AI Database Comparison: MongoDB Atlas vs. Elasticsearch
Large language models (LLMs) are revolutionizing AI application development, from retrieval-augmented generation (RAG) to intelligent agentic systems that dynamically reason, learn, and adapt. The AI ecosystem offers a range of technologies to build these solutions—but one of the most critical elements is the database.
In generative AI applications, your database directly impacts response latency, application performance, and output accuracy. This session compares two solutions for vector data storage, indexing, and retrieval: MongoDB Atlas and Elasticsearch. We break down how each handles semantic search, provide performance insights, and walk through best practices for implementing vector search in MongoDB Atlas.
Watch MongoDB Staff Developer Advocate Richmond Alake as he guides you through:
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 agentic systems, understanding how your database powers LLM-enabled applications is essential. Watch now to gain practical knowledge and working code examples that will help you design and optimize AI-powered solutions with MongoDB Atlas.
