Comparison
aquila vs fastembed
Verdict
Pick aquila if aquila is an efficient neural search engine designed for indexing latent vectors and JSON metadata while performing k-NN searches; pick fastembed if fastembed is a lightweight and efficient Python library for creating state-of-the-art embeddings.
Markdown twin · aquila alternatives · fastembed alternatives
GraphCanon updated 3d
vs
Trust & integrity
| Signal | aquila | fastembed |
|---|---|---|
| Maintenance | Dormant (817d since push) As of 3w · github_public_v1 | Very active (2d since push) As of 3d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 3d · 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
- aquila
- Efficient Neural Search Engine
- fastembed
- Fast, Accurate, Lightweight Python library for creating state-of-the-art embeddings
Stars
- aquila
- 379
- fastembed
- 3.2k
Forks
- aquila
- 26
- fastembed
- 231
Open issues
- aquila
- 13
- fastembed
- 111
Language
- aquila
- HTML
- fastembed
- Python
Adopt for
- aquila
- Aquila is an efficient neural search engine designed for indexing latent vectors and JSON metadata while performing k-NN searches.
- fastembed
- Fastembed is a lightweight and efficient Python library for creating state-of-the-art embeddings.
Persona
- aquila
- -
- fastembed
- -
Runtime
- aquila
- -
- fastembed
- -
License
- aquila
- -
- fastembed
- Apache-2.0 License
Last pushed
- aquila
- May 6, 2024
- fastembed
- Aug 19, 2026
Categories
- aquila
- Data & Retrieval, Vector Databases
- fastembed
- Data & Retrieval, Vector Databases
Trust and health
Maintenance
- aquila
- Dormant (18%)
- fastembed
- Very active (96%)
Days since push
- aquila
- 817d
- fastembed
- 2d
Open issues (now)
- aquila
- 13
- fastembed
- 111
Stars delta
- aquila
- Unknown
- fastembed
- +55 (30d)
Open issues delta
- aquila
- Unknown
- fastembed
- -26 (30d)
Full report
- aquila
- Trust report
- fastembed
- Trust report
Choose aquila if…
- aquila is primarily HTML; fastembed is Python.
- Tags unique to aquila: approximate-nearest-neighbor-search, embedding, faiss, feature-vectors.
- When deploying a solution that requires the processing of feature vectors in image or video search contexts, where efficiency in approximate nearest neighbor search is necessary
When NOT to use aquila
- If the development team lacks experience with Docker, as Aquila's setup heavily relies on Docker images to run locally or in a big data configuration
- In scenarios where strict control over metadata and vector indexing is required beyond what JSON and latent vectors can provide
Choose fastembed if…
- fastembed is primarily Python; aquila is HTML.
- Requirements: Does not require Docker, making the setup straightforward for Python environments..
- Tags unique to fastembed: embeddings, openai, rag, retrieval-augmented-generation.
- 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 (Aquila-Network/aquila) · observed Aug 2, 2026
- GitHub forks (Aquila-Network/aquila) · observed Aug 2, 2026
- Last push (Aquila-Network/aquila) · observed May 6, 2024
- License file (unknown) · observed Aug 2, 2026
- Decision facts (enrichment) · observed Jul 17, 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: aquila 379 · fastembed 3.2k (synced Aug 2, 2026).
Common questions
- What is the difference between aquila and fastembed?
- aquila: Efficient Neural Search Engine. 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 aquila over fastembed?
- Choose aquila over fastembed when aquila is primarily HTML; fastembed is Python; Tags unique to aquila: approximate-nearest-neighbor-search, embedding, faiss, feature-vectors; When deploying a solution that requires the processing of feature vectors in image or video search contexts, where efficiency in approximate nearest neighbor search is necessary.
- When should I choose fastembed over aquila?
- Choose fastembed over aquila when fastembed is primarily Python; aquila is HTML; Requirements: Does not require Docker, making the setup straightforward for Python environments.; Tags unique to fastembed: embeddings, openai, rag, retrieval-augmented-generation; When you need to generate high-quality embeddings quickly in Python.
- When should I avoid aquila?
- If the development team lacks experience with Docker, as Aquila's setup heavily relies on Docker images to run locally or in a big data configuration In scenarios where strict control over metadata and vector indexing is required beyond what JSON and latent vectors can provide
- 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 aquila or fastembed more popular on GitHub?
- fastembed has more GitHub stars (3,158 vs 379). Stars measure visibility, not whether either tool fits your constraints.
- Are aquila and fastembed open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to aquila or fastembed?
- GraphCanon lists graph-backed alternatives at aquila alternatives and fastembed alternatives (aquila 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, aquila or fastembed?
- aquila: 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 aquila and fastembed?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: aquila trust report; fastembed trust report.