How OpenTable Reinvented Restaurant Discovery with Qdrant - Qdrant
Sep 2, 2025Daniel Azoulai

Summary
This post details OpenTable's use of Qdrant for their Concierge AI dining assistant. It highlights Qdrant's strengths in handling sparse embeddings for scenarios requiring fine-grained filtering down to a single restaurant out of over 60,000. The article emphasizes Qdrant's predictable performance for combined metadata and review filtering, meeting latency budgets, and the ease of deployment with Qdrant Cloud. It also notes the operational stability of Qdrant in production, allowing the OpenTable team to focus on model performance and user experience.
More from the Filtered Vector Search trail
- How Lucy Built a Source-of-Truth Retrieval Layer for 2.8M+ SEC and DART Filings with Qdrant - Qdrant
- How Himalayas Built Semantic Job Matching for 100,000+ Remote Listings with Qdrant - Qdrant
- How to Tune Vector Search Without Guessing - Qdrant
- Pre-Filtering vs Post-Filtering (and Why Qdrant Does Neither) - Qdrant
- Filtered Vector Search: What ACORN Fixes, and What Fixes ACORN - Qdrant