# Why Booking.com switched their vector database for better scale > Source: > Published: 2026-08-12 03:29:29+00:00 # 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/)