---
title: "pgvector vs vearch"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/pgvector-pgvector-vs-vearch-vearch"
tools: ["pgvector-pgvector", "vearch-vearch"]
---

# pgvector vs vearch

Neutral, constraint-first comparison with live GitHub stats.

| | [pgvector](/tools/pgvector-pgvector.md) | [vearch](/tools/vearch-vearch.md) |
| --- | --- | --- |
| Tagline | Open-source vector similarity search for Postgres | Distributed vector search for AI-native applications |
| Stars | 22,112 | 2,317 |
| Forks | 1,233 | 362 |
| Open issues | 14 | 170 |
| Language | C | Go |
| Adopt for | <ul><li><strong>Open-source vector similarity search:</strong> pgvector extends PostgreSQL for exact and approximate nearest neighbor search on various types of vectors.</li><li><strong>C-based library:</strong> Written, | Vearch is a cloud-native distributed vector database optimized for efficient similarity searches and scalar filtering in AI applications. |
| Persona | - | - |
| Runtime | - | - |
| License | Other | Apache-2.0 |
| Categories | Vector Databases | Data & Retrieval, Vector Databases |

## Trust and health

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

| | [pgvector](/tools/pgvector-pgvector.md) | [vearch](/tools/vearch-vearch.md) |
| --- | --- | --- |
| Days since push | 0d | 1d |
| Open issues (now) | 14 | 170 |
| Security scan | No lockfile | 16 low (16 low) |
| Full report | [trust report](/tools/pgvector-pgvector/trust.md) | [trust report](/tools/vearch-vearch/trust.md) |

**Typed relationship:** pgvector _(alternative)_ vearch

Both vearch and pgvector provide distributed and scalable vector similarity search functionalities, though they differ in their core technologies (PostgreSQL extension vs standalone solution).

## Shared compatibility

- **Python**: [pgvector](/tools/pgvector-pgvector.md) - Python runtime; [vearch](/tools/vearch-vearch.md) - Python runtime

## Decision facts: pgvector

- **Adopt for:** <ul><li><strong>Open-source vector similarity search:</strong> pgvector extends PostgreSQL for exact and approximate nearest neighbor search on various types of vectors.</li><li><strong>C-based library:</strong> Written,

## Decision facts: vearch

- **Adopt for:** Vearch is a cloud-native distributed vector database optimized for efficient similarity searches and scalar filtering in AI applications.

## Choose when

### Choose pgvector if…

- pgvector is primarily C; vearch is Go.
- License: pgvector is Other, vearch is Apache-2.0.
- Both vearch and pgvector provide distributed and scalable vector similarity search functionalities, though they differ in their core technologies (PostgreSQL extension vs standalone solution).
- Tags unique to pgvector: approximate-nearest-neighbor-search, nearest-neighbor-search.
- pgvector ships Docker support for self-hosted deployment.
- You prefer an open-source solution for integrating vector similarity search with existing Postgres databases, offering ACID compliance and robust data management features.

### Choose vearch if…

- vearch is primarily Go; pgvector is C.
- License: vearch is Apache-2.0, pgvector is Other.
- Both vearch and pgvector provide distributed and scalable vector similarity search functionalities, though they differ in their core technologies (PostgreSQL extension vs standalone solution).
- Tags unique to vearch: embeddings, cloud-native, retrieval-augmented-generation, ai-native-database.
- Also covers Data & Retrieval.
- - When you need a robust, scalable solution that supports both vector search and scalar filtering.

## When NOT to use pgvector

- You are working on Windows environments without access to C++ support tools required for native compilation; alternative installation methods like Docker might introduce additional setup complexity.
- Your project requires real-time search operations with extremely low latency that may not be fully satisfied by the PostgreSQL infrastructure underlying pgvector.

## When NOT to use vearch

- - Avoid Vearch if you require real-time search on unbounded datasets, given its focus on efficient similarity searches over pre-defined or limited datasets.
- - If your application primarily focuses on scalar data and rarely involves embedding vectors for similarity searches, another database solution might be more appropriate.

## Common questions

### What is the difference between pgvector and vearch?

pgvector: Open-source vector similarity search for Postgres. vearch: Distributed vector search for AI-native applications. See the comparison table for live GitHub stats and shared categories.

### When should I choose pgvector over vearch?

Choose pgvector over vearch when pgvector is primarily C; vearch is Go; License: pgvector is Other, vearch is Apache-2.0; Both vearch and pgvector provide distributed and scalable vector similarity search functionalities, though they differ in their core technologies (PostgreSQL extension vs standalone solution); Tags unique to pgvector: approximate-nearest-neighbor-search, nearest-neighbor-search; pgvector ships Docker support for self-hosted deployment; You prefer an open-source solution for integrating vector similarity search with existing Postgres databases, offering ACID compliance and robust data management features.

### When should I choose vearch over pgvector?

Choose vearch over pgvector when vearch is primarily Go; pgvector is C; License: vearch is Apache-2.0, pgvector is Other; Both vearch and pgvector provide distributed and scalable vector similarity search functionalities, though they differ in their core technologies (PostgreSQL extension vs standalone solution); Tags unique to vearch: embeddings, cloud-native, retrieval-augmented-generation, ai-native-database; Also covers Data & Retrieval; - When you need a robust, scalable solution that supports both vector search and scalar filtering.

### When should I avoid pgvector?

You are working on Windows environments without access to C++ support tools required for native compilation; alternative installation methods like Docker might introduce additional setup complexity. Your project requires real-time search operations with extremely low latency that may not be fully satisfied by the PostgreSQL infrastructure underlying pgvector.

### When should I avoid vearch?

- Avoid Vearch if you require real-time search on unbounded datasets, given its focus on efficient similarity searches over pre-defined or limited datasets. - If your application primarily focuses on scalar data and rarely involves embedding vectors for similarity searches, another database solution might be more appropriate.

### Is pgvector or vearch more popular on GitHub?

pgvector has more GitHub stars (22,112 vs 2,317). Stars measure visibility, not whether either tool fits your constraints.

### Are pgvector and vearch open source?

Yes - both are open-source projects on GitHub (pgvector: Other, vearch: Apache-2.0).

### Where can I find alternatives to pgvector or vearch?

GraphCanon lists graph-backed alternatives at /tools/pgvector-pgvector/alternatives and /tools/vearch-vearch/alternatives (/tools/pgvector-pgvector/alternatives.md, /tools/vearch-vearch/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 /compare/pgvector-pgvector-vs-vearch-vearch.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, pgvector or vearch?

pgvector: Very active. vearch: Very active. 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 pgvector and vearch?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: pgvector: /tools/pgvector-pgvector/trust; vearch: /tools/vearch-vearch/trust.

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=pgvector-pgvector`](/api/graphcanon/graph?tool=pgvector-pgvector)
- 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/_