Webinar

AI Database Comparison: MongoDB vs. PostgreSQL and pgvector

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February 20, 2025

12 P.M. ET.

Large language models (LLMs) are rapidly reshaping how we build AI solutions, from retrieval-augmented generation (RAG) to AI agents and agentic systems that continuously reason, reflect, and act on new data. The database technology you choose to power these applications can significantly impact the performance, scalability, and success of your AI application.

In this webinar, Staff Developer Advocate Richmond Alake will compare two vector search solutions — PostgreSQL with pgvector and MongoDB Atlas Vector Search — and guide you through selecting the right option for your AI workloads. Whether you’re a data engineer, AI architect, or developer, you’ll walk away with actionable insights on 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 RAG boosts LLM-based applications by integrating external data in real time and how semantic search and RAG can be implemented using both databases
  • How PostgreSQL/pgvector and MongoDB Atlas handle high-performance vector operations crucial for tasks like semantic search and recommendation
  • 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 and how to apply proven tuning techniques

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