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March 27, 2025

AI Database Comparison: MongoDB Atlas vs. Elasticsearch

12 P.M. ET Large language models (LLMs) are transforming how AI applications are built, from retrieval-augmented generation (RAG) to agentic systems that dynamically reason, learn, and adapt. And the AI ecosystem offers an array of technologies developers can leverage to build intelligent solutions. One of the most critical elements is the database. In generative AI applications, the database directly impacts response latency, application performance, and output accuracy. This webinar compares two NoSQL databases, MongoDB Atlas and Elasticsearch. We’ll implement semantic search, laying the groundwork for robust RAG pipelines, and compare how each solution compares in key metrics like latency and queries per second. Join MongoDB Staff Developer Advocate Richmond Alake for this important webinar where you'll learn: Common search patterns in AI Workloads and performance guidance. Why the database matters in generative AI and how it influences performance, speed, and accuracy. How MongoDB Atlas Vector Search and Elasticsearch handle semantic search to power RAG solutions. How to implement RAG pipelines that integrate real-time data enabling more dynamic LLM-driven applications. How MongoDB Atlas and Elasticsearch compare in latency, throughput, and scalability for AI workloads. Whether you’re building AI chatbots, recommendation engines, or advanced agentic systems, understanding how your database underpins LLM-enabled applications is essential. By the end of this webinar, you’ll be equipped to make informed decisions on how to architect your AI solutions using MongoDB Atlas for vector search and beyond.

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