From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-06.)
Qdrant 0.8.1 has 98 facts recorded in Dontopedia across 13 references, with 8 live disagreements.
Mostly:rdf:type(10), rdfs:label(8), has version(6)
hasSecurityFeaturehasHighestThroughputhasHigherUptimeThanhasHigherThroughputThanhasProgrammingLanguagecomparedTooutperformsrdfs:labelhasVersionhasCosthasSearchTimeOther 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.
hasMemberHas Member(8)ex:cost-orderingex:database-listex:databases-listex:five-librariesex:library-order-in-costex:performance-matrixex:vector-search-librariesex:vector-search-librariesincludesLibraryIncludes Library(4)ex:cloud-supporting-librariesex:encryption-equipped-librariesex:feature-equivalence-group-1ex:feature-matching-triadcomparedWithCompared With(3)ex:annoy-1.18.0ex:faiss-1.7.3ex:hnswlib-0.9.2hasLowerThroughputThanHas Lower Throughput Than(3)ex:annoy-1.18.0ex:faiss-1.7.3ex:hnswlib-0.9.2comparedToCompared to(2)ex:hnswlib-0.9.2ex:weaviate-1.14.0containsContains(2)ex:databases-to-compareex:vector-search-librariescontainsElementContains Element(2)ex:databasesex:databases-listincludesIncludes(2)ex:databases-to-compareex:vector-databasesandLibraryAnd Library(1)ex:cost-differenceappliesToApplies to(1)ex:general-community-supportassignedToAssigned to(1)ex:cost-value-110close-thirdClose Third(1)ex:performance-rankingcomparesEntityCompares Entity(1)ex:database-comparisoncontainsLibraryContains Library(1)matrix-datadescribesEntityDescribes Entity(1)ex:summary-texthasBetterSearchTimeThanHas Better Search Time Than(1)ex:milvus-2.3.0hasHighestUptimeHas Highest Uptime(1)ex:milvus-2.3.0hasMissingDataHas Missing Data(1)ex:databaseshasRowIndexHas Row Index(1)ex:matrixincludesDatabaseIncludes Database(1)ex:refined-comparison-matrixmentionsEntityMentions Entity(1)ex:search-time-comparisonsecond-lowestSecond Lowest(1)ex:cost-rankingunderperformsUnderperforms(1)ex:annoy-1.18.0The 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.
| Predicate | Value | Ref |
|---|---|---|
| Security Features Exact | Encryption, Access Control | [5] |
| Deployment Flexibility Exact | Cloud, On-Premises | [5] |
| Has Lowest Cost | true | [5] |
| Supports on Premises Deployment | true | [5] |
| Supports Cloud Deployment | true | [5] |
| Has Security Features | Encryption, Access Control | [5] |
| Has Deployment Flexibility | Cloud, On-Premises | [5] |
| Has Community Support | 0.9 | [8] |
| Has Ease of Integration | 0.9 | [8] |
| Has Uptime | 0.999 | [8] |
| Has Throughput | 1020 | [8] |
| Has Setup Quality | Setup and Configuration | [4] |
| Part of | Vector Search Libraries | [4] |
| Client Library Quality | multiple-available | [4] |
| Has Client Library | Multiple Client Libraries | [4] |
| Is Evaluated by | Evaluation Script | [10] |
| Ranks As | Top Performer | [6] |
| Implements | Security Implementation | [6] |
| Has Performance Profile | High Performance Profile | [6] |
| Has Highest Uptime | true | [6] |
| Has Best F1 Score | true | [6] |
| Has Best Precision | true | [6] |
| Has Best Recall | true | [6] |
| Has Second Best Indexing Time | True | [7] |
| Has Second Best Search Time | True | [7] |
| Has Better Indexing Time Than | Milvus 2.3.0 | [7] |
| Is Member of | Databases List | [7] |
| Has Indexing Time | 320 | [7] |
| Is Vector Database | true | [11] |
| Is Part of | Databases to Compare | [11] |
| Alternative to | Milvus 2.3.0 | [1] |
| Has Search Time Characteristic | slightly-slower | [1] |
| Has Lower Cost Than | Milvus 2.3.0 | [1] |
| Has Slower Search Time Than | Milvus 2.3.0 | [1] |
| Is Close Alternative to | Milvus 2.3.0 | [1] |
| Fastest Search | true | [3] |
| Best Search Time | true | [3] |
| Search Time Ms | 190 | [3] |
| Has Advantage | [2] | |
| Rank | 1 | [2] |
| Search Time Value | 190 | [2] |
| Belongs to | Vector Search Libraries | [2] |
| Has Fastest Search Time | true | [2] |
| Compared With | Weaviate 1.14.0 | [2] |
| Search Time Is | Performance Matrix | [2] |
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.
doc:beam/0e56e8f7-6bb5-47d4-bd16-a0b896835d01matrix.loc['Faiss 1.7.3', 'search_time'] = 200 matrix.loc['Annoy 1.18.0', 'search_time'] = 250 matrix.loc['Hnswlib 0.9.2', 'search_time'] = 220 matrix.loc['Qdrant 0.8.1', 'search_time'] = 190 matrix.loc['Weaviate 1.14.0', 'search_time'] = 2…
doc:beam/617fa408-66c0-4ffc-bb73-07d89c7fc9f3- **Community Support**: Active community and good documentation. ### 2. **Faiss 1.7.3** - **Programming Languages**: Primarily C++ with bindings for Python and Java. - **Client Libraries**: Strong support through Python bindings. - **Setu…
doc:beam/92df79b7-23d1-48bf-b715-dabb66f6c12bmatrix.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 …
doc:beam/f046bfd3-c03b-4abb-8935-1462ceeedfa6# Define the databases to compare databases = ['Milvus 2.3.0', 'Faiss 1.7.3', 'Annoy 1.18.0', 'Hnswlib 0.9.2', 'Qdrant 0.8.1', 'Weaviate 1.14.0'] # Define the performance metrics to compare metrics = [ 'search_time', 'indexing_time', '…
doc:beam/8d93ca4e-fed2-4c20-bf07-6ffa8a290e9fmatrix.loc['Faiss 1.7.3', 'throughput'] = 950 matrix.loc['Annoy 1.18.0', 'throughput'] = 900 matrix.loc['Hnswlib 0.9.2', 'throughput'] = 930 matrix.loc['Qdrant 0.8.1', 'throughput'] = 1020 matrix.loc['Weaviate 1.19.0', 'throughput'] = 980 …
doc:beam/6d659c29-d1a3-4424-91bd-3c71b2e411ec- Registers a microservice with the service discovery. - Starts and stops the microservice to simulate its operation. - Queries the service and retrieves the uptime percentage. This example provides a basic framework for understan…
doc:beam/ec280d12-a176-448c-83cf-6e81d66796f4databases = ['Milvus 2.3.0', 'Faiss 1.7.3', 'Annoy 1.18.0', 'Hnswlib 0.9.2', 'Qdrant 0.8.1', 'Weaviate 1.14.0'] # Define the performance metrics to evaluate metrics = ['search_time', 'index_size', 'query_latency'] # Evaluate each database…
doc:beam/281022af-d1fb-4d4d-9af4-f837536bcaeeBased on the current data, Sparse Retrieval appears to be the best choice due to its superior recall, precision, and f1_score, along with lower memory usage and storage size. However, further evaluation of other metrics such as scalability …
doc:beam/7ee070e6-2cfb-4b71-bea2-0c6ae37bc64bBy following these steps and using the provided example code, you can integrate Weaviate 1.19.0 into your existing system architecture to support 5,000 concurrent queries with 99.85% uptime. Ensure you monitor and scale your setup to mainta…
doc:beam/7fe8a152-f4b0-4ead-886d-12532ab7dcc38. **Ease of Integration**: How easy it is to integrate the database into your existing system. 9. **Community Support**: The level of community support and documentation available. 10. **Cost**: The financial cost associated with using the…
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