

7. Aikido

Aikido should not be read only as a SAST tool. It works more like an all-in-one code and cloud security platform, and that changes how the product should be evaluated.

It covers SAST, SCA, IaC, secrets, cloud, runtime, containers, and AI remediation. For companies that want to reduce the number of separate tools, Aikido can make sense because it concentrates several AppSec fronts in a single operational layer.

In the scope of this article, it appears because it covers PR checks, IDE, custom rules, autofix, and risk-oriented analysis. But the purchase does not revolve only around SAST. The center of the proposal is a broader platform for application and cloud security.

The AI SAST side also stands out because it tries to go beyond traditional rules. The proposal is to find classes of problems that more static scanners usually struggle to catch, such as logic flaws, broken access controls, and flows that depend more on application context.

Pros

Aikido’s main point is the breadth of the package. A team can look at code security, dependencies, IaC, cloud, containers, and secrets inside the same platform. This mainly helps smaller companies or teams with a lean AppSec team that do not want to operate several separate tools.

The AI remediation layer also helps the flow. Instead of only pointing out the problem, the platform tries to bring the fix closer to where the developer works, both in the PR and in the IDE.

Another positive point is commercial transparency. Aikido publishes prices more clearly than many enterprise vendors, which makes the initial evaluation easier and avoids depending on a sales conversation right at the beginning.

Cons

The main caution is depth by language and stack. The documentation itself shows that some features, such as cross-file taint analysis, have more complete support in certain stacks than in others. In a serious POC, this needs to be tested on the company’s real codebase.

It is also worth remembering that Aikido is a broad platform. For those looking only for pure SAST, part of the package may be larger than necessary.

Another point is that teams with more AppSec maturity may still prefer more specialized tools for specific cases, especially when they need very advanced rules, deep tuning, or an operation already built around another scanner. That is usually when a closer look at Aikido alternatives becomes useful.

Pricing

The Developer plan is free for up to 2 users. The Basic plan starts at $300 per month, including 10 users. The Pro plan starts at $600 per month, also including 10 users. The Enterprise plan is custom pricing.

8. Endor Labs

Endor Labs first became known for supply chain and reachability, but today it is also pulling the conversation toward AI SAST and AI security code review.

The proposal is to attack a very common AppSec problem: reducing noise and bringing analysis closer to what can actually be exploited. Instead of treating every finding as equal, the platform tries to prioritize what has a higher chance of representing real risk to the application.

In the SAST layer, Endor uses Opengrep together with AI analysis to classify findings as true positives or false positives. In the AI SAST layer, the promise is to get closer to the reasoning of a security engineer, looking at multi-file flows, relationships between functions, and defects that are harder to catch with simple rules.

There is also an AI Security Review layer for PRs. It brings part of this analysis into the code review flow, but today it still seems more restricted: the documentation positions the feature for GitHub and in beta.

In the comparison, Endor Labs makes more sense in companies where the volume of findings has already become an operational problem. It does not seem to be a lightweight or self-serve purchase for every team. The fit is better in organizations with more mature AppSec, many dependencies, many repositories, and a clear need for prioritization.

Pros

Endor Labs’s main point is the combination of supply chain, reachability, and AI security analysis. For teams struggling with too many alerts, this helps better separate what deserves attention from what probably will not become a real risk.

Another important point is the attempt to reduce false positives in SAST. AI classification helps attack one of the oldest pains in this type of tool: the backlog full of findings that no one trusts enough to fix.

The more contextual analysis also helps in complex scenarios. Flows between files, multiple functions, and indirect dependencies are usually hard for simpler scanners, and that is exactly where Endor tries to differentiate itself.

Cons

The main caution is the adoption profile. Endor Labs looks more like an enterprise product than a simple tool to get started with in a few minutes. For small squads or teams without a clearer AppSec operation, it may be more product than necessary.

The scope of AI Security Review also needs to be confirmed. Based on the current positioning, it still appears more restricted to GitHub and in beta, so it needs to be validated before becoming a purchase criterion.

Pricing

Endor Labs does not publish an open pricing table. The model is custom pricing.

9. Veracode

Veracode is one of the most traditional names on this list. That still counts a lot in large, regulated companies or companies with many different systems, including legacy applications.

It is not the newest tool in the comparison, and it does not try to sell a lightweight adoption experience. Its point is broad coverage, a more structured process, and use in environments where security needs to go through governance, audit, and well-defined criteria.

Veracode’s SAST works with source code analysis, compiled binary, or a combination of both. This detail matters in companies with varied stacks, older systems, and distributed teams, where the flow is not always as simple as “opened a PR, run the scanner, fix it right there.”

In the comparison, Veracode makes more sense for organizations that already have a more mature AppSec operation. For squads that want something simpler, closer to the PR, and with more local autonomy, other tools may fit better day to day.

Pros

Veracode’s main point is coverage. The public documentation highlights support for more than 100 languages and frameworks, which helps a lot in companies with large portfolios and applications in different stacks.

Another important point is binary and bytecode analysis. This is still useful in environments where the company needs to evaluate applications without depending only on source code, or where there are legacy systems that are hard to standardize.

The remediation side with Veracode Fix also helps bring security closer to the development flow. For companies that already use the platform at scale, this can reduce some of the effort between finding a flaw and guiding the fix.

For regulated organizations, the combination of coverage, market history, and SDLC integration remains a clear reason to consider Veracode.

Cons

The main caution is operational effort. Veracode usually makes more sense in companies that already have an AppSec process, governance, and teams prepared to operate a platform of this size.

For small teams or squads that want fast and lighter adoption, it can feel too big for the problem.

It also would not be my first choice if the priority were team-level flexibility, quick workflow customization, or a more open experience around model, context, and specific product rules.

Pricing

Veracode does not publish a self-serve pricing table for SAST. The model is custom pricing.

10. Mend.io

Mend.io has moved beyond its origin in open source security. Today, it presents itself as an AppSec and AI security platform, covering SAST, SCA, containers, update automation, and governance of AI components.

This changes how the product should be evaluated. Instead of looking at Mend only as a SAST scanner, many companies will see the tool as a way to bring more parts of the security operation into a single layer.

On the code axis, Mend combines SAST with SCA, reachability-based prioritization, AI fixes, and integration with coding assistants. This helps companies that deal at the same time with vulnerabilities in proprietary code, open source dependencies, and code generated with AI support.

There is also a clear governance angle. Mend tries to capture risk earlier, including in tools like Cursor, Windsurf, and Copilot, before the problem reaches the PR or becomes security backlog.

Pros

Mend’s main point is the attempt to bring code security, dependencies, and AI security into the same platform. For companies already struggling with several separate tools, this can simplify part of the operation.

The platform also helps in scenarios where dependencies, updates, and reachability already consume a lot of the team’s time. Instead of just listing vulnerabilities, Mend tries to better prioritize what needs attention and reduce some of the manual work of fixing.

Another important point is its presence in flows with coding assistants. For companies concerned about AI-generated code, this puts Mend in a more current conversation than tools focused only on traditional SAST.

Cons

The main caution is scope. For some teams, Mend can be too much platform for a problem that is still small or very localized.

It is also important to validate the IDE experience and the team’s real workflow. Since part of the documentation shows transitions in plugins and integrations, it is worth testing in the company’s stack before treating this as a decisive criterion.

Another point is cost. The full proposal goes through AppSec, supply chain, and AI security, so the bill can get high for teams that only want to solve SAST in isolation.

Pricing

On the pricing page, Mend lists the AppSec plan at up to $1,000 per dev/year, AI Premium at up to $300 per dev/year, and Renovate Enterprise at up to $250 per dev/year.

11. Socket

Socket appears on this list because code security no longer lives separately from supply chain. In many companies, the most urgent risk is not in proprietary code, but in vulnerable dependencies, malicious packages, and CVE alerts that do not always affect the application.

The platform was born in software supply chain security and is still more specialized in that than in traditional SAST. The focus is on vulnerabilities and dependency behavior, reachability, malware, policy enforcement, GitHub Actions, AI models, and firewall at install time.

It has already added basic SAST, secrets, container scanning, and AI model analysis, but it should not be read as a tool focused mainly on proprietary code. Socket’s point is to help the team better understand risk coming from the open source ecosystem and cut some of the noise that usually blocks triage.

If the team’s main problem is “I cannot take another dashboard full of irrelevant CVEs,” Socket enters the comparison early. If the priority is to find logic flaws, auth bypass, or flow problems inside the application code, it works better as a complement to a SAST tool more focused on first-party code.

Pros

Socket’s main differentiator is reachability. The platform tries to show which vulnerabilities can actually affect the application, instead of treating every CVE in a dependency as the same priority.

Another important point is package behavior analysis. In supply chain, the risk does not come only from known vulnerabilities. Malicious packages, typosquatting, suspicious scripts, and dangerous changes in dependencies also count.

Protection at install time also helps. Socket Firewall can block suspicious packages before they enter the developer’s environment, which reduces a class of problems that traditional scanners usually catch too late.

Public pricing also makes the initial evaluation easier. For smaller teams, it is simpler to understand the bill before talking to sales.

Cons

The main caution is expectation. Socket is not the tool I would put as the first choice for deep analysis of proprietary code, business logic, or internal application flows. The platform’s focus is still supply chain.

It can complement an AppSec stack very well, but it does not necessarily replace a SAST tool more dedicated to internal code.

It is also worth looking carefully at what comes in each plan. Depending on the level of analysis, repository volume, and governance needs, the simplest plan may not cover everything the team needs.

Pricing

Socket publishes a Free plan, a Team plan at $25 per developer per month, a Business plan at $50 per developer per month, and Enterprise with custom pricing.

12. Codacy

Codacy is an option when the company wants to combine quality, security, and guardrails in a platform that is simple to turn on and operate.

It does not try to occupy the same place as a highly specialized SAST tool. The proposal appears more as a practical layer for continuous analysis, quality gates, PR feedback, AI guardrails, and visibility by repository and team.

For some teams, that is exactly what is missing. Not necessarily a deeper tool, but a simpler way to keep quality and security standards inside the normal development flow.

Codacy also stands out because of its more predictable commercial model. The company presents plans without usage limits, which can make the bill easier for teams that do not want to estimate consumption by analysis, repository, or run.

Pros

Codacy’s main point is simple adoption. The platform tries to avoid too much pipeline complexity and bring fast feedback to the PR and the IDE.

The coverage fits the product proposal well. There are 49 languages and frameworks, with SAST, secrets, dependency checks, malicious package detection, dashboards, and AI guardrails.

Another important point is the focus on enforcement in the developer flow. Instead of leaving quality and security only in a separate dashboard, Codacy tries to bring these signals to where the team already works.

For teams that want to improve quality and security without restructuring the whole process, this helps a lot.

Cons

The main caution is depth. Codacy makes more sense as an operational platform for continuous analysis than as the only SAST engine for more complex cases.

It is also important to consider SCM limitations. The public documentation shows a focus on GitHub Cloud, GitLab Cloud, and Bitbucket Cloud. At the moment, there is no support for Git on-premise or Azure Repos.

Another point is that, in companies with a more mature AppSec operation, Codacy may fit better as a quality and guardrails layer than as a direct replacement for more specialized tools.

Pricing

Codacy offers a free Developer plan. The Team plan starts at $18 per developer per month annually or $21 monthly. The Business plan is custom pricing.

Full comparison of SAST tools in 2026

FAQ

What is a SAST tool?

A SAST tool analyzes source code, bytecode, or binaries without running the application. It helps find vulnerabilities, insecure patterns, hardcoded secrets, injection risks, and other security issues early in development.

What is the best SAST tool for most teams in 2026?

If your definition of SAST is strictly a classic static analysis engine, Semgrep and CodeQL are among the strongest choices. But if the question is more practical, meaning which tool actually helps detect risk in the real development flow, with context, custom rules, and less friction, Kodus stands out as the most complete option for most modern teams.

Why does Kodus appear so strongly on this list if it is not a pure classic SAST tool?

Because the market has changed. Today, many teams do not just need a scanner that generates alerts. They need useful structural analysis in the PR, in the CLI, and in the team’s workflow. Kodus combines AST-based analysis, module dependencies, natural-language rules, repository context, cross-file checks, and configurable severity. In practice, this puts it as a very good SAST option, especially for teams that want more control and less noise.

Which tool has the best custom rules?

Semgrep remains a strong reference in custom rules for technical AppSec. But Kodus deserves attention because it makes this process much more accessible in the team’s day-to-day work, with natural-language rules, scope by directory or language, categories and severity, plus sync with existing rule files. For many teams, this is more practical than depending only on a more rigid technical engine.

Which tool has the best value for money?

For teams that value flexibility, predictability, and control, Kodus has one of the best value-for-money profiles on the list. The Community plan is free, Teams costs $10 per dev/month, and the BYOK model avoids token markup. Semgrep Free is also a great entry point. Broader platforms like Snyk, Mend, Veracode, and Checkmarx tend to make more sense when the company already wants a larger AppSec strategy.

What is the best option for a monorepo?

Kodus is one of the most natural options for monorepos because it allows rules by directory, different scopes by code area, repository context, and analysis that is more adaptable to the way the team organizes the codebase. Semgrep also works very well in this scenario, especially in teams with more mature AppSec. CodeQL fits better when the GitHub ecosystem is already dominant.

Which tool is better for teams already living in GitHub?

CodeQL is the most natural choice for GitHub-first organizations that want deep semantic analysis and native integration with GitHub Code Security. Still, Kodus remains very competitive in this scenario when the goal is to add more flexible rules, review context, and a more adaptable layer of control in the PR.

Which tool reduces false positives best?

Endor Labs, Semgrep, Aikido, and Mend talk a lot about noise reduction, but through different paths. Endor bets heavily on exploitability and reachability. Semgrep combines fine tuning with AI-assisted triage. Aikido tries to capture more contextual bugs with AI SAST. Mend mixes prioritization of first-party and open source risk. Kodus reduces noise through another path: it uses repository context, team history, and custom rules to avoid generic comments and findings that are not very actionable.

Which tool makes the most sense for startups and small teams?

Kodus, Semgrep Free, Codacy, and Aikido’s free plan are usually more accessible entry points. Kodus’s advantage is that it already delivers a strong base of contextual analysis and customization without requiring a heavy AppSec platform from the start. For small teams, that usually matters a lot.

What is the best open source tool for SAST?

Among the more open options, Semgrep and CodeQL are two well-known names in SAST. Semgrep works well for those who want custom rules and simpler adoption. CodeQL makes more sense for GitHub-first teams that need deeper semantic analysis.

Kodus comes in through another path. It is not a pure classic SAST tool, but it can be the best option for teams that want to bring static analysis into the real review flow. The platform combines AST-based analysis, repository context, natural-language rules, BYOK, and greater control over how findings appear in the PR.

In practice, if the idea is to have a traditional SAST scanner, Semgrep or CodeQL may make more sense. If the goal is to reduce risk in day-to-day development, with team context and fewer loose alerts, Kodus tends to be the most complete option.

What is the difference between SAST and DAST?

SAST analyzes code before the application runs. DAST tests a running application from the outside. SAST is usually better earlier in development, while DAST helps catch runtime and configuration issues.