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Similarity search does not become mathematically less accurate simply because a collection grows. What changes is the cost of finding neighbors—and, when a system switches to approximate search or spreads work across indexes and machines, the chance of missing useful candidates. The harder question is whether the results remain relevant to the task while meeting its latency, memory, update, and cost limits.
What does “scale” change in similarity search?
A similarity score ranks items according to a representation and a scoring rule. It does not prove that the top-ranked item answers a question, fits a recommendation, or is relevant to a person. A system can retrieve the mathematically nearest vectors and still return the wrong result if the embedding or scoring rule does not capture the task’s notion of relevance.
Scale also means more than adding vectors. A workload may grow in dimensionality, query rate, update frequency, target recall, latency requirements, or number of shards. These pressures interact: for example, increasing the requested recall can require more search work, while frequent writes can compete with search for index resources.
- More vectors: an exact scan has more candidates to score, and an index must represent a larger collection.
- More dimensions or different data characteristics: distance calculations and nearest-neighbor difficulty depend on dimensionality and sparsity as well as database size. He, Kumar, and Chang proposed relative contrast as a measure that considers these properties together in their ICML 2012 work on nearest-neighbor search difficulty.
- More traffic or stricter service targets: searches must meet throughput or latency goals without using unlimited memory and compute.
- More writes or shards: index maintenance and coordination can become as important as the distance calculations for an individual query.
Why not score every vector exactly?
Brute-force search scores every candidate and returns the true nearest neighbors under the chosen representation and metric. That makes it the useful exact baseline for measuring an approximate index. Its drawback is that the work grows with the candidate set, so it can become too costly for very large collections or tight query budgets. Google’s retrieval guide describes precomputed candidate lists and approximate nearest neighbors (ANN) as ways to make large-scale retrieval more efficient.
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Exact search is not a guarantee of semantic relevance; it is a guarantee about the ranking under the chosen vectors and scoring rule. ANN adds a separate possibility of error: it may fail to return some of those exact nearest neighbors in exchange for doing less work or making comparisons cheaper.
What does approximate search trade away?
ANN recall measures how many of the exact nearest neighbors are recovered, using exact search as ground truth. It measures agreement with that mathematical baseline—not whether the embedding itself reflects user relevance. NVIDIA’s cuVS documentation captures the central systems tradeoff: “Higher recall usually costs more build time, more search time, more memory, or some combination of all three.”
| Approach | Typical operating profile described by NVIDIA | What to weigh |
|---|---|---|
| HNSW graph index | Fast CPU search; high memory use; graph construction can be expensive. | Useful when query speed and recall matter, if the memory footprint and index-build cost fit the workload. |
| IVF partitioning | Searches selected partitions rather than the entire collection. | Partition selection reduces search work, but an unvisited partition can contain a true neighbor. |
| Compressed vectors or representations | Reduce memory use at some recall cost. | Consider whether the memory savings justify the loss in fidelity for the target recall. |
| Disk-backed Vamana/DiskANN | Supports search when the corpus cannot comfortably remain in memory. | Changes the memory assumption; evaluate it against the workload’s latency and throughput requirements. |
These are broad index-family profiles, not guarantees for every implementation or workload. NVIDIA’s vector-search guide treats target recall, latency, memory, build time, dataset size, dimensionality, and deployment environment as inputs to choosing an approach. It also describes GPU graph construction and search: a GPU may help with some search or build workloads, particularly for large datasets where high recall matters, while its complexity may not be justified for a tiny dataset.
Why can updates and sharding hurt performance?
At scale, query scoring is only one part of the system. Building or rebuilding an index consumes resources, and a design optimized for reads may behave differently when writes arrive concurrently. Distributing a collection across shards can also make a high-recall query visit many independent indexes, reducing throughput.
The authors of the 2025 HAKES paper identify graph-index build overhead, contention under concurrent read/write workloads, and lower throughput when high-recall queries fan out to many shards as limitations in the context they studied. These are reported system-design challenges, not a diagnosis that applies to every vector database. HAKES proposes a filter-and-refine design using compressed candidates followed by full-precision reranking; that is a research design, not a universal fix. See Hu et al., “HAKES: Scalable Vector Database for Embedding Search Service,” PVLDB 18(9), 2025.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What do published scale figures actually show?
Benchmark numbers are conditional on dataset, configuration, workload, hardware, and metric. The NeurIPS’21 billion-scale ANN challenge evaluated recall at throughput thresholds and also considered cost- and power-normalized throughput. Its authors noted that many earlier evaluations focused on datasets of about one million points, while motivating embedding use cases could require billion-, trillion-, or larger-scale indexes. Those figures describe the challenge’s motivation and prior evaluation patterns; they are not a claim that every deployment has that size. See Simhadri et al., Proceedings of Machine Learning Research, 2022.
A 2026 Frontiers in Computer Science study of vector databases for embedding-based image retrieval tested lifecycle behavior from 100 to 10,000 vectors and extended its scale tests through 50,000 vectors. That is useful evidence about configuration sensitivity at those tested sizes, not a billion-vector benchmark.
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- In that study’s reported HNSW configuration, Qdrant reached Recall@5 of 0.94 at 50,000 vectors. The authors attributed the decline to their graph and search setup and reported that increasing
efto meet a 0.95 requirement increases latency. This is a configuration-specific result, not a general property of Qdrant. - In the same study and configuration, reported pgvector resident memory at 50,000 vectors was approximately 8 GB, compared with approximately 102 MB for the raw data of 512-dimensional floating-point vectors. The larger figure includes index and system overhead; it should not be read as a universal pgvector memory requirement.
How should you scale vector search without losing useful recall?
- Define the workload before choosing an index. Record vector count and dimensions, query distribution, filters, update rate, target recall, latency or throughput objective, hardware, and memory budget. A system that performs well for unfiltered read-only queries may not suit concurrent updates or a different filter pattern.
- Establish an exact baseline. For representative queries, use exact search to define ground-truth nearest neighbors and measure the ANN system’s recall using a stated Recall@K convention. Keep task relevance evaluation separate: recall against exact vector neighbors cannot validate the embedding’s quality.
- Select an ANN family to fit the constraint. Consider graph search when its recall and query-speed profile justify memory and construction costs; partitioning or compression when reducing search work or memory is important; and disk-backed designs when the corpus does not fit comfortably in memory. Evaluate GPU acceleration only where its expected search or build role warrants the deployment complexity.
- Tune against the actual target, not a single score. Compare recall alongside latency percentiles or throughput, memory footprint, index build and rebuild time, update behavior, and hardware or power cost. If raising a search parameter improves recall, measure its effect on latency and throughput as well.
- Test lifecycle and distribution behavior. Include the expected write concurrency and shard count in evaluation. Measure whether builds, writes, or shard fan-out change throughput and service targets rather than assuming a query-only benchmark predicts production behavior.
- Keep comparisons like-for-like. Hold dataset, dimensions, query distribution, filters, update rate, hardware, and target recall constant when comparing options. State whether results are exact or approximate, how ground truth and Recall@K are defined, and the workload and scale tested.
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