From RAG to scale: Rethinking the vector database stack

Ajay Nair

GM, Elasticsearch

Elastic

Noel Yuhanna

Vice President, Principal Analyst

Forrester Research

Vector search may work fine in a prototype, but at production scale, the costs get harder to predict and the tuning gets harder to staff. Most teams end up managing a patchwork of tools: a vector database, an embedding pipeline, a reranker, and a dedicated infrastructure team to hold it all together.

This webinar breaks down the "stack tax" of a DIY solution with Ajay Nair, GM, Elasticsearch Platform, and guest speaker Noel Yuhanna, VP, principal analyst at Forrester Research. The webinar opens with Forrester's market view on how to evaluate vector databases, covering relevance quality, latency, and total cost of ownership at scale. From there, we'll move into a walkthrough showing how Elasticsearch Vector Database addresses those gaps directly.

You'll leave with a clearer framework for evaluating specialist vector stores against converged retrieval engines, plus the benchmark data to support that decision. Register now to save your seat.

Highlights

• Get Forrester's read on the vector database market's inflection point, including how specialist stores, general-purpose databases, and converged retrieval engines stack up against each other.
• See a walkthrough of Elasticsearch Vector Database, covering how features such as semantic_text and Elastic Inference Service remove the undifferentiated work of managing embedding pipelines.
• Review benchmark data on memory reduction and throughput, including compression and indexing results, to understand what production
• Learn Forrester's three-question framework for deciding when a specialist vector store fits and when a converged platform reduces migration risk.
• Hear a Q&A with Noel Yuhanna and Ajay Nair to get answers to your team's specific retrieval challenges.

Additional resources

• Elastic named a Leader in The Forrester Wave™: Cognitive Search Platforms, Q4 2025
• The most widely deployed, open source vector database