{"id":2846,"date":"2026-07-25T15:00:00","date_gmt":"2026-07-25T15:00:00","guid":{"rendered":"https:\/\/futurenews24.com\/index.php\/2026\/07\/25\/optimizing-vector-search-on-disk-vs-in-memory-ann-indexes-when-ram-gets-too-expensive\/"},"modified":"2026-07-25T22:59:06","modified_gmt":"2026-07-25T22:59:06","slug":"optimizing-vector-search-on-disk-vs-in-memory-ann-indexes-when-ram-gets-too-expensive","status":"publish","type":"post","link":"https:\/\/futurenews24.com\/index.php\/2026\/07\/25\/optimizing-vector-search-on-disk-vs-in-memory-ann-indexes-when-ram-gets-too-expensive\/","title":{"rendered":"The best way to Optimize Vector Search When RAM Will get Too Costly: On-Disk vs. In-Reminiscence ANN Indexes"},"content":{"rendered":"
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, vector search has change into a important piece of AI infrastructure, powering use instances from RAG and semantic search to agentic reminiscence and context layers. With the rise of agentic techniques, firms are attempting to offer as a lot context to the brokers as potential, which requires vector db indexes to develop from an preliminary million or dozens of tens of millions scale to the tons of of tens of millions and even billions. At this scale, storing indexes and related information in RAM will price hundreds of {dollars} monthly, and HNSW can change into a scalability bottleneck.<\/p>\n
On this article I wish to dive into the main points of what really makes semantic search quick and environment friendly: approximate nearest neighbor (ANN) algorithms, what completely different choices exist, and their trade-offs.<\/p>\n
Deep dive into vector DB<\/h2>\n
Vector databases include three major elements:<\/p>\n
embeddings \u2013 the numeric illustration of the corpus\u00a0<\/p>\n
search algorithm and index construction \u2013 the algorithm defines the search high quality and pace<\/p>\n
storage \u2013 how the info is saved (in reminiscence, on disk, payload along with embeddings, and so forth.). Whether or not in RAM or on disk, it determines the prices and latency at scale<\/p>\n
Embeddings are already properly outlined and mentioned in lots of articles, and this one will deal with search algorithms, and particularly ANN ones. There are typically two approaches for the search execution:<\/p>\n
Precise search \u2013 which demonstrates one of the best retrieval metrics though it doesn’t scale properly when it comes to latency\u00a0<\/p>\n
Approximate nearest neighbor (ANN) \u2013 which trades off the retrieval high quality for the latency and scalability.\u00a0<\/p>\n
The precise search is a straightforward strategy which loops via the entire entries within the index and calculates the space between your search question and current information. There aren’t any losses associated to any approximation or generalization with the trade-off of the latency and scalability. It\u2019s an amazing strategy for both very small indexes or experimentation, however typically not very appropriate for the manufacturing scale.\u00a0<\/p>\n
Fascinating truth: many trendy vector databases will let you bypass index constructing for small collections, falling again to kNN search as a result of the overhead of constructing an index shouldn’t be value it for just a few thousand vectors.<\/p>\n
The second choice is approximate nearest neighbor algorithms, which is a gaggle of algorithms with the primary goal of enhancing scalability by avoiding visiting all of the entries within the index. The thought right here is to offer some shortcuts to hurry up the search and ingestion. The implementations differ, though many trendy ANN algorithms (for instance, HNSW and DiskANN) depend on a graph construction to offer low question latency.<\/p>\n
Approximate nearest neighbor algorithms<\/h2>\n
It\u2019s necessary to debate that though ANN algorithms are all following an analogous idea to realize the aim of offering a brief path to the top end result, there are completely different implementations with completely different algorithms having distinctive units of trade-offs, which makes it essential to pick the one that matches your precise use case.\u00a0<\/p>\n
On this article I wish to deal with two completely different teams of the ANN algorithms:<\/p>\n
RAM-based \u2013 such algorithms are optimized for storing all or an enormous chunk of information in reminiscence, which offers extraordinarily low latency with the prices as a trade-off. The usual instance right here is HNSW (Hierarchical Navigable Small World)\u00a0<\/p>\n
On-disk \u2013 these algorithms are minimizing RAM utilization and relying closely on disk to load the required information. An instance right here is DiskANN or SPANN<\/p>\n
It\u2019s required to say that it\u2019s potential to retailer underlying information buildings of each of those algorithm teams both on disk or in reminiscence (at the very least partially), however they’re optimized for the precise storage sort and due to this fact will present one of the best outcomes using what they had been designed for.\u00a0<\/p>\n
In-memory ANN<\/h2>\n
HNSW (Hierarchical Navigable Small World)<\/h3>\nHNSW\u2019s layered graph: a question enters on the sparse high layer, greedily walks to the closest node, then drops down and repeats till the dense backside layer. Picture by creator.<\/figcaption><\/figure>\n
The most well-liked ANN algorithm utilized by virtually any trendy vector database. The thought is to make the most of a layered graph-based information construction to attach vectors with close to neighbors, which offers extraordinarily quick retrieval when storing the entire index in reminiscence. It\u2019s an amazing match for small-medium use instances and can present one of the best retrieval pace.<\/p>\n
Nonetheless, as soon as the index is sufficiently big that it now not suits in RAM or storing it in RAM turns into very costly, the choice is to both transfer information to disk, which can trigger a drastic efficiency hit, or extremely quantize it, which might trigger a big drop in retrieval high quality. The primary purpose for such a efficiency hit is that the HNSW construction shouldn’t be optimized for the clustered disk entry, and consequently, the search would produce a variety of non-sequential I\/O operations. Contemplating a number of hops per search and comparatively excessive learn site visitors, the disk I\/O will change into a bottleneck, which might considerably improve latency, from milliseconds to tons of of milliseconds or worse underneath heavy I\/O strain.<\/p>\n
The in-memory algorithms are extremely optimized to retailer all vectors and connections in RAM, which makes them extraordinarily quick, however with a trade-off of being reminiscence hungry. Furthermore, with on-disk choices such algorithms depend on random disk entry, which might change into a possible bottleneck for large-scale indexes (100 million+).<\/p>\n
This group of algorithms is designed particularly to interrupt the RAM consumption limitation of the in-memory ANN algorithms and scale back the storage prices whereas offering acceptable latency. It\u2019s an amazing selection if search latency shouldn’t be important and the index dimension is predicted to be giant. We will think about two major algorithms on this group:<\/p>\n
SPANN<\/h3>\nSPANN\u2019s routing layer: centroids keep in RAM, the vectors they characterize are saved on disk. Picture by creator.<\/figcaption><\/figure>\n
It\u2019s a disk-based ANN algorithm that follows the inverted-index (IVF) methodology: vectors are grouped into clusters, every represented by a centroid. It was particularly designed to deal with extraordinarily giant billion-vector+ indexes that received\u2019t slot in RAM or might be too costly to be saved in reminiscence. The thought is to arrange factors in clusters, which is a pure property of the embedding area, choose a centroid illustration of the cluster, and put it to use for the routing layer. Centroids and mainly the entire routing layer might be saved in RAM whereas the vectors represented by the centroids are saved on disk. The necessary element is that vectors represented by the identical centroid are saved on disk sequentially and due to this fact might be loaded from it quick and effectively. Throughout search, centroids are used to search out the closest teams of vectors, after which the vectors related to these centroids are loaded from disk to carry out a full scan.<\/p>\n
Notice: In comparison with HNSW with the on-disk storage choice, SPANN ensures that as a substitute of random disk entry, the vectors represented by the one centroid are grouped on disk and due to this fact loaded as blocks, which dramatically reduces the variety of required disk I\/O operations whereas offering acceptable latency.<\/p>\n
Instance vector databases: Turbopuffer (constructed on SPFresh, a SPANN successor), Chroma DB (cloud)<\/p>\n
DiskANN\u00a0<\/h3>\nDiskANN\u2019s format: quantized vectors and the graph are stored in RAM, the full-precision vector is learn from disk for every visited level. Picture by creator.<\/figcaption><\/figure>\n
As an alternative of a centroid-based strategy, DiskANN maintains a single-layer graph referred to as Vamana. The primary thought behind it’s to reduce the variety of hops required to search out the highest okay factors and due to this fact the variety of random disk entry operations. It\u2019s achieved by retaining some longer-range connections as a substitute of solely the closest ones, so fewer hops are wanted to achieve the goal. The unique vectors are saved on disk whereas the extremely quantized model of the vectors is saved in RAM, which additionally contributes to decreasing the required variety of disk entry operations. In comparison with SPANN, DiskANN\u2019s Vamana graph is constructed over each level, so the graph itself scales with the dataset, which is an actual reminiscence consideration at billion scale, the place SPANN solely wants its centroids resident. The info on disk shouldn’t be clustered, and the disk I\/O is minimized by the routing layer doing a minimal variety of hops to get to related vectors, resulting in extremely environment friendly search in apply. There are a variety of inner particulars on how precisely it\u2019s applied, and I extremely suggest exploring the origin paper, which is linked within the references for this text.<\/p>\n
Instance vector databases: Milvus, PostgreSQL (by way of pg_diskann)<\/p>\n
Economics<\/h2>\n
Notice: the costs beneath are approximate and present as of writing. Cloud pricing shifts over time and varies by supplier, area, and dedication, so deal with these figures as illustrative of the RAM-vs-disk ratio moderately than precise quotes<\/p>\n
With RAM costing about 5$ per GB via cloud suppliers, EBS is about 50 occasions cheaper, round 0.08-0.10$ per GB, and native NVMe SSD round 0.20-0.25$ per GB. Subsequently, for the 100,000,000 index in 1024 dimensions with float32 precision, it is going to be 1024 * 4 bytes = 4 KB per vector, and with production-grade replication of three it can require 12 KB of storage per vector.<\/p>\n
Subsequently, for 100,000,000 vectors, the overall required quantity of storage is 1.2 TB. In fact, there’s a quantization choice, which can scale back this quantity, and the most well-liked and least invasive scalar quantization would require 25% of the storage, which is 300 GB.<\/p>\n
Subsequently, the approximate month-to-month storage related prices:<\/p>\n
Non-quantized in RAM ~6000 USD\u00a0<\/p>\n
Scalar-quantized in RAM ~ 1500 USD<\/p>\n
Distant Disk ~120 USD<\/p>\n
Native Disk ~ 300 USD<\/p>\n
And since it is a linear relationship, the hole solely widens because the index grows towards the dimensions agentic techniques are pushing towards:<\/p>\nVectorsStorage (non-quantized)RAM price\/monthScalar-quantized RAM price\/monthRemote Disk price\/monthLocal Diskcost\/month100M1.2 TB~6,000~1,500~120~300500M6 TB~30,000~7,500~600~15001B12 TB~60,000~15,000~1,200~3000Desk 1: Approximate month-to-month storage prices, excluding compute, throughout index sizes and storage tiers. Picture by creator<\/figcaption><\/figure>\n
Consequently, though scalar quantization reduces the invoice considerably, it\u2019s nonetheless a excessive price in comparison with the on-disk choice.<\/p>\n
The trade-off<\/h2>\n
As with the whole lot in engineering, the associated fee discount offered by on-disk ANN algorithms shouldn’t be free. Whereas the routing layer does guarantee environment friendly information retrieval and narrows down the exploration to the smaller subset, the info nonetheless must be loaded from the disk, which is considerably slower than loading it from RAM. It\u2019s value mentioning that for lots of use instances it is probably not a deal breaker. Contemplating instances reminiscent of RAG, the place outcomes from the vector db are then handed to the reranker and LLM, the 100ms delay on the retrieval shouldn’t be going to be the primary bottleneck, however for instances reminiscent of agentic reminiscence, context, and so forth., it really could also be most popular to have the ability to execute search as quick as potential, particularly if there are a number of calls throughout a single agent request processing.\u00a0<\/p>\n
It\u2019s genuinely exhausting to offer a transparent quantity for on-disk latency, and that\u2019s kind of the purpose, it extremely depends upon the precise setup. For instance, the SPANN paper studies reaching 90% recall in round 1ms at billion scale, however that\u2019s a imply latency on a single machine with the index saved on native SSD. As soon as you progress to an actual deployment, the image adjustments. Turbopuffer\u2019s benchmark on a 10M-vector index reveals round 14ms at p50 when the index is heat on quick storage, however near 874ms when it\u2019s chilly and must be fetched from object storage, which is round a 60x distinction on the identical information simply from the cache state. Elements like {hardware} and whether or not the info is heat (already in cache) can every transfer the quantity by 10x or extra. Basic steerage is that disk-based ANN algorithms present slower latency than HNSW (in RAM) simply because RAM entry is far quicker.<\/p>\n
Select properly<\/h2>\n
With each on-disk and in-memory algorithms, it\u2019s necessary to make the proper selection about which one might be a greater match to your use case. Whereas HNSW offers a simple, well-rounded resolution for small and medium dimension indexes, it might be value exploring the on-disk choices as soon as your index grows greater and the related prices of storing vectors in RAM change into a burden. Furthermore, there are at all times edge instances like comparatively high-dimensional vectors for which RAM might be a bottleneck comparatively early or large low-dimensionality indexes which might make the most of RAM for for much longer. For engineers, it\u2019s necessary to concentrate on such use instances and make a complete choice on the trade-offs acceptable for his or her use instances.\u00a0<\/p>\nAlgorithmWhat stays in RAMLatencyScales to\u00a0Attain for it when\u00a0Precise searchFull vectors (or streamed from disk)\u00a0grows with N< ~10ktiny collections, ground-truth evalHNSWwhole index (disk potential, huge latency penalty)typically in sub 10ms~1M\u2013100M in RAMlatency-critical, index suits RAM budgetDiskANNPQ vectors + graph; graph grows with Nlow ms heat, sluggish when cold100M\u20131B+giant & cost-sensitiveSPANNcentroids solely\u00a0low ms heat, sluggish when cold100M\u2013multi billionseven PQ-in-RAM is simply too costlyDesk 2: Abstract of ANN algorithms by RAM footprint, latency, and sensible scale. Picture by creator.<\/figcaption><\/figure>\n
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