Large language models (LLMs) are rapidly reshaping how we build AI solutions. The database technology you choose to power these applications can significantly impact the performance, scalability, and success of your AI application.

In this webinar, Senior Staff Developer Advocate Anant Srivastava compares two vector search solutions—PostgreSQL with pgvector and MongoDB Atlas Vector Search—and guides you through selecting the right option for your AI workloads.

Whether you’re a data engineer, AI architect, or developer, you’ll understand how to think about and optimize critical metrics like latency and throughput to meet the demands of modern AI applications.

What you’ll learn:

How retrieval-augmented generation (RAG) boosts LLM-based applications by integrating external data in real time.
How PostgreSQL/pgvector and MongoDB Atlas handle high-performance vector operations for tasks like semantic search and recommendation engines.
How robust vector databases enable AI agents to reason, plan, and act autonomously, creating truly dynamic and interactive AI experiences.
How a real-world application using a financial Q&A dataset illustrates practical deployment and optimization strategies.
How key metrics like latency and throughput directly affect the success of LLM applications.