Comparison
embedbase vs fastembed
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 fastembed if fastembed is a lightweight and efficient Python library for creating state-of-the-art embeddings.
Markdown twin · embedbase alternatives · fastembed alternatives
GraphCanon updated 4d
vs
Trust & integrity
| Signal | embedbase | fastembed |
|---|---|---|
| Maintenance | Dormant (632d since push) As of 4d · github_public_v1 | Very active (2d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 4d · github_public_v1 | Not a fork · Organization account As of 4d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- embedbase
- A dead-simple API to build LLM-powered apps
- fastembed
- Fast, Accurate, Lightweight Python library for creating state-of-the-art embeddings
Stars
- embedbase
- 523
- fastembed
- 3.2k
Forks
- embedbase
- 54
- fastembed
- 231
Open issues
- embedbase
- 35
- fastembed
- 111
Language
- embedbase
- TypeScript
- fastembed
- Python
Adopt for
- embedbase
- 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.
- fastembed
- Fastembed is a lightweight and efficient Python library for creating state-of-the-art embeddings.
Persona
- embedbase
- -
- fastembed
- -
Runtime
- embedbase
- -
- fastembed
- -
License
- embedbase
- MIT
- fastembed
- Apache-2.0 License
Last pushed
- embedbase
- Nov 27, 2024
- fastembed
- Aug 19, 2026
Categories
- embedbase
- Data & Retrieval, Vector Databases
- fastembed
- Data & Retrieval, Vector Databases
Trust and health
Maintenance
- embedbase
- Dormant (18%)
- fastembed
- Very active (96%)
Days since push
- embedbase
- 632d
- fastembed
- 2d
Open issues (now)
- embedbase
- 35
- fastembed
- 111
Stars delta
- embedbase
- -1 (30d)
- fastembed
- +55 (30d)
Open issues delta
- embedbase
- 0 (30d)
- fastembed
- -26 (30d)
Full report
- embedbase
- Trust report
- fastembed
- Trust report
Choose embedbase if…
- embedbase is primarily TypeScript; fastembed is Python.
- License: embedbase is MIT, fastembed is Apache-2.0.
- Tags unique to embedbase: ai, artificial-intelligence, chatgpt, machine-learning.
- * Use Embedbase if you require direct integration capabilities specifically designed for embeddings and vector databases, like pgvector or Supabase.
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.
Choose fastembed if…
- fastembed is primarily Python; embedbase is TypeScript.
- License: fastembed is Apache-2.0, embedbase is MIT.
- Requirements: Does not require Docker, making the setup straightforward for Python environments..
- Tags unique to fastembed: rag, retrieval-augmented-generation, vector-search.
- When you need to generate high-quality embeddings quickly in Python.
When NOT to use fastembed
- If your project is not using Python, as Fastembed does not offer support for other programming languages directly.
- In scenarios demanding heavy customization or fine-tuning at a lower level than what Fastembed provides out-of-the-box. Consider alternatives that may offer more flexibility.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (different-ai/embedbase) · observed Aug 22, 2026
- GitHub forks (different-ai/embedbase) · observed Aug 22, 2026
- Last push (different-ai/embedbase) · observed Nov 27, 2024
- License file (MIT) · observed Aug 22, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (qdrant/fastembed) · observed Aug 22, 2026
- GitHub forks (qdrant/fastembed) · observed Aug 22, 2026
- Last push (qdrant/fastembed) · observed Aug 19, 2026
- License file (Apache-2.0) · observed Aug 22, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: embedbase 523 · fastembed 3.2k (synced Aug 22, 2026).
Common questions
- What is the difference between embedbase and fastembed?
- embedbase: A dead-simple API to build LLM-powered apps. fastembed: Fast, Accurate, Lightweight Python library for creating state-of-the-art embeddings. See the comparison table for live GitHub stats and shared categories.
- When should I choose embedbase over fastembed?
- Choose embedbase over fastembed when embedbase is primarily TypeScript; fastembed is Python; License: embedbase is MIT, fastembed is Apache-2.0; Tags unique to embedbase: ai, artificial-intelligence, chatgpt, machine-learning; * Use Embedbase if you require direct integration capabilities specifically designed for embeddings and vector databases, like pgvector or Supabase.
- When should I choose fastembed over embedbase?
- Choose fastembed over embedbase when fastembed is primarily Python; embedbase is TypeScript; License: fastembed is Apache-2.0, embedbase is MIT; Requirements: Does not require Docker, making the setup straightforward for Python environments.; Tags unique to fastembed: rag, retrieval-augmented-generation, vector-search; When you need to generate high-quality embeddings quickly in Python.
- 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 fastembed?
- If your project is not using Python, as Fastembed does not offer support for other programming languages directly. In scenarios demanding heavy customization or fine-tuning at a lower level than what Fastembed provides out-of-the-box. Consider alternatives that may offer more flexibility.
- Is embedbase or fastembed more popular on GitHub?
- fastembed has more GitHub stars (3,158 vs 523). Stars measure visibility, not whether either tool fits your constraints.
- Are embedbase and fastembed open source?
- Yes - both are open-source projects on GitHub (embedbase: MIT, fastembed: Apache-2.0).
- Where can I find alternatives to embedbase or fastembed?
- GraphCanon lists graph-backed alternatives at embedbase alternatives and fastembed alternatives (embedbase markdown twin, fastembed markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, embedbase or fastembed?
- embedbase: Dormant. fastembed: 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 embedbase and fastembed?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: embedbase trust report; fastembed trust report.