Vector Database Performance Comparison
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)
Vector Database Performance Comparison has 20 facts recorded in Dontopedia across 8 references, with 3 live disagreements.
20 facts·5 predicates·8 sources·3 in dispute
Mostly:rdf:type(8), has metric(5), has unit(4)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (4)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
comparesDatabasesCompares Databases(1)
- Code Block 1
ex:code-block-1
describesDescribes(1)
- Summary
ex:summary
designedForDesigned for(1)
- Matrix
ex:matrix
is-designed-forIs Designed for(1)
- Benchmarking Script
ex:benchmarking-script
Other facts (19)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
19 facts
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Analysis Topic | [1] |
| Rdf:type | Task | [2] |
| Rdf:type | Dataset | [3] |
| Rdf:type | Performance Comparison | [4] |
| Rdf:type | Technical Analysis | [5] |
| Rdf:type | Data Matrix | [6] |
| Rdf:type | Comparison Document | [7] |
| Rdf:type | Performance Evaluation Task | [8] |
| Has Metric | Search Time | [4] |
| Has Metric | Cost | [6] |
| Has Metric | Community Support | [6] |
| Has Metric | Deployment Flexibility | [6] |
| Has Metric | Security Features | [6] |
| Has Unit | memory_usage_in_mb | [3] |
| Has Unit | storage_size_in_mb | [3] |
| Has Unit | rate_as_decimal | [3] |
| Has Unit | time_in_seconds | [3] |
| Has Purpose | Performance Evaluation | [4] |
| Topic | Vector Database Solutions | [7] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
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typebeam/ebc2fa71-57f7-42c2-94dc-697ba4990811
ex:AnalysisTopic
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typebeam/0da25b5e-237a-422f-96bc-668666933b81
ex:Task
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typebeam/1ff666a3-024a-43b9-a61b-238256feb9fd
ex:Dataset
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labelbeam/1ff666a3-024a-43b9-a61b-238256feb9fd
Vector Database Performance Comparison
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hasUnitbeam/1ff666a3-024a-43b9-a61b-238256feb9fd
memory_usage_in_mb
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hasUnitbeam/1ff666a3-024a-43b9-a61b-238256feb9fd
storage_size_in_mb
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hasUnitbeam/1ff666a3-024a-43b9-a61b-238256feb9fd
rate_as_decimal
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hasUnitbeam/1ff666a3-024a-43b9-a61b-238256feb9fd
time_in_seconds
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typebeam/4c0b780e-77bc-43f6-89c0-9fc02ba7ab53
ex:PerformanceComparison
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hasMetricbeam/4c0b780e-77bc-43f6-89c0-9fc02ba7ab53
ex:search-time
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hasPurposebeam/4c0b780e-77bc-43f6-89c0-9fc02ba7ab53
ex:performance-evaluation
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typebeam/92df79b7-23d1-48bf-b715-dabb66f6c12b
ex:TechnicalAnalysis
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typebeam/f81bd713-418c-4331-b01a-b394a1295f13
ex:DataMatrix
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hasMetricbeam/f81bd713-418c-4331-b01a-b394a1295f13
ex:cost
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hasMetricbeam/f81bd713-418c-4331-b01a-b394a1295f13
ex:community_support
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hasMetricbeam/f81bd713-418c-4331-b01a-b394a1295f13
ex:deployment_flexibility
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hasMetricbeam/f81bd713-418c-4331-b01a-b394a1295f13
ex:security_features
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typebeam/f82b7bb2-ccfc-486e-9a90-aa9d29f0fdaf
ex:ComparisonDocument
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topicbeam/f82b7bb2-ccfc-486e-9a90-aa9d29f0fdaf
ex:vector-database-solutions
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typebeam/5e937662-abc6-4623-b5b6-7b168728e324
ex:performance-evaluation-task
References (8)
8 references
ctx:claims/beam/ebc2fa71-57f7-42c2-94dc-697ba4990811ctx:claims/beam/0da25b5e-237a-422f-96bc-668666933b81- full textbeam-chunktext/plain1 KB
doc:beam/0da25b5e-237a-422f-96bc-668666933b81Show excerpt
matrix.loc['Qdrant 0.8.1', 'community_support'] = 0.9 matrix.loc['Weaviate 1.14.0', 'community_support'] = 0.85 matrix.loc['Milvus 2.3.0', 'cost'] = 100 matrix.loc['Faiss 1.7.3', 'cost'] = 120 matrix.loc['Annoy 1.18.0', 'cost'] = 150 matri…
ctx:claims/beam/1ff666a3-024a-43b9-a61b-238256feb9fd- full textbeam-chunktext/plain1 KB
doc:beam/1ff666a3-024a-43b9-a61b-238256feb9fdShow excerpt
matrix.loc['Weaviate 1.14.0', 'indexing_time'] = 360 matrix.loc['Milvus 2.3.0', 'memory_usage'] = 500 matrix.loc['Faiss 1.7.3', 'memory_usage'] = 550 matrix.loc['Annoy 1.18.0', 'memory_usage'] = 600 matrix.loc['Hnswlib 0.9.2', 'memory_usag…
ctx:claims/beam/4c0b780e-77bc-43f6-89c0-9fc02ba7ab53- full textbeam-chunktext/plain1 KB
doc:beam/4c0b780e-77bc-43f6-89c0-9fc02ba7ab53Show excerpt
matrix = pd.DataFrame(index=databases, columns=metrics) # Fill in the matrix with sample data matrix.loc['Milvus 2.3.0', 'search_time'] = 180 matrix.loc['Faiss 1.7.3', 'search_time'] = 200 matrix.loc['Annoy 1.18.0', 'search_time'] = 250 ma…
ctx:claims/beam/92df79b7-23d1-48bf-b715-dabb66f6c12b- full textbeam-chunktext/plain884 B
doc:beam/92df79b7-23d1-48bf-b715-dabb66f6c12bShow excerpt
matrix.loc['Qdrant 0.8.1', 'security_features'] = 'Encryption, Access Control' matrix.loc['Weaviate 1.14.0', 'security_features'] = 'Encryption, Access Control' print(matrix) ``` ### Summary and Recommendation After filling in the matrix …
ctx:claims/beam/f81bd713-418c-4331-b01a-b394a1295f13- full textbeam-chunktext/plain1 KB
doc:beam/f81bd713-418c-4331-b01a-b394a1295f13Show excerpt
matrix.loc['Annoy 1.18.0', 'community_support'] = 0.8 matrix.loc['Hnswlib 0.9.2', 'community_support'] = 0.85 matrix.loc['Qdrant 0.8.1', 'community_support'] = 0.9 matrix.loc['Weaviate 1.14.0', 'community_support'] = 0.85 matrix.loc['Milvu…
ctx:claims/beam/f82b7bb2-ccfc-486e-9a90-aa9d29f0fdafctx:claims/beam/5e937662-abc6-4623-b5b6-7b168728e324- full textbeam-chunktext/plain1 KB
doc:beam/5e937662-abc6-4623-b5b6-7b168728e324Show excerpt
print(f"Weaviate time: {weaviate_time} seconds") print(f"FAISS time: {faiss_time} seconds") ``` ->-> 5,10 [Turn 4941] Assistant: Certainly! To properly benchmark Weaviate and FAISS, you'll want to measure both the indexing time and the sea…
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