A developer receiving a security finding needs more than another warning. The useful handoff explains the vulnerable behavior, proposes a change and gives the reviewer enough context to decide whether that change belongs in the application. AI AppSec assistants are worth comparing around that handoff, rather than the number of comments they can generate.
For an engineering lead already using GitHub CodeQL, start with GitHub Code Security's Copilot Autofix before buying a separate remediation product. Compare Semgrep Code when the team wants its finding analysis and a draft pull-request workflow across connected repositories. Existing Checkmarx One customers should evaluate Triage Assist and Remediation Assist against their supported project configuration before replacing the platform.
This guide is for a small software company with private repositories, developer reviewers and somebody responsible for application security. Solo maintainers may be served by public-repository features or a free scanner plan. Larger organizations enter the comparison where governance and an existing security platform change the choice. Research was checked on 10 November 2026; no product was installed or tested for this article.
Follow a finding through to the reviewer
Consider a hypothetical access-control finding in a service your team maintains. The security lead establishes whether it is relevant, the responsible developer examines the proposed change, and the normal test and review process determines whether to merge it. The security scan then checks the changed code. A plausible patch is an input to that process, not evidence that the vulnerability has been eliminated.
Three outputs deserve separate treatment: an explanation of the finding, a suggested code change, and a pull request containing that change. They impose different work on the developer and may use different billing units. A tool that offers guidance has not necessarily created a branch, and a generated pull request has not necessarily passed the application's tests.
These products are also different from general code review tools. This comparison focuses on security findings and their remediation. It does not rank coding assistants for writing features, replace the underlying scanner, or claim that AI can decide every business-specific authorization rule.
GitHub Code Security: use the finding already in GitHub
Classic Copilot Autofix is a useful first finalist when CodeQL alerts already reach the team's GitHub workflow. GitHub's feature comparison includes suggested fixes and explanations for CodeQL alerts in pull requests and the backlog. Public repositories have access; organization-owned private or internal repositories need the qualifying Code Security entitlement.
The distinction from a general Copilot subscription is material. GitHub documents that a separate Copilot subscription is not required for classic Autofix. Buying a coding assistant seat merely to obtain this security feature would therefore confuse two purchases. Conversely, having an ordinary coding subscription is not a substitute for private-repository Code Security licensing.
In the documented backlog workflow, a user opens a code-scanning alert and generates a suggested fix where supported. The developer reviews the proposed change and carries it through the normal development process. The repository administrator must first ensure that code scanning and the required access are configured; ongoing work includes scan configuration, reviewing proposed changes and maintaining the tests used to validate them.
GitHub also documents agentic autofix, which explores the codebase and opens a pull request. That is a separate mode: it consumes Code Security AI Credits and GitHub Actions minutes. The billing documentation explicitly says classic Autofix does not consume AI credits. The cost comparison below therefore uses classic suggestions, not an unpriced assumption of unlimited agent sessions.
Choose this GitHub route over Semgrep when retaining the CodeQL finding and existing GitHub operating model is the main requirement. Compare Semgrep when its finding analysis and draft PR or merge-request output are the desired workflow. This is a conditional workflow recommendation; no relative accuracy, fix-success rate or setup-speed claim was measured.
Semgrep Code: distinguish guidance from a proposed pull request
Semgrep's current AI documentation uses the name Multimodal, so older references to an assistant should not be treated as a separate product purchase. Its overview describes explanations, triage and remediation guidance connected to findings. It also distinguishes Suggested fix guidance from Autofix code changes. That distinction is useful for a security lead deciding how much investigation remains with developers.
For the PR-producing workflow, the Autofix documentation says Semgrep generates proposed changes, creates a branch and opens a draft pull or merge request. A connected repository with Semgrep Code findings, enabled Multimodal and appropriate read/write access is required. On GitHub, insufficient repository write permission triggers setup instructions rather than a generated PR. A developer or repository administrator needs to handle that integration; a dashboard-only security user cannot assume it is already configured.
In ordinary use, the security lead identifies the finding, requests an Autofix, and the developer reviews the draft with its code change. Tests, code-owner review and rescanning remain part of the team's release process. Recurring administration includes repository connections, relevant rules, permissions and the AI usage balance. These are documented prerequisites and editorial operating recommendations, not observed implementation hours.
Choose Semgrep over GitHub's classic suggestions when the finding-to-draft-PR path and Semgrep's analysis are worth adopting as the team's security workflow. Stay with GitHub when its existing alerts and suggestions already give reviewers enough support. A different finding engine and configuration need an evaluation on your code; two subscriptions at the same monthly component price are not automatically equivalent services.
Compare one team's subscription and AI workload
Use this hypothetical monthly scenario: ten active contributing developers, twenty private repositories and one security administrator who does not commit code. The team reviews twenty security findings and requests five AI-assisted fixes. It needs contextual findings and developer-controlled changes, not automatic merging. Prices are USD and exclude taxes, existing source-host subscriptions, runner charges and staff time unless stated.
| Option | Qualifying configuration | Published subscription component | AI workload treatment | Important purchase boundary |
|---|---|---|---|---|
| GitHub | Existing Team or Enterprise organization plus Code Security | $30 per active committer/month; ten is $300/month | Classic fix suggestions do not use AI credits | Agentic autofix and Actions use are separate |
| Semgrep | Code Teams, connected repositories and Multimodal enabled | $30 per contributor/month; ten is $300/month | Twenty analyses plus five Autofixes use 120 credits | Other AI activity also draws on the allowance |
| Checkmarx | One configuration with Triage Assist and Remediation Assist | Custom package and prepaid-credit proposal | Twenty triage actions plus five remediation actions use 35 credits | Credits are not stated here as dollars |
GitHub's active-committer licensing uses a rolling ninety-day definition for repositories with the product enabled. Semgrep likewise counts contributors to scanned private repositories over ninety days. The hypothetical ten people must satisfy each service's definition; an organization should not substitute its payroll headcount. The non-contributing administrator is not an extra active developer in this example.
For Semgrep's published Teams plan, ten contributor seats include 200 AI credits monthly. Its usage documentation assigns one credit to analysis and twenty to an Autofix: 20 × 1 + 5 × 20 = 120 credits. This leaves room within that allowance only if other consumption does not exhaust it. Additional AI detection scans can change the calculation. The Free Edition's ten-private-repository limit does not fit this twenty-repository example, even though the contributor count is ten.
GitHub's $300 monthly Code Security component does not mean it performs the same twenty analyses and creates the same five PRs for that amount. Here the team reviews findings and uses classic suggestions; purchasing agentic work changes the meter. Semgrep's $300 monthly Code component includes the modeled credits but excludes Supply Chain and Secrets subscriptions. These are bounded purchase comparisons, not total-cost estimates or a cheapest-tool verdict.
Semgrep documents monthly charging for self-service Teams subscriptions paid by card. Cancellation stops renewal, with access continuing through the paid term. GitHub's applicable billing arrangement and any sales-managed Semgrep commitment should be checked against the actual account or proposal. No annual discount, refund entitlement or unstated seat minimum is assumed.
Checkmarx One: keep governance with the security platform
Checkmarx deserves consideration when the organization already operates One and wants triage and remediation policy in that environment. Its current product page documents global execution rules with project overrides, supported SAST and SCA findings, and developer review of proposed changes. Supported GitHub and Azure DevOps Code Repository Integration projects can receive remediation pull requests; other eligible project types can use guidance or patch exports. Do not assume that every connected source-code manager has the same PR workflow.
The same modeled workload consumes 35 Checkmarx Credits: twenty triage actions at one credit each, plus five remediation actions at three credits each. The pricing page describes a shared prepaid balance and custom packages. Credit-consuming actions pause at zero without automatic overage charges. The dollar value, minimum purchase, contract term and qualifying configuration require a proposal; thirty-five credits is not a thirty-five-dollar quote.
Choose this route over adopting another scanner when existing project policy and review workflows are the decisive requirement. Compare Semgrep when the team is choosing a new Code workflow instead. Large organizations must also scope deployment, code handling, support and exit requirements in the actual contract; the presence of platform governance does not establish identical AI behavior in every deployment.
Existing Veracode users should also inspect Veracode Fix before switching platforms. Its documented SAST and dependency-remediation offerings are different configurations. It is an existing-stack route here, not a claim that a generic platform license automatically includes every fix capability.
Pay for less investigation, not more generated patches
The business case starts with work the team can observe: investigation, preparing a change, reviewing it, running tests and handling rework. Record those separately for the present process and the proposed tool. Add the subscription, metered AI activity, integration maintenance and runner usage. A developer's released time is capacity unless the business actually changes expenditure; do not report it automatically as cash savings.
Pay more when the required integration, code-handling arrangement or reviewer-ready output materially changes that work. Keep the existing tool when supported suggestions already resolve the bottleneck. A solo maintainer working in public GitHub repositories should examine the available public-repository Autofix route first, rather than copy this private-team subscription scenario. Teams with no one to review changes have an ownership problem that another fix generator does not solve.
For the primary small team, shortlist GitHub Code Security and Semgrep Code, compare the same supported finding types, and inspect the change that actually reaches a reviewer. Keep accepted changes, rejected proposals and rework separate when judging usefulness. This gives the purchase a practical basis without inventing a success rate, promising zero vulnerabilities or replacing the team's approval responsibility.
