Why Booking.com switched their vector database for better scale

Booking.com switched its vector database to improve scale and reduce cost-per-query, according to a technical article on Production AI Infrastructure. The article advises teams to evaluate indexing speed, memory footprint, recall vs. latency, and horizontal scaling, and to use production data for benchmarks rather than synthetic sets. It also recommends a shadow-testing period during deployment to compare result sets and ensure the new database performs reliably under load.

Why Booking.com switched their vector database for better scale Most teams start with a simple managed service, but at a certain scale, you hit a wall where the cost-per-query becomes unsustainable or the indexing lag makes the search results irrelevant. For a travel platform, having a "stale" index means showing a hotel that's already fully booked, which kills the user experience. If you're building a real-world AI workflow, you need to look at these specific technical vectors during your evaluation: Indexing Speed: How fast can the system ingest new embeddings without locking the read queries? Memory Footprint: Does the database require the entire index to be in RAM, or does it support efficient disk-based storage? Recall vs. Latency: At 95% recall, what is the p99 latency? If that spikes during peak traffic, the system is useless. Horizontal Scaling: Can you add nodes without taking the cluster offline for a full re-index? For anyone looking for a practical tutorial on how to evaluate these systems, I recommend setting up a benchmark using your actual production data distributions rather than synthetic sets. Synthetic benchmarks are lying to you; they don't account for the "long tail" of queries that actually break a system in production. When implementing a new vector store, the deployment phase is where most people fail. You can't just swap the API endpoint. You need a shadow-testing period where you run the new database in parallel with the old one, comparing the result sets in real-time to ensure the new vector database isn't hallucinating nearest neighbors or missing obvious matches. If you are starting from scratch, avoid the temptation to over-engineer. Start with a solution that integrates with your existing Kubernetes stack to simplify the deployment. The goal isn't to have the "fastest" database in a vacuum, but the one that provides the most stable LLM agent performance under actual load. Focus on the observability tools—if you can't see why a specific query failed or why the latency spiked, you're flying blind. Production AI Infrastructure 7d ago /en/news/4926/ Next Big Tech spent trillions on AI but the ROI is still a ghost → /en/news/5982/