
Keystone CMS
CMS
Ghost
CMSKeystone CMS and Ghost are both popular choices, but they serve different needs. Keystone CMS is a CMS with a traditional, manual approach to building, while Ghost is a CMS that prioritises developer or designer control.
Below you'll find a side-by-side breakdown of detection signals, AI scores, and technical fingerprints — plus our honest take on which builder wins for different use cases.
How we detect Keystone CMS vs Ghost — see our methodology: AI Influence Score calculation, evidence tiers, and fingerprint signal types.
| Category | CMS | CMS |
| AI Score | 20/100 — Traditional | 20/100 — Traditional |
| Detection Signals | 2 patterns | 7 patterns |
| Script Detection | — | 2 patterns |
| CDN Detection | — | — |
| Header Detection | 1 headers | 2 headers |
| Sites Detected | No data yet | 3,296 scans |
| Best For | Blogs & content-heavy sites | Blogs & content-heavy sitesTry Ghost → |
| Official Website | — | Visit |
CMS
Keystone CMS is a cms with an AI Score of 20/100 (Traditional). Our detection engine uses 2 signal patterns to identify Keystone CMS-built sites.
CMS
Ghost is a cms with an AI Score of 20/100 (Traditional). Our detection engine uses 7 signal patterns to identify Ghost-built sites.
Keystone CMS is an open-source, database-driven content management system built on Node.js and designed for developers who need a flexible, API-first backend with an auto-generated Admin UI for managing structured content. It is commonly used by development teams building custom web applications, editorial platforms, and headless CMS implementations where schema control and GraphQL access are priorities. AIWebsiteDetector.com identifies Keystone-powered sites through a combination of one distinct HTML pattern — typically a markup signature embedded during server-side rendering or within the admin interface — and one characteristic HTTP response header that Keystone's underlying Express-based server exposes during page delivery. These two signals form the core of the detection engine's fingerprinting logic, allowing the tool to distinguish Keystone deployments from other Node.js-based frameworks with reasonable confidence. Because Keystone is self-hosted and infrastructure-agnostic, it appears across a wide range of hosting environments including cloud VMs, containerized setups, and platforms like Railway or Render, making hosting-based heuristics unreliable — which is precisely why header and HTML pattern detection are essential to accurate identification.
Ghost is an open-source Node.js-based content management system designed primarily for professional publishers, bloggers, and media organizations seeking a streamlined platform for subscription-based content and newsletters. AIWebsiteDetector.com identifies Ghost-powered sites using a combination of 2 script patterns, 2 HTML patterns, 2 HTTP headers, and 1 meta tag pattern, providing multiple redundant signals that make detection highly reliable even when sites use custom themes or third-party CDNs. Common detection signals include Ghost-specific script references embedded in page source, characteristic HTML structural attributes injected by the Ghost rendering engine, and HTTP response headers that expose version or platform metadata. The meta tag pattern typically surfaces a generator tag pointing explicitly to Ghost, which remains present across most default and custom theme configurations and serves as one of the most definitive single-signal identifiers. Ghost is available both as a self-hosted open-source installation and as a managed cloud service through Ghost(Pro), meaning detection patterns must account for both infrastructure environments — a distinction reflected in the diversity of header-based signals our engine tracks. The platform's consistent use of standardized theme APIs and its tightly controlled front-end architecture make it one of the more reliably detectable CMS platforms in the publishing category.
Choose Keystone CMS if…
Choose Ghost if…
Our Pick — Based on 3,296+ detections
The most frequently detected cms in our scan database.
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