The Polyglot Data Layer: Architecting Databases for GenAI System s In traditional software engineering, the database is a place to store state. In GenAI system design, the database is something far more critical: it is the Long-Term Memory of the agent. When building Agentic AI or RAG (Retrieval-Augmented Generation) systems, a single database is rarely enough. You need a “Polyglot Persistence” strategy where different engines handle different aspects of the AI’s reasoning process. Here is how to choose the right data foundation for your GenAI stack. Press enter or click to view image in full size Generated by AI Why Database Choice Hits Differently in GenAI In a traditional web application, database selection is largely a structural decision. You look at your data model, your transaction requirements, and your scale, and you pick accordingly. PostgreSQL for most things, Redis if you need speed, MongoDB if your schema is genuinely unpredictable. When you are building a GenAI applicati...
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The AI Infrastructure Shift: Why Your API Gateway Isn’t Enough for LLMs Building a GenAI prototype is easy. Moving it to production is where the real engineering begins. As developers scale from a single OpenAI key to a multi-model architecture, they quickly realize that traditional API Gateways (like Kong, Apigee, or AWS API Gateway) are not designed for the unique “non-deterministic” nature of Large Language Models. This gap has led to the rise of the LLM Gateway . Press enter or click to view image in full size Generated by AI What is an LLM Gateway? An LLM Gateway is a specialized proxy layer that sits between your application and various AI providers (OpenAI, Anthropic, Azure, Bedrock, etc.). While a traditional API Gateway manages standard REST traffic, an LLM Gateway understands “AI-native” concepts like tokens, prompt injection, and model-specific error codes. LLM vs API Gateway: The Infrastructure Gap Most AI Teams Ignore Your API Gateway was built for a world where services r...