Optimizing Vector Search When RAM Costs Soar
Comparing On-Disk and In-Memory ANN Algorithms
1What is Vector Search?
Vector search is a critical component of AI infrastructure, powering use cases like RAG, semantic search, and context layers. At large scales, storing indexes in RAM can become prohibitively expensive.
2The Role of ANN Algorithms
Approximate Nearest Neighbor (ANN) algorithms accelerate vector search by providing shortcuts instead of scanning the entire dataset. This improves scalability and efficiency.
3Exact Search vs. Approximate Search
Exact search delivers the best results but suffers from latency and scalability issues. ANN algorithms trade off some result quality for speed and scalability.
4In-Memory ANN: HNSW
HNSW is a widely used algorithm in modern vector databases. It offers extremely fast search speeds with indexes stored in memory but becomes costly at large scales.
5On-Disk ANN: DiskANN
Algorithms like DiskANN reduce RAM consumption by storing data on disk. This lowers costs for large indexes but may increase search latency.
6Limitations of HNSW
HNSW becomes expensive when indexes grow too large for RAM. Moving data to disk can degrade performance since the algorithm isn't optimized for disk access.
7The SPANN Algorithm
SPANN is a disk-based ANN algorithm that groups vectors on disk while keeping centroids in RAM. This reduces RAM usage significantly.
8Balancing Disk and Memory
Both disk and memory-based algorithms can store data structures differently. However, each is optimized for a specific storage type.
9Choosing for Large-Scale Projects
For large-scale projects, balancing RAM costs and search latency is crucial. Disk-based algorithms offer cost savings with acceptable latency.
10Examples of Vector Databases
Modern vector databases like Qdrant, Milvus, and pgvector support various ANN algorithms, making it easier to choose the right one for specific use cases.
11Conclusion and Recommendations
Optimizing vector search plays a critical role in reducing costs for AI projects. Selecting the right ANN algorithm can make a significant difference in both performance and cost.
Original article: Towards Data Science — RAM Maliyetlerini Düşürmek İçin Vektör Arama Optimizasyonu