When developing generative and agentic AI solutions for the enterprise, it is crucial to choose the right data platform for operations like vector search and Retrieval-Augmented Generation (RAG) as it directly impacts performance, efficiency, and effectiveness. A well-designed AI data pipeline ensures optimal retrieval, accurate generation, and efficient processing, enabling scalability, flexibility, and customization. By selecting the right data platform, you can improve user experience, enhance system performance, and increase adoption, ultimately unlocking AI’s full potential and achieving desired outcomes.
Companies are comparing on-premises and cloud-based solutions for enterprise GenAI use cases like RAG. This study compares the Total Cost of Ownership (TCO) of deploying PostgreSQL pipelines supporting a modern AI workload including RAG between two data platform approaches:
- Sovereign, on-premises solution: Using EDB’s integrated platform, EDB Postgres AI on customer-owned hardware.
- Public cloud solution: A do-it-yourself (DIY) approach on AWS with managed services.
