Building a Production RAG Ingestion Pipeline on AWS: Unstructured.io, S3 Vectors, and a Private VPC

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Learn to build a production RAG ingestion pipeline on AWS using Unstructured.io, S3 Vectors, and a Private VPC to scale your knowledge base

advanced Published 24 May 2026
Action Steps
  1. Build a private VPC on AWS to host your RAG ingestion pipeline
  2. Configure Unstructured.io to ingest unstructured data
  3. Use S3 Vectors to store and manage vector embeddings
  4. Integrate Unstructured.io with S3 Vectors and your private VPC
  5. Test and deploy your RAG ingestion pipeline to production
Who Needs to Know This

This tutorial is beneficial for machine learning engineers, data scientists, and DevOps teams who want to build a scalable RAG ingestion pipeline on AWS. It helps them to overcome production limits and improve the performance of their knowledge base

Key Insight

💡 Using a private VPC and self-hosted tools like Unstructured.io and S3 Vectors can help scale your RAG ingestion pipeline and improve performance

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💡 Build a scalable RAG ingestion pipeline on AWS using Unstructured.io, S3 Vectors, and a Private VPC
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