TL;DR: We replaced 100+ isolated Cloud Run services with a single Compute Engine VM running 23 WordPress sites, a unified Content Engine, and autonomous AI workflows — cutting hosting costs to $15-25/site/month while launching new client sites in under 10 minutes.
The Problem With One Site, One Stack
When we started managing WordPress sites for clients at Tygart Media, each site got its own infrastructure: a Cloud Run container, its own database, its own AI pipeline, its own monitoring. At 5 sites, this was manageable. At 15, it was expensive. At 23, it was architecturally insane — over 100 Cloud Run services spinning up and down, each billing independently, each requiring separate deployments and credential management.
The monthly infrastructure cost was approaching $2,000 for what amounted to medium-traffic WordPress sites. The cognitive overhead was worse: updating a single AI optimization skill meant deploying it 23 times.
So we built the Site Factory.
Three-Layer Architecture
The Site Factory runs on a three-layer model that separates shared infrastructure from per-site WordPress instances and AI operations.
Layer 1: Shared Platform (GCP). A single Compute Engine VM hosts all 23 WordPress installations with a shared MySQL instance and a centralized BigQuery data warehouse. A single Content Engine — one Cloud Run service — handles all AI-powered content operations across every site. A Site Registry in BigQuery maps every site to its credentials, hosting configuration, and optimization schedule.
Layer 2: Per-Site WordPress. Each WordPress installation lives in its own directory on the VM with its own database. They share the same PHP runtime, Nginx configuration, and SSL certificates, but their content and configurations are completely isolated. Hosting cost per site: $15-25/month, compared to $80-150/month on containerized Cloud Run.
Layer 3: Claude Operations. This is where the Expert-in-the-Loop architecture meets WordPress at scale. Routine operations — SEO scoring, schema injection, internal linking audits, AEO refreshes — run autonomously via Cloud Scheduler. Strategic operations — content strategy, complex article writing, taxonomy redesign — route to an interactive AI session where Claude operates as a system administrator with full context about every site in the registry.
The Model Router
Not every AI task requires the same model. Schema injection? Haiku handles it in 2 seconds at $0.001. A nuanced 2,000-word article on luxury asset lending? That’s Opus territory. SERP data extraction? Gemini is faster and cheaper.
The Model Router is a centralized Cloud Run service that accepts task requests and dynamically routes them to the cheapest capable model on Vertex AI. It evaluates task complexity, required output length, and domain specificity, then selects the optimal model. This alone cut our AI compute costs by 40% compared to routing everything through a single frontier model.
10-Minute Site Launch
Adding a new client site to the factory takes 5 configuration steps and under 10 minutes:
Register the domain and SSL certificate in Nginx. Create the WordPress database and installation directory. Add the site to the BigQuery Site Registry with credentials and vertical classification. Run the initial site audit to establish a content baseline. Enable the autonomous optimization schedule.
From that point, the site receives the same AI optimization pipeline as every other site in the factory: daily content scoring, weekly SEO/AEO refreshes, monthly schema audits, and continuous internal linking optimization. No additional infrastructure. No new Cloud Run services. No incremental hosting cost beyond the shared VM allocation.
Self-Healing Loop
At 23 sites, things break. APIs rate-limit. WordPress plugins conflict. SSL certificates expire. The Self-Healing Loop monitors every site and every API endpoint continuously.
When a WordPress REST API call fails, the system retries with exponential backoff. If the failure persists, it falls back to WP-CLI over SSH. If the site is completely unreachable, it triggers a Slack alert to the operations channel and pauses that site’s optimization schedule until the issue is resolved.
For AI model failures, the Model Router implements automatic fallback: if Opus returns a 429 (rate limited), the task routes to Sonnet. If Sonnet fails, it queues for batch processing overnight at reduced rates. No task is ever dropped — only deferred.
Cross-Site Intelligence
The real power of the Site Factory isn’t cost reduction — it’s the intelligence layer that emerges when 23 sites share a single data warehouse. BigQuery holds content performance data, keyword rankings, schema coverage, and information density scores for every post on every site.
This enables cross-site pattern recognition that’s impossible when sites operate in isolation. When an article format performs well on one site, the system can identify similar opportunities across all 22 other sites. When a keyword strategy drives organic growth in one vertical, the Content Engine can adapt that strategy for adjacent verticals automatically.
The Site Factory isn’t a hosting solution. It’s an operating system for AI-powered content operations — one that gets smarter with every site we add.
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