--- title: "embedbase vs what_are_embeddings" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/different-ai-embedbase-vs-veekaybee-what-are-embeddings" tools: ["different-ai-embedbase", "veekaybee-what-are-embeddings"] --- # embedbase vs what_are_embeddings *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 what_are_embeddings if focuses on educational materials for understanding embeddings in ML and NLP using Jupyter Notebooks. [embedbase](https://docs.embedbase.xyz) reports 523 GitHub stars, 54 forks, and 35 open issues, last pushed Nov 27, 2024. [what_are_embeddings](http://vickiboykis.com/what_are_embeddings/) has 1.1k stars, 86 forks, and 0 open issues, last pushed Jan 17, 2026. Figures are from public GitHub metadata via [embedbase's repository](https://github.com/different-ai/embedbase) and [what_are_embeddings's repository](https://github.com/veekaybee/what_are_embeddings). | | [embedbase](/tools/different-ai-embedbase.md) | [what_are_embeddings](/tools/veekaybee-what-are-embeddings.md) | | --- | --- | --- | | Tagline | A dead-simple API to build LLM-powered apps | A deep dive into embeddings starting from fundamentals | | Stars | 523 | 1,096 | | Forks | 54 | 86 | | Open issues | 35 | 0 | | Language | TypeScript | Jupyter Notebook | | 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. | Focuses on educational materials for understanding embeddings in ML and NLP using Jupyter Notebooks. | | Persona | - | - | | Runtime | - | - | | License | MIT | - | | Categories | Data & Retrieval, Vector Databases | Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [embedbase](/tools/different-ai-embedbase.md) | [what_are_embeddings](/tools/veekaybee-what-are-embeddings.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 632d | 217d | | Open issues (now) | 35 | 0 | | Stars delta | -1 (30d) | +4 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/different-ai-embedbase/trust.md) | [trust report](/tools/veekaybee-what-are-embeddings/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: what_are_embeddings - **Adopt for:** Focuses on educational materials for understanding embeddings in ML and NLP using Jupyter Notebooks. ## Choose when ### Choose embedbase if… - embedbase is primarily TypeScript; what_are_embeddings is Jupyter Notebook. - Tags unique to embedbase: ai, artificial-intelligence, chatgpt, machine-learning. - Also covers Vector Databases. - * Use Embedbase if you require direct integration capabilities specifically designed for embeddings and vector databases, like pgvector or Supabase. ### Choose what_are_embeddings if… - what_are_embeddings is primarily Jupyter Notebook; embedbase is TypeScript. - Tags unique to what_are_embeddings: machine-learning-algorithms, nlp-machine-learning. - When you are looking to gain foundational knowledge about how embeddings work in machine learning and natural language processing tasks. ## 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 what_are_embeddings - If you need practical, real-world application examples or code implementations not grounded in explanatory educational content. - When an advanced understanding of embeddings is required as this repository prioritizes fundamental comprehension over deep technical insights. ## Common questions ### What is the difference between embedbase and what_are_embeddings? embedbase: A dead-simple API to build LLM-powered apps. what_are_embeddings: A deep dive into embeddings starting from fundamentals. See the comparison table for live GitHub stats and shared categories. ### When should I choose embedbase over what_are_embeddings? Choose embedbase over what_are_embeddings when embedbase is primarily TypeScript; what_are_embeddings is Jupyter Notebook; Tags unique to embedbase: ai, artificial-intelligence, chatgpt, machine-learning; Also covers Vector Databases; * Use Embedbase if you require direct integration capabilities specifically designed for embeddings and vector databases, like pgvector or Supabase. ### When should I choose what_are_embeddings over embedbase? Choose what_are_embeddings over embedbase when what_are_embeddings is primarily Jupyter Notebook; embedbase is TypeScript; Tags unique to what_are_embeddings: machine-learning-algorithms, nlp-machine-learning; When you are looking to gain foundational knowledge about how embeddings work in machine learning and natural language processing tasks. ### 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 what_are_embeddings? If you need practical, real-world application examples or code implementations not grounded in explanatory educational content. When an advanced understanding of embeddings is required as this repository prioritizes fundamental comprehension over deep technical insights. ### Is embedbase or what_are_embeddings more popular on GitHub? what_are_embeddings has more GitHub stars (1,096 vs 523). Stars measure visibility, not whether either tool fits your constraints. ### Are embedbase and what_are_embeddings open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to embedbase or what_are_embeddings? GraphCanon lists graph-backed alternatives at [embedbase alternatives](/tools/different-ai-embedbase/alternatives) and [what_are_embeddings alternatives](/tools/veekaybee-what-are-embeddings/alternatives) ([embedbase markdown twin](/tools/different-ai-embedbase/alternatives.md), [what_are_embeddings markdown twin](/tools/veekaybee-what-are-embeddings/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-veekaybee-what-are-embeddings.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, embedbase or what_are_embeddings? embedbase: Dormant. what_are_embeddings: Slowing. 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 what_are_embeddings? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [embedbase trust report](/tools/different-ai-embedbase/trust); [what_are_embeddings trust report](/tools/veekaybee-what-are-embeddings/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/_