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 solutions for vector data storage, indexing and retrieval, MongoDB Atlas and Elasticsearch. We’ll implement semantic search for MongoDB and Elasticsearch, laying the groundwork for robust RAG pipelines, and provide performance guidance for semantic search on MongoDB Atlas.
Join MongoDB Staff Developer Advocate Richmond Alake for this important webinar where you'll learn:
Common search patterns in AI workloads with concrete examples and detailed performance guidance for MongoDB Atlas Vector Search.
Why database architecture matters in generative AI, illustrated through real-world implementation scenarios that showcase developer productivity and application capabilities.
How to implement MongoDB Atlas Vector Search step-by-step, with live coding demonstrations that show how to enable semantic search for powerful RAG solutions through a unified developer experience.
Practical walkthrough of building complete RAG pipelines that integrate real-time data, with working code examples you can adapt for your own dynamic LLM-driven applications.
Actionable best practices for optimizing MongoDB Atlas for AI workloads, including live demonstrations of index configuration, efficient query patterns, and practical scaling strategies.
Whether you're building AI chatbots, recommendation engines, or advanced agentic systems, understanding how your database powers LLM-enabled applications is essential. By the end of this webinar, you'll have practical implementation knowledge and working code examples to architect your AI solutions using MongoDB Atlas for vector search and beyond.
