Modern applications need real-time AI search that unifies vectors, text, and structured signals—with the flexibility to customize ranking, integrate multiple models, and run ML inference at scale. Vespa was built for exactly this.
Migrating from Elasticsearch is simpler than you think—and it unlocks a platform where hybrid retrieval, multi-phase ranking, and machine learning come together natively, without external workarounds.
Join Vespa.ai and implementation partner Searchplex for a practical session on how to make the move and scale with confidence.
What You’ll Learn:
- Why teams move to Vespa: A unified platform for hybrid search, advanced ranking, and in-cluster ML inference.
- The migration path: How to translate Elasticsearch schemas to Vespa, optimize ingestion pipelines, and configure access controls.
- What success looks like: Real-world improvements in relevance, performance, and flexibility for future ML-powered enhancements
Why attend
You’re hitting the limits of Elasticsearch performance or scalability
You’re exploring GenAI or hybrid retrieval strategies
You want to reduce infrastructure complexity and cost
You’re evaluating Vespa as a long-term search platform