Side-by-sides with the tools people already use when something breaks in production. Each page says where the other tool is the better choice, and what Patcherly does not do.
Most AI fixes fail because the model sees a stack trace and nothing else. Patcherly gives it what an engineer would check first, without handing over your codebase.
Runtime and framework versions, and the plugins, packages and theme actually installed, collected by the connector on your server within the sharing level you choose.
On Core and Pro, fixes that worked or were rolled back on your own sites guide the next one. A private memory for your workspace, never used to train any model.
The AI sees the error and short redacted excerpts around it. Your code stays on your server. Connect a GitHub repo for pull requests and only the files a fix touches are read.
For comparison, published industry deployments report 58% (Getafix) to around 85% (SapFix) of automated patches rejected. Benchmark figures from the research behind Patcherly (Martelli, OPIT, 2025). Research findings →
Patcherly does one job: it catches errors on a live server, drafts the fix, and after you approve it applies it with a backup and rollback, or opens it as a GitHub pull request. If you need any of the things on the right, keep the tool that does them. Patcherly is built to sit alongside it.
Connect one site and look at what Patcherly drafts for the errors it was already throwing. Nothing is applied until you approve it.
Full Pro plan for 30 days. No credit card. Free Personal plan after that, for as long as you like.