--- title: "awesome-vector-database vs lantern" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dangkhoasdc-awesome-vector-database-vs-lanterndata-lantern" tools: ["dangkhoasdc-awesome-vector-database", "lanterndata-lantern"] --- # awesome-vector-database vs lantern *GraphCanon updated Aug 23, 2026* ## Verdict Pick awesome-vector-database if a curated list of works on vector databases and high-dimensional structure searching without any implementation details; pick lantern if lantern is an extension for PostgreSQL written in Rust, providing capabilities for approximate nearest neighbor search tailored for AI applications. [awesome-vector-database](https://github.com/dangkhoasdc/awesome-vector-database) reports 359 GitHub stars, 31 forks, and 10 open issues, last pushed Jul 20, 2026. [lantern](https://lantern.dev) has 889 stars, 67 forks, and 42 open issues, last pushed Dec 12, 2024. Figures are from public GitHub metadata via [awesome-vector-database's repository](https://github.com/dangkhoasdc/awesome-vector-database) and [lantern's repository](https://github.com/lanterndata/lantern). | | [awesome-vector-database](/tools/dangkhoasdc-awesome-vector-database.md) | [lantern](/tools/lanterndata-lantern.md) | | --- | --- | --- | | Tagline | A curated list of works on high dimensional structure/vector search and databases | PostgreSQL vector database extension for building AI applications | | Stars | 359 | 889 | | Forks | 31 | 67 | | Open issues | 10 | 42 | | Language | - | Rust | | Adopt for | A curated list of works on vector databases and high-dimensional structure searching without any implementation details. | Lantern is an extension for PostgreSQL written in Rust, providing capabilities for approximate nearest neighbor search tailored for AI applications. | | Persona | - | - | | Runtime | - | - | | License | CC0-1.0 | AGPL-3.0, allowing free use and modification but requiring derivative works to be open-sourced as well. | | Categories | Vector Databases | Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-vector-database](/tools/dangkhoasdc-awesome-vector-database.md) | [lantern](/tools/lanterndata-lantern.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 33d | 618d | | Open issues (now) | 10 | 42 | | Stars delta | +4 (30d) | 0 (30d) | | Open issues delta | +4 (30d) | 0 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/dangkhoasdc-awesome-vector-database/trust.md) | [trust report](/tools/lanterndata-lantern/trust.md) | ## Decision facts: awesome-vector-database - **Adopt for:** A curated list of works on vector databases and high-dimensional structure searching without any implementation details. ## Decision facts: lantern - **Adopt for:** Lantern is an extension for PostgreSQL written in Rust, providing capabilities for approximate nearest neighbor search tailored for AI applications. - **License detail:** AGPL-3.0, allowing free use and modification but requiring derivative works to be open-sourced as well. ## Choose when ### Choose awesome-vector-database if… - License: awesome-vector-database is CC0-1.0, lantern is AGPL-3.0. - Tags unique to awesome-vector-database: embedding-similarity, embeddings-similarity, nearest-neighbor-search, search-engine. - If you require a comprehensive overview of vector database projects and research papers, as it aggregates information from various sources across the field. ### Choose lantern if… - License: lantern is AGPL-3.0, awesome-vector-database is CC0-1.0. - Tags unique to lantern: ai, ann, data-science, embeddings. - When you need to perform vector database operations integrated with a PostgreSQL environment ## When NOT to use awesome-vector-database - To find ready-to-use implementations or specific product releases; this repository serves more as a collection of references rather than real-world tools. - If you are looking for direct integration code snippets or detailed tutorials, since the tool is centered on listing and curating resources without delving into practical guides. ## When NOT to use lantern - Avoid when an open-source license like AGPL-3.0 might interfere with proprietary or closed-source projects - Not suitable if you require direct support for non-vector indexing operations outside ANN capabilities ## Common questions ### What is the difference between awesome-vector-database and lantern? awesome-vector-database: A curated list of works on high dimensional structure/vector search and databases. lantern: PostgreSQL vector database extension for building AI applications. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-vector-database over lantern? Choose awesome-vector-database over lantern when License: awesome-vector-database is CC0-1.0, lantern is AGPL-3.0; Tags unique to awesome-vector-database: embedding-similarity, embeddings-similarity, nearest-neighbor-search, search-engine; If you require a comprehensive overview of vector database projects and research papers, as it aggregates information from various sources across the field. ### When should I choose lantern over awesome-vector-database? Choose lantern over awesome-vector-database when License: lantern is AGPL-3.0, awesome-vector-database is CC0-1.0; Tags unique to lantern: ai, ann, data-science, embeddings; When you need to perform vector database operations integrated with a PostgreSQL environment. ### When should I avoid awesome-vector-database? To find ready-to-use implementations or specific product releases; this repository serves more as a collection of references rather than real-world tools. If you are looking for direct integration code snippets or detailed tutorials, since the tool is centered on listing and curating resources without delving into practical guides. ### When should I avoid lantern? Avoid when an open-source license like AGPL-3.0 might interfere with proprietary or closed-source projects Not suitable if you require direct support for non-vector indexing operations outside ANN capabilities ### Is awesome-vector-database or lantern more popular on GitHub? lantern has more GitHub stars (889 vs 359). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-vector-database and lantern open source? Yes - both are open-source projects on GitHub (awesome-vector-database: CC0-1.0, lantern: AGPL-3.0). ### Where can I find alternatives to awesome-vector-database or lantern? GraphCanon lists graph-backed alternatives at [awesome-vector-database alternatives](/tools/dangkhoasdc-awesome-vector-database/alternatives) and [lantern alternatives](/tools/lanterndata-lantern/alternatives) ([awesome-vector-database markdown twin](/tools/dangkhoasdc-awesome-vector-database/alternatives.md), [lantern markdown twin](/tools/lanterndata-lantern/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/dangkhoasdc-awesome-vector-database-vs-lanterndata-lantern.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-vector-database or lantern? awesome-vector-database: Steady. lantern: 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 awesome-vector-database and lantern? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-vector-database trust report](/tools/dangkhoasdc-awesome-vector-database/trust); [lantern trust report](/tools/lanterndata-lantern/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dangkhoasdc-awesome-vector-database`](/api/graphcanon/graph?tool=dangkhoasdc-awesome-vector-database) - 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/_