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Estimate OpenSearch vector-index memory from the vector count, method, dimensions, and representation, then account for replicas and shard placement. The result is an index-memory estimate—not a node-RAM recommendation. JVM heap, the k-NN native-memory limit, operating-system page cache, and other workloads all affect how much capacity a node needs.
What you need to estimate
Collect the configuration and deployment details that determine the estimate before choosing a formula:
- Vector count: Count documents carrying vectors in the index or shard allocation you are sizing. Keep the logical count separate from replica copies.
- Vector dimension: Use the dimension configured for the indexed vectors.
- Method and parameters: Record whether the index uses HNSW or IVF and the actual values for parameters such as
mornlist. - Representation: Identify float, half-float, byte, binary, scalar-quantized, or product-quantized storage. Their estimates are not interchangeable.
- Placement: Determine which shards and copies are allocated to each node. An index-wide total does not tell you the peak memory on any one node.
Estimate memory for the selected method
Float-vector HNSW
OpenSearch documents this estimate for default float-vector HNSW indexes:
bytes ≈ 1.1 × (4 × dimension + 8 × m) × number_of_vectors
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The four-byte term represents each float dimension; the graph-link term uses 8 × m, and the estimate applies a 1.1 multiplier. For one million 256-dimensional vectors with m=16, OpenSearch’s example gives approximately 1.267 GB. This is a formula estimate for index memory, not measured node capacity or a complete RAM requirement. See OpenSearch’s k-NN memory estimate.
IVF
For IVF, OpenSearch documents a different estimate:
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bytes ≈ 1.1 × ((4 × dimension × number_of_vectors) + (4 × nlist × dimension))
Its example for one million 256-dimensional vectors with nlist=128 is approximately 1.126 GB. Because the method changes the formula, do not use the HNSW calculation to size an IVF index. The documented estimate is described in OpenSearch’s approximate k-NN documentation.
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Quantized and compact representations
OpenSearch notes that default float vectors use four bytes per dimension and that quantization trades memory footprint against search accuracy. Its example estimates for one million 256-dimensional HNSW vectors at m=16 are:
| Representation | Documented estimate |
|---|---|
| 1-bit quantization | 0.176 GB |
| 2-bit quantization | 0.211 GB |
| 4-bit quantization | 0.282 GB |
| 7-bit quantization | 0.387 GB |
| Half-float | 0.656 GB |
| Byte vector | 0.39 GB |
Each figure is an OpenSearch documentation formula example for that same vector count, dimension, and HNSW parameter; these are not independent capacity benchmarks. Do not apply them to a different count or configuration without using the corresponding method and representation calculation. See OpenSearch’s vector quantization overview and its representation examples.
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Product quantization
Product quantization (PQ) adds code storage and code-table costs to graph overhead; its documented estimate also depends on the number of segments. OpenSearch’s example for one million vectors with dimension 256, hnsw_m=16, pq_m=32, pq_code_size=8, and 100 segments is approximately 0.215 GB. Since segment count is not generally known in advance, the documentation recommends using 300 as a default for the estimate. Use the full PQ formula and the settings for the index rather than treating the example as a general per-vector value: OpenSearch’s product-quantization guidance.
Count replicas and calculate the scope you need
OpenSearch states that a replica doubles the total vector count for an index. For a primary plus one replica, calculate the estimate for twice the logical vector count. If you already counted all copies, do not multiply by the replica factor again.
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- Calculate memory per vector using the selected method, representation, and actual parameters.
- Multiply by the number of vectors in the relevant primary data.
- Include replica copies if they are not already part of that vector count.
- Map the resulting index or shard totals to the nodes that host those copies; use the busiest node’s allocation when assessing per-node capacity.
Replica-aware totals and the HNSW example are described in OpenSearch’s approximate k-NN documentation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Translate index memory into node capacity
Native vector indexes are only one consumer of a node’s RAM. OpenSearch separates JVM heap from memory for native-library indexes; the k-NN circuit_breaker_limit controls the portion available to native indexes. The documented default is 50% of memory remaining after JVM allocation. As an example, OpenSearch says a 100 GB machine with a 32 GB JVM has a default k-NN limit of 34 GB. That is a configured limit, not a recommendation to assign all remaining RAM to vectors. See OpenSearch k-NN settings.
For memory-mapped Lucene vector data, leave RAM available for the operating system’s page cache. Node sizing must also account for other workloads, ingest activity, the selected engine and topology, and the latency and recall targets. The vector estimate alone cannot establish that a particular node size will work. OpenSearch’s performance guidance recommends testing because recall and performance depend on factors such as vector count, dimensions, and segments, and settings trade search quality, latency, and indexing time.
Validate the estimate on a representative index
- Index representative data. Use the intended vector dimensions, method, representation, shard layout, and relevant index settings.
- Check actual per-node k-NN usage. The k-NN statistics API reports
graph_memory_usageandgraph_memory_usage_percentage, as well as cache capacity, circuit-breaker, load, and eviction information. Follow the API documentation for the deployed version: OpenSearch k-NN stats API. - Test realistic load and placement. Expand the test to expected vector and replica counts, and exercise the actual shard distribution and query mix. Observe memory, cache evictions, latency, and recall rather than relying on the index-wide estimate alone.
- Separate cold and warm behavior. OpenSearch documents that initial queries can be slower while native indexes load, with later queries faster when the circuit breaker is not triggered. Its query-performance page describes memory-optimized search as available starting with OpenSearch 3.1 and documents an index-warming API. Check the deployed version and test both startup and steady-state behavior: OpenSearch vector-search query performance.
- Adjust deliberately. Change a small set of settings at a time, then recheck resource use, query latency, and recall against the workload’s requirements.
Compare options using workload results
There is no universal winner established by the documented formulas. Compare the candidate methods and representations on the same data and workload:
- Memory: Estimate with the matching formula, count replicas correctly, and inspect per-node use.
- Search quality: Measure recall on representative queries; quantization and algorithm parameters can change accuracy.
- Latency: Evaluate both warm and cold behavior under production-like concurrency.
- Indexing behavior: Include graph construction and quantizer training where applicable when testing ingest requirements.
- Operations: Monitor native-memory usage, cache loads and evictions, and circuit-breaker state.
OpenSearch’s performance-tuning guidance describes the trade-offs among recall, latency, and indexing time. The right configuration depends on the workload and required accuracy and latency balance.
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