Page groups with 5,000+ generated URLs run on a single base template without issue. The data layer is cached and rendering re-uses your existing WordPress page, so the practical ceiling is your hosting plan and your sitemap budget. Most recipe sites top out well below the technical limit because Google's crawl budget for new pages slows past a few thousand.

Yes. Edit your Google Sheet, push to your REST endpoint, or update the JSON file in the theme. SleekRank refreshes on the next cache cycle, and you can clear the cache manually from the admin or via WP-CLI. No theme deploy, no static build, no engineering ticket.

Yes. SleekRank uses your existing base WordPress page as the template. Whatever theme, blocks, page builder, or custom CSS rendered that page renders every generated URL identically. Bricks, Elementor, Gutenberg, Oxygen, and classic themes all work because SleekRank operates on the rendered HTML.

Yes. They are real WordPress URLs with full HTML, sitemap inclusion, and per-page meta tag mappings for title, description, canonical, and og:image. The base template page is excluded from the sitemap and marked noindex automatically so it never competes with the generated children.

Yes. A meta mapping pointing at a JSON-LD script tag in the head produces full Recipe schema per page, with name, recipeIngredient, recipeInstructions, recipeYield, and totalTime drawn from the row. Google's recipe rich results pick this up cleanly.

On the next cache refresh the URL stops resolving and returns 404. The sitemap is regenerated automatically so search engines drop the URL cleanly. If you need a redirect to a successor build, point the slug at a wildcard rule in your normal WordPress redirects plugin before deleting the row.

Add a temperature column, then ship distinct slugs when the build differs (e.g. classic-cuban vs grilled-cuban). The selector mapping renders the badge; shared filling notes come from a sibling row via a Twig include.

Yes. A URL pattern like /{region}/{slug}/ produces /vietnam/banh-mi/, /usa/reuben/ from a combined dataset. Use a region column with a fixed slug list, then run mappings against the cross-product where the search volume justifies the per-region depth.