The vector database market exploded from $2.2 billion in 2024 with promises of revolutionizing AI applications, yet the reality reveals a stark gap between marketing
Category: Cloud
Vector Databases Part 7: Production Deployment Patterns and Operations
Moving vector databases from development to production requires addressing challenges that prototype implementations ignore including high availability, disaster recovery, cost optimization, and operational monitoring. Production
Vector Databases Part 6: GraphRAG Architecture and Knowledge Graphs
Traditional RAG systems excel at finding semantically similar documents but fail catastrophically when queries require connecting information across multiple sources or understanding dataset-wide themes. A
Vector Databases Part 5: Advanced Optimization and Reranking Strategies
The gap between acceptable and exceptional RAG performance often comes down to optimization decisions made after basic implementation. Production systems require careful tuning of reranking
Vector Databases: From Hype to Production Reality – Part 4: Building RAG Applications on Azure
Theory becomes reality in this part. We move from understanding vector databases and comparing options to actually building a production-ready Retrieval Augmented Generation application on
Vector Databases: From Hype to Production Reality – Part 3: The Vector Database Landscape
With the technical foundations established in Parts 1 and 2, we now face the practical question every development team encounters: which vector database should we
Vector Databases: From Hype to Production Reality – Part 2: Architecture and Indexing Algorithms
In Part 1, we explored what vector databases are and why they have become essential infrastructure for AI applications. Now we dive into the technical
Vector Databases: From Hype to Production Reality – Part 1: Understanding Vector Databases
The artificial intelligence landscape has undergone a seismic shift in recent years, and at the center of this transformation lies a technology that most developers
Database Integration: MCP for Azure PostgreSQL and pgvector
Throughout this series, we have explored Model Context Protocol fundamentals, Azure MCP Server capabilities, custom server development, and multi-agent orchestration. Now we focus on one
Azure AI Agent Service with Model Context Protocol
In the previous posts, we explored the Model Context Protocol fundamentals, examined Azure MCP Server’s capabilities, and built custom MCP servers on Azure. Now we
