About This Architecture

Enterprise RAG solution on GCP using VPC custom routing across four isolated subnets for ingestion, query, evaluation, and observability. Raw documents flow through Cloud Dataflow and Vertex AI Embedding Model into Vector Search, while user queries traverse Cloud CDN, Cloud Armor WAF, and Cloud Load Balancing to Cloud Run Query Pipeline for LLM inference. Evaluation subnet runs Cloud Functions-triggered model evaluation against ground truth data, with results stored in BigQuery and metrics streamed to Cloud Monitoring. Fork this diagram to customize subnet CIDR ranges, add Cloud VPN for hybrid connectivity, or integrate additional Vertex AI services.

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How do I architect a production RAG solution on GCP with VPC subnets and Vertex AI?

This diagram shows a complete GCP RAG architecture spanning four VPC subnets: ingestion (Dataflow → Vertex AI Embedding → Vector Search), query (Cloud Run → LLM Inference), evaluation (Cloud Functions → Model Evaluation → BigQuery), and observability (Cloud Monitoring). Users access via Cloud CDN and Cloud Armor WAF through Cloud Load Balancing, ensuring security and performance at scale.