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

# dingo vs pgvector

Neutral, constraint-first comparison with live GitHub stats.

| | [dingo](/tools/dingodb-dingo.md) | [pgvector](/tools/pgvector-pgvector.md) |
| --- | --- | --- |
| Tagline | A multi-modal vector database that supports upserts and vector queries using unified SQL on structured and unstructured data | Open-source vector similarity search for Postgres |
| Stars | 1,701 | 22,112 |
| Forks | 264 | 1,233 |
| Open issues | 8 | 14 |
| Language | Java | C |
| Adopt for | Dingo is a multi-modal vector database adept at serving both structured and unstructured data, featuring MySQL compatibility, high concurrency, low latency, and real-time scalar-vector hybrid retrieval. It supports SQL,悠 | <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, |
| Persona | - | - |
| Runtime | - | - |
| License | Dingo is licensed under Apache-2.0 | Other |
| Categories | Vector Databases, Data & Retrieval | Vector Databases |

## Trust and health

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

| | [dingo](/tools/dingodb-dingo.md) | [pgvector](/tools/pgvector-pgvector.md) |
| --- | --- | --- |
| Maintenance | Steady (60%) | Very active (96%) |
| Days since push | 43d | 0d |
| Open issues (now) | 8 | 14 |
| Security scan | Not scanned | No lockfile |
| Full report | [trust report](/tools/dingodb-dingo/trust.md) | [trust report](/tools/pgvector-pgvector/trust.md) |

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

Both Dingo and pgVector extend databases with vector search capabilities, although Dingo focuses specifically on a multi-modal database system with SQL support.

## Decision facts: dingo

- **Requirements:** Min 8 GB RAM; Requires Docker
- **Adopt for:** Dingo is a multi-modal vector database adept at serving both structured and unstructured data, featuring MySQL compatibility, high concurrency, low latency, and real-time scalar-vector hybrid retrieval. It supports SQL,悠
- **License detail:** Dingo is licensed under Apache-2.0

## 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,

## Choose when

### Choose dingo if…

- dingo is primarily Java; pgvector is C.
- License: dingo is Apache-2.0, pgvector is Other.
- Requirements: Min 8 GB RAM; Requires Docker.
- Both Dingo and pgVector extend databases with vector search capabilities, although Dingo focuses specifically on a multi-modal database system with SQL support.
- Tags unique to dingo: key-value-distributed-store, unified-sql, real-time-semantic-search, embedding-search.
- Also covers Data & Retrieval.
- - When you require efficient handling of mixed structured and unstructured data within an enterprise-grade solution with high availability.

### Choose pgvector if…

- pgvector is primarily C; dingo is Java.
- License: pgvector is Other, dingo is Apache-2.0.
- Both Dingo and pgVector extend databases with vector search capabilities, although Dingo focuses specifically on a multi-modal database system with SQL support.
- 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 NOT to use dingo

- - If you are looking for a solution that does not offer MySQL compatibility or SQL support for vector databases.
- - When your application’s requirements do not include hybrid scalar-vector retrievals and real-time index optimization capabilities.
- - If your project specifically excludes Java-based solutions or does not benefit from the automatic elastic sharding Dingo provides.

## 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.

## Common questions

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

dingo: A multi-modal vector database that supports upserts and vector queries using unified SQL on structured and unstructured data. pgvector: Open-source vector similarity search for Postgres. See the comparison table for live GitHub stats and shared categories.

### When should I choose dingo over pgvector?

Choose dingo over pgvector when dingo is primarily Java; pgvector is C; License: dingo is Apache-2.0, pgvector is Other; Requirements: Min 8 GB RAM; Requires Docker; Both Dingo and pgVector extend databases with vector search capabilities, although Dingo focuses specifically on a multi-modal database system with SQL support; Tags unique to dingo: key-value-distributed-store, unified-sql, real-time-semantic-search, embedding-search; Also covers Data & Retrieval; - When you require efficient handling of mixed structured and unstructured data within an enterprise-grade solution with high availability.

### When should I choose pgvector over dingo?

Choose pgvector over dingo when pgvector is primarily C; dingo is Java; License: pgvector is Other, dingo is Apache-2.0; Both Dingo and pgVector extend databases with vector search capabilities, although Dingo focuses specifically on a multi-modal database system with SQL support; 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 avoid dingo?

- If you are looking for a solution that does not offer MySQL compatibility or SQL support for vector databases. - When your application’s requirements do not include hybrid scalar-vector retrievals and real-time index optimization capabilities. - If your project specifically excludes Java-based solutions or does not benefit from the automatic elastic sharding Dingo provides.

### 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.

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

pgvector has more GitHub stars (22,112 vs 1,701). Stars measure visibility, not whether either tool fits your constraints.

### Are dingo and pgvector open source?

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

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

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

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

dingo: Steady. pgvector: 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 dingo and pgvector?

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

---

**Machine-readable endpoints**

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