--- title: "embedbase vs lantern" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/different-ai-embedbase-vs-lanterndata-lantern" tools: ["different-ai-embedbase", "lanterndata-lantern"] --- # embedbase vs lantern *GraphCanon updated Aug 22, 2026* ## Verdict Pick embedbase if embedbase is a TypeScript-based API designed to facilitate the creation of Large Language Model (LLM) powered applications via integrations with embeddings and vector databases; pick lantern if lantern is an extension for PostgreSQL written in Rust, providing capabilities for approximate nearest neighbor search tailored for AI applications. [embedbase](https://docs.embedbase.xyz) reports 523 GitHub stars, 54 forks, and 35 open issues, last pushed Nov 27, 2024. [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 [embedbase's repository](https://github.com/different-ai/embedbase) and [lantern's repository](https://github.com/lanterndata/lantern). | | [embedbase](/tools/different-ai-embedbase.md) | [lantern](/tools/lanterndata-lantern.md) | | --- | --- | --- | | Tagline | A dead-simple API to build LLM-powered apps | PostgreSQL vector database extension for building AI applications | | Stars | 523 | 889 | | Forks | 54 | 67 | | Open issues | 35 | 42 | | Language | TypeScript | Rust | | Adopt for | Embedbase is a TypeScript-based API designed to facilitate the creation of Large Language Model (LLM) powered applications via integrations with embeddings and vector databases. | Lantern is an extension for PostgreSQL written in Rust, providing capabilities for approximate nearest neighbor search tailored for AI applications. | | Persona | - | - | | Runtime | - | - | | License | MIT | AGPL-3.0, allowing free use and modification but requiring derivative works to be open-sourced as well. | | Categories | Data & Retrieval, Vector Databases | Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [embedbase](/tools/different-ai-embedbase.md) | [lantern](/tools/lanterndata-lantern.md) | | --- | --- | --- | | Days since push | 632d | 618d | | Open issues (now) | 35 | 42 | | Stars delta | -1 (30d) | 0 (30d) | | Full report | [trust report](/tools/different-ai-embedbase/trust.md) | [trust report](/tools/lanterndata-lantern/trust.md) | ## Decision facts: embedbase - **Adopt for:** Embedbase is a TypeScript-based API designed to facilitate the creation of Large Language Model (LLM) powered applications via integrations with embeddings and vector databases. ## 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 embedbase if… - embedbase is primarily TypeScript; lantern is Rust. - License: embedbase is MIT, lantern is AGPL-3.0. - Tags unique to embedbase: artificial-intelligence, chatgpt, natural-language-processing, openai. - Also covers Data & Retrieval. - * Use Embedbase if you require direct integration capabilities specifically designed for embeddings and vector databases, like pgvector or Supabase. ### Choose lantern if… - lantern is primarily Rust; embedbase is TypeScript. - License: lantern is AGPL-3.0, embedbase is MIT. - Tags unique to lantern: ann, approximate-nearest-neighbor-search, data-science, hnsw. - When you need to perform vector database operations integrated with a PostgreSQL environment ## When NOT to use embedbase - * Avoid using Embedbase if your application's technology stack cannot effectively integrate TypeScript, as its primary language support is in this framework and not others like Python. - * Do not use it when you need extensive customization options for the vector database configurations beyond what pgvector or Supabase offers. ## 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 embedbase and lantern? embedbase: A dead-simple API to build LLM-powered apps. lantern: PostgreSQL vector database extension for building AI applications. See the comparison table for live GitHub stats and shared categories. ### When should I choose embedbase over lantern? Choose embedbase over lantern when embedbase is primarily TypeScript; lantern is Rust; License: embedbase is MIT, lantern is AGPL-3.0; Tags unique to embedbase: artificial-intelligence, chatgpt, natural-language-processing, openai; Also covers Data & Retrieval; * Use Embedbase if you require direct integration capabilities specifically designed for embeddings and vector databases, like pgvector or Supabase. ### When should I choose lantern over embedbase? Choose lantern over embedbase when lantern is primarily Rust; embedbase is TypeScript; License: lantern is AGPL-3.0, embedbase is MIT; Tags unique to lantern: ann, approximate-nearest-neighbor-search, data-science, hnsw; When you need to perform vector database operations integrated with a PostgreSQL environment. ### When should I avoid embedbase? * Avoid using Embedbase if your application's technology stack cannot effectively integrate TypeScript, as its primary language support is in this framework and not others like Python. * Do not use it when you need extensive customization options for the vector database configurations beyond what pgvector or Supabase offers. ### 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 embedbase or lantern more popular on GitHub? lantern has more GitHub stars (889 vs 523). Stars measure visibility, not whether either tool fits your constraints. ### Are embedbase and lantern open source? Yes - both are open-source projects on GitHub (embedbase: MIT, lantern: AGPL-3.0). ### Where can I find alternatives to embedbase or lantern? GraphCanon lists graph-backed alternatives at [embedbase alternatives](/tools/different-ai-embedbase/alternatives) and [lantern alternatives](/tools/lanterndata-lantern/alternatives) ([embedbase markdown twin](/tools/different-ai-embedbase/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/different-ai-embedbase-vs-lanterndata-lantern.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, embedbase or lantern? embedbase: Dormant. 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 embedbase and lantern? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [embedbase trust report](/tools/different-ai-embedbase/trust); [lantern trust report](/tools/lanterndata-lantern/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=different-ai-embedbase`](/api/graphcanon/graph?tool=different-ai-embedbase) - 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/_