GitHub Code Quality moved out of public preview and into general availability on 20 July 2026, and billing started the same day it went live. The pricing has two parts: a flat $10 per active committer per month for enabled repositories, plus usage-based charges for the AI-powered pieces — AI-assisted issue detection and Copilot Autofix — on top of standard GitHub Actions compute minutes for running the underlying CodeQL analysis. It’s available on GitHub Enterprise Cloud and GitHub Team plans; Enterprise Server doesn’t get it at launch.
The product itself combines CodeQL’s deterministic static analysis with AI-assisted detection to flag maintainability and reliability issues directly on pull requests, then has Copilot Autofix generate a suggested fix for a human to review before merging. New at general availability: organisation-wide enablement, org-level quality dashboards, coverage-based quality gates enforced through GitHub rulesets, and APIs for managing which repositories have it switched on. GitHub says more than 10,000 enterprises used the free preview before this launch.
GitHub’s own framing is direct: “AI accelerates code output, and Code Quality helps teams ship code they trust.” That’s an admission worth sitting with — the tool exists specifically because AI-assisted teams are producing more code than human review alone can reliably check, and GitHub has decided that gap is now a paid product rather than a preview feature bundled into existing seats.
So what
This follows the same arc as GitHub Copilot’s own move to token-based billing at the start of June: as AI tooling shifts from experimental to load-bearing, the vendors start charging for it properly, and quality-gating the output of AI-assisted development is turning into its own budget line rather than something you get for free alongside the coding assistant. If you’re commissioning software from a team that leans on AI-assisted development, it’s a reasonable question to ask what quality gating sits between the AI-generated code and your production branch, and whether that’s something they’ve actually budgeted for rather than treated as a free extra. We treat that discipline as core to how we run AI-assisted development projects — get in touch if you want to see how it works in practice.