Provision Infrastructure and Deploy AI Models with RAG

Scenario

Use Terraform to provision enterprise cloud infrastructure (VPC, ECS clusters, RDS, networking), then deploy custom AI embedding and LLM models on Alibaba Cloud Linux instances for inference serving, and finally deploy a RAG application using Elasticsearch as the vector knowledge base that calls these self-hosted models for end-to-end enterprise AI deployment.

How the products combine

1. bailian+bailian+opensearch+es+opensearch+pai · end-to-end-rag-knowledge-base-to-deployed-pipeli-754299 — End-to-End RAG: Knowledge Base to Deployed Pipeline

See _combos/end-to-end-rag-knowledge-base-to-deployed-pipeli-754299.

2. alinux+es · deploy-complete-rag-system-with-ai-models-d62047 — Deploy Complete RAG System with AI Models

See _combos/deploy-complete-rag-system-with-ai-models-d62047.

3. es · es-deploy-application — Elasticsearch — Deploy a Retrieval-Augmented Generation (RAG) AI application

See es/es-deploy-application.

4. es+rds+terraform · deploy-enterprise-rag-application-stack-457815 — Deploy Enterprise RAG Application Stack

See _combos/deploy-enterprise-rag-application-stack-457815.

Typical questions

• deploy rag with custom infrastructure
• provision and deploy complete rag system
• 搭建企业级RAG基础设施
• 部署自定义模型的RAG系统
• terraform deploy rag with models
• end-to-end enterprise rag deployment
• 从零搭建RAG系统
• build rag with self-hosted models

Q: How do I provision enterprise infrastructure and deploy a complete RAG system with custom models using Terraform? A: You can provision enterprise infrastructure and deploy a complete RAG system with custom models by using Terraform to automate the setup of VPCs, ECS clusters, RDS, and networking. After provisioning, you deploy custom AI embedding and LLM models on Alibaba Cloud Linux instances and connect them to an Elasticsearch-based RAG application that serves as the vector knowledge base.