GraphCanon updated 1mo · GitHub synced 1mo · 35 views this month
Decision brief
VectorDBBench is a benchmark tool for evaluating vector databases written in Python under the MIT license.
Good fit when
- When you need a comprehensive performance analysis of vector database solutions using Python
- For assessing cost-effectiveness of various vector databases
Avoid when
- If your evaluation framework requires languages other than Python or licenses other than MIT
- When benchmarking non-vector type databases, as VectorDBBench is specialized for vector databases
Observed Jul 14, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Active (7d since push)
- As of 1mo
- Provenance
- Not a fork · Organization account
- As of 1mo
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Backing
Company context for Zilliz. Display-only - separate from trust and ranking.
- Company
- Zilliz·GitHub org profile·1mo
- Funding
- $60,000,000 (2022-08)·GraphCanon curated seed (public press)·1mo
- Commercial model
- Open core·GraphCanon curated seed·1mo
Install
pip install VectorDBBenchHow it fits your stack(13)
Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.
Integrates
Related
Relationship graph
Optional deeper exploration of typed edges and category neighbours.
Similar tools
Same-category neighbours not already linked as typed edges.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A benchmark tool written in Python to assess the performance and cost-effectiveness of various vector databases.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Jul 22, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Jul 22, 2026
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Jul 22, 2026
- Languages
- python
Source: github.language+pyproject.toml · Jul 22, 2026
Categories
Graph entities
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Jul 22, 2026)
pip install vectordb-benchSource link
Tags
README
Install
Install vectordb-bench with only PyMilvus
pip install vectordb-bench
Install the specific database client
pip install 'vectordb-bench[pinecone]'
All the database client supported
| Optional database client | install command |
|---|---|
| pymilvus, zilliz_cloud (default) | pip install vectordb-bench |
| qdrant | pip install vectordb-bench[qdrant] |
| pinecone | pip install vectordb-bench[pinecone] |
| weaviate | pip install vectordb-bench[weaviate] |
| elastic, aliyun_elasticsearch | pip install vectordb-bench[elastic] |
| pgvector, pgvectorscale, pgdiskann, alloydb, vectorchord | pip install vectordb-bench[pgvector] |
| pgvecto.rs | pip install vectordb-bench[pgvecto_rs] |
| redis | pip install vectordb-bench[redis] |
| memorydb | pip install vectordb-bench[memorydb] |
| chromadb | pip install vectordb-bench[chromadb] |
| cockroachdb | pip install vectordb-bench[cockroachdb] |
| awsopensearch | pip install vectordb-bench[opensearch] |
| aliyun_opensearch | pip install vectordb-bench[aliyun_opensearch] |
| mongodb | pip install vectordb-bench[mongodb] |
| tidb | pip install vectordb-bench[tidb] |
| vespa | pip install vectordb-bench[vespa] |
| oceanbase | pip install vectordb-bench[oceanbase] |
| hologres | pip install vectordb-bench[hologres] |
| tencent_es | pip install vectordb-bench[tencent_es] |
| alisql | pip install vectordb-bench[alisql] |
| polardb | pip install vectordb-bench[polardb] |
| doris | pip install vectordb-bench[doris] |
| zvec | pip install vectordb-bench[zvec] |
| endee | pip install vectordb-bench[endee] |
| lindorm | pip install vectordb-bench[lindorm] |
| volc_mysql | pip install vectordb-bench[volc_mysql] |
| adbpg | pip install vectordb-bench[adbpg] |
Install requirements
pip install -e '.[test]'
pip install -e '.[pinecone]'
Installation
The system under test can be installed in any form to achieve optimal performance. This includes but is not limited to binary deployment, Docker, and cloud services.
For agents
This page has a .md twin and JSON over the API.