Building efficient vector search indexes and optimizing queries locally are foundational steps. However, transitioning these systems to handle real-world traffic and massive datasets introduces distinct operational and architectural challenges. When the number of vectors $N$ scales into the billions and query throughput ($QPS$) needs to remain high, the single-node approaches discussed earlier become insufficient.

This chapter focuses on the strategies and architectural patterns required to build and manage vector search systems that operate reliably and efficiently at production scale. You will learn about:

• Designing distributed system architectures for vector search.
• Implementing sharding techniques to partition large indexes across multiple machines.
• Ensuring high availability and fault tolerance through replication.
• Distributing query load effectively using load balancing.
• Monitoring key performance metrics specific to vector search systems.
• Managing index updates and maintenance in live environments.
• Considering cost implications and optimization strategies for large deployments.

By the end of this chapter, you will understand the engineering principles needed to architect vector search solutions capable of supporting demanding, large-scale LLM applications.