At its core, Vector Search & Management provide the critical link between vectorized data and machine learning models that can extract insights and guide decisions. But designing and implementing your vector storage, indexing, and retrieval strategy to match the requirements of your use case involves several considerations:
First, the update frequency of your vector embeddings. Your use case may warrant an architecture optimized for real-time streaming data or higher-latency batch processing.
Second, the access pattern you use to perform nearest neighbor search. Whether it’s responsive user queries or scheduled large batch analysis - your data pipelines and query patterns must be structured to match your access needs.
Third, your use case will require its own particular prioritization of latency, throughput, and accuracy. To optimise your application's objectives, you must balance latency, throughput, and accuracy concerns.
Tying your Vector Search & Management together with high-quality vector embeddings produces a modern, scalable retrieval stack. Rich vectors encode important relationships. Combined with performant Search & Management, enabling responsivity and large-scale analytics – a wealth of latent value.