GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
35views this month
Decision brief
vector-db-benchmark is a Python-based framework that focuses on benchmarking vector search engines critical for applications ranging from recommendation systems to semantic search.
Good fit when
- Use this tool when you need precisely measured performance metrics of vector databases, especially in environments where decision-making is driven by nuanced data comparisons and analysis.
- Consider leveraging it if your project involves developing or enhancing a recommendation system or another application requiring accurate semantic search capabilities.
Avoid when
- Avoid this tool if you are looking to benchmark non-vector database types, as its focus specifically lies on vector databases used in specialized scenarios like the ones mentioned.
- Do not use vector-db-benchmark when your project does not require deep analysis or comparison of vector search performance, as it might add unnecessary complexity.
Observed Jul 14, 2026 · Source: enrich:decision_facts
Verify the decision
Adoption
Package downloads where a registry match exists. GitHub stars (371) are secondary evidence.
- Docker Hub pulls (30d)
- 92,670·Docker Hub API·Sep 20, 2026
Maintenance and security
Full trust report- Maintenance
- Very active (1d since push)
- As of Sep 20, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 20, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Backing
Company context for Qdrant. Display-only - separate from trust and ranking.
- Company
- Qdrant·GitHub org profile·Jul 11, 2026
- Funding
- $28,000,000 (2024-01)·GraphCanon curated seed (public press)·Jul 11, 2026
- Commercial model
- Open core·GraphCanon curated seed·Jul 11, 2026
Install
pip install vector-db-benchmarkHow it fits your stack(1)
Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.
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
Provides tools and benchmarks to evaluate performance of various vector databases used in applications such as recommendation systems and semantic search.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Sep 20, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Sep 20, 2026
- Languages
- python
Source: github.language+pyproject.toml · Sep 20, 2026
Categories
Graph entities
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 20, 2026)
$ python run.py --helpSource link
Tags
README
vector db benchmark View results There are various vector search engines available, and each of them may offer a different set of features and efficiency. But how do we measure the performance? There is no clear definition and in a specific case you may worry about a specific thi...
For agents
This page has a .md twin and JSON over the API.