# Optimizing Vector Search When RAM Costs Soar

Comparing On-Disk and In-Memory ANN Algorithms

- **Source:** [Towards Data Science](https://towardsdatascience.com)
- **Published:** 2026-07-25
- **Topic:** [Data & AI](https://brifing.si/c/data-ai)
- **Original article:** <https://towardsdatascience.com/optimizing-vector-search-on-disk-vs-in-memory-ann-indexes-when-ram-gets-too-expensive/>
- **On Brifing:** https://brifing.si/s/941adde3-9000-4deb-ac9e-c58441c4dfff

## What 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.

## The 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.

## Exact 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.

## In-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.

## On-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.

## Limitations 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.

## The SPANN Algorithm

SPANN is a disk-based ANN algorithm that groups vectors on disk while keeping centroids in RAM. This reduces RAM usage significantly.

## Balancing Disk and Memory

Both disk and memory-based algorithms can store data structures differently. However, each is optimized for a specific storage type.

## Choosing 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.

## Examples 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.

## Conclusion 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.

## Sources covering this story

- https://towardsdatascience.com/optimizing-vector-search-on-disk-vs-in-memory-ann-indexes-when-ram-gets-too-expensive/ — 

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_Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._