Engineering Vector Databases at Scale

Build vector search systems that hold up in production.

This book gives engineers, architects, and technical leaders a practical blueprint for designing, building, and operating vector databases at scale. It moves from core retrieval concepts to distributed execution, showing how to turn embeddings into fast, reliable, and measurable search services for modern applications.

Through a full systems view, it covers the decisions that matter most, what to store, how to index it, how to shard it, how to merge results, and how to keep latency, recall, and cost under control. The chapters connect theory with implementation, so readers can understand not only what works, but why it works and when to choose one approach over another.

Inside, you will find guidance on:

• Vector representations, similarity metrics, and exact search baselines
• Embedding pipelines, normalization, updates, deletes, and schema design
• Approximate nearest neighbor methods, including graphs, trees, and quantization
• Compression techniques that reduce memory use while preserving retrieval quality
• Index build workflows, compaction, refresh, and integrity checks
• Sharding, replication, routing, and distributed top-k result merging
• Hybrid retrieval with metadata filters, candidate generation, and reranking
• Performance tuning for CPU, GPU, throughput, caching, and capacity planning
• Reliability, security, governance, audit logging, and deletion workflows

The later chapters focus on operational reality, scaling ingestion, handling skewed data, maintaining consistent rankings, and supporting updates without sacrificing service quality. You also get reference implementations and end-to-end examples that tie the concepts together into working systems.

Why this book stands out: it treats vector search as an engineering discipline, not just a model feature. That means clear tradeoffs, measurable outcomes, and a strong emphasis on production readiness. If you are building retrieval infrastructure for search, RAG, recommendation, or related AI products, this guide offers a grounded path from design to deployment.