Are Vector Databases Still Needed? RAG, AI Agents & Search

Learn more about Vector Databases here → https://ibm.biz/~aHY62ka9p Do AI applications still need vector databases? Martin Keen explores RAG, AI agents, embeddings, semantic search, and context windows to explain when vector databases matter and when simpler retrieval approaches work just as well. Learn how to choose the right retrieval architecture for your workload. 00:00 - Are Vector Databases Still Needed in the Age of AI Agents? 00:29 - What Vector Databases Actually Do: Embeddings & Semantic Search 01:15 - The Retrieval Pyramid: Choosing the Right Search Strategy 01:49 - 3 Reasons You Might Not Need a Vector Database 02:01 - Agentic Search vs Vector Search: Can Keyword Search Win? 03:08 - Do Long Context Windows Make Retrieval Obsolete? 03:33 - The Hidden Costs of Embeddings and Vector Indexes 05:27 - When Vector Search Outperforms Keyword Search 07:22 - Vector Index vs Vector Database: What's the Difference? 08:18 - How to Decide the Right Retrieval Approach for Your AI System AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://ibm.biz/~YCTMzkcZz AI was used in the creation of the transcript and metadata for this video. #vectordatabases #retrievalaugmentedgeneration #aiagents #vectorsearch #ai --------------------------------------------------------------------------------------------------------- Find us on YouTube: 🔵 IBM Technology: https://www.youtube.com/@IBMTechnology 🔵 IBM: https://www.youtube.com/@IBM 🔵 IBM Developer: https://youtube.com/@IBMDeveloperAdvocates