---
title: "vector-db-benchmark vs cherche"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/qdrant-vector-db-benchmark-vs-raphaelsty-cherche"
tools: ["qdrant-vector-db-benchmark", "raphaelsty-cherche"]
---

# vector-db-benchmark vs cherche

*GraphCanon updated Aug 23, 2026*

## Verdict

Pick vector-db-benchmark if 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; pick cherche if cherche is a Python library for implementing neural search capabilities.

[vector-db-benchmark](https://qdrant.tech/benchmarks/) reports 368 GitHub stars, 153 forks, and 35 open issues, last pushed Aug 21, 2026. [cherche](https://github.com/raphaelsty/cherche) has 332 stars, 14 forks, and 4 open issues, last pushed Jun 1, 2024. Figures are from public GitHub metadata via [vector-db-benchmark's repository](https://github.com/qdrant/vector-db-benchmark) and [cherche's repository](https://github.com/raphaelsty/cherche).

| | [vector-db-benchmark](/tools/qdrant-vector-db-benchmark.md) | [cherche](/tools/raphaelsty-cherche.md) |
| --- | --- | --- |
| Tagline | Framework for benchmarking vector search engines | Neural Search |
| Stars | 368 | 332 |
| Forks | 153 | 14 |
| Open issues | 35 | 4 |
| Language | Python | Python |
| Adopt for | 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. | Cherche is a Python library for implementing neural search capabilities. |
| Persona | - | - |
| Runtime | - | - |
| License | Apache-2.0 | MIT |
| Categories | Vector Databases | Data & Retrieval, Evaluation & Observability, Vector Databases |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [vector-db-benchmark](/tools/qdrant-vector-db-benchmark.md) | [cherche](/tools/raphaelsty-cherche.md) |
| --- | --- | --- |
| Maintenance | Very active (96%) | Dormant (18%) |
| Days since push | 1d | 812d |
| Open issues (now) | 35 | 4 |
| Open issues delta | -10 (30d) | 0 (30d) |
| Owner type | Organization | User |
| Full report | [trust report](/tools/qdrant-vector-db-benchmark/trust.md) | [trust report](/tools/raphaelsty-cherche/trust.md) |

## Shared compatibility

- **Python**: [vector-db-benchmark](/tools/qdrant-vector-db-benchmark.md) - Python runtime; [cherche](/tools/raphaelsty-cherche.md) - Python runtime

## Decision facts: vector-db-benchmark

- **Adopt for:** 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.

## Decision facts: cherche

- **Adopt for:** Cherche is a Python library for implementing neural search capabilities.

## Choose when

### Choose vector-db-benchmark if…

- License: vector-db-benchmark is Apache-2.0, cherche is MIT.
- Tags unique to vector-db-benchmark: benchmark, vector-database, vector-search, vector-search-engine.
- vector-db-benchmark ships Docker support for self-hosted deployment.
- 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.

### Choose cherche if…

- License: cherche is MIT, vector-db-benchmark is Apache-2.0.
- Tags unique to cherche: bm25, flashtext, information-retrieval, machine-learning.
- Also covers Data & Retrieval, Evaluation & Observability.
- Cherche is a Python library for implementing neural search capabilities.

## When NOT to use vector-db-benchmark

- 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.

## When NOT to use cherche

- Last GitHub push was 815 days ago (dormant maintenance, Jun 1, 2024). Validate activity before betting a new project on cherche.
- Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough.
- Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.

## Common questions

### What is the difference between vector-db-benchmark and cherche?

vector-db-benchmark: Framework for benchmarking vector search engines. cherche: Neural Search. See the comparison table for live GitHub stats and shared categories.

### When should I choose vector-db-benchmark over cherche?

Choose vector-db-benchmark over cherche when License: vector-db-benchmark is Apache-2.0, cherche is MIT; Tags unique to vector-db-benchmark: benchmark, vector-database, vector-search, vector-search-engine; vector-db-benchmark ships Docker support for self-hosted deployment; 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.

### When should I choose cherche over vector-db-benchmark?

Choose cherche over vector-db-benchmark when License: cherche is MIT, vector-db-benchmark is Apache-2.0; Tags unique to cherche: bm25, flashtext, information-retrieval, machine-learning; Also covers Data & Retrieval, Evaluation & Observability; Cherche is a Python library for implementing neural search capabilities.

### When should I avoid vector-db-benchmark?

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.

### When should I avoid cherche?

Last GitHub push was 815 days ago (dormant maintenance, Jun 1, 2024). Validate activity before betting a new project on cherche. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.

### Is vector-db-benchmark or cherche more popular on GitHub?

vector-db-benchmark has more GitHub stars (368 vs 332). Stars measure visibility, not whether either tool fits your constraints.

### Are vector-db-benchmark and cherche open source?

Yes - both are open-source projects on GitHub (vector-db-benchmark: Apache-2.0, cherche: MIT).

### Where can I find alternatives to vector-db-benchmark or cherche?

GraphCanon lists graph-backed alternatives at [vector-db-benchmark alternatives](/tools/qdrant-vector-db-benchmark/alternatives) and [cherche alternatives](/tools/raphaelsty-cherche/alternatives) ([vector-db-benchmark markdown twin](/tools/qdrant-vector-db-benchmark/alternatives.md), [cherche markdown twin](/tools/raphaelsty-cherche/alternatives.md)), ranked by typed relationship edges rather than popularity votes.

### Is there a machine-readable version of this comparison?

Yes. The markdown twin at [this comparison](/compare/qdrant-vector-db-benchmark-vs-raphaelsty-cherche.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, vector-db-benchmark or cherche?

vector-db-benchmark: Very active. cherche: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

### Where are the full trust reports for vector-db-benchmark and cherche?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [vector-db-benchmark trust report](/tools/qdrant-vector-db-benchmark/trust); [cherche trust report](/tools/raphaelsty-cherche/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=qdrant-vector-db-benchmark`](/api/graphcanon/graph?tool=qdrant-vector-db-benchmark)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_