Building Retrieval Augmented Generation (RAG) systems typically requires expensive vector databases, but a new approach demonstrates how DynamoDB can serve as a cost-effective alternative for proof-of-concept projects. This serverless architecture leverages AWS Lambda, AWS Bedrock, Amazon S3, and Amazon SQS to create a fully functional document Q&A system for approximately $1 per month. The solution works by splitting PDF documents into semantic chunks, generating vector embeddings through Bedrock, and storing them in DynamoDB with user isolation built into the table design.

When users ask questions, the system performs brute-force similarity searches using cosine similarity calculations. This approach matters because it democratizes RAG development for startups, individual developers, and organizations with limited budgets. The implications are significant for machine learning engineers and AI product teams who can now prototype LLM applications without massive infrastructure investments.

However, the trade-off is reduced performance at scale, making this solution ideal for MVPs with fewer than 1,000 document chunks per user.Mate329, the solution's creator, emphasizes that this isn't a replacement for dedicated vector databases but rather a strategic starting point that allows teams to validate ideas before migrating to more robust architectures.