--- title: "aquila vs fastembed" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aquila-network-aquila-vs-qdrant-fastembed" tools: ["aquila-network-aquila", "qdrant-fastembed"] --- # aquila vs fastembed *GraphCanon updated Aug 22, 2026* ## 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. [aquila](https://aquila.network) reports 379 GitHub stars, 26 forks, and 13 open issues, last pushed May 6, 2024. [fastembed](https://qdrant.github.io/fastembed/) has 3.2k stars, 231 forks, and 111 open issues, last pushed Aug 19, 2026. Figures are from public GitHub metadata via [aquila's repository](https://github.com/Aquila-Network/aquila) and [fastembed's repository](https://github.com/qdrant/fastembed). | | [aquila](/tools/aquila-network-aquila.md) | [fastembed](/tools/qdrant-fastembed.md) | | --- | --- | --- | | Tagline | Efficient Neural Search Engine | Fast, Accurate, Lightweight Python library for creating state-of-the-art embeddings | | Stars | 379 | 3,158 | | Forks | 26 | 231 | | Open issues | 13 | 111 | | Language | HTML | Python | | Adopt for | Aquila is an efficient neural search engine designed for indexing latent vectors and JSON metadata while performing k-NN searches. | Fastembed is a lightweight and efficient Python library for creating state-of-the-art embeddings. | | Persona | - | - | | Runtime | - | - | | License | - | Apache-2.0 License | | Categories | Data & Retrieval, Vector Databases | Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [aquila](/tools/aquila-network-aquila.md) | [fastembed](/tools/qdrant-fastembed.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 817d | 2d | | Open issues (now) | 13 | 111 | | Stars delta | Unknown | +55 (30d) | | Open issues delta | Unknown | -26 (30d) | | Full report | [trust report](/tools/aquila-network-aquila/trust.md) | [trust report](/tools/qdrant-fastembed/trust.md) | ## Decision facts: aquila - **Adopt for:** Aquila is an efficient neural search engine designed for indexing latent vectors and JSON metadata while performing k-NN searches. ## Decision facts: fastembed - **Requirements:** Does not require Docker, making the setup straightforward for Python environments. - **Adopt for:** Fastembed is a lightweight and efficient Python library for creating state-of-the-art embeddings. - **License detail:** Apache-2.0 License ## Choose when ### 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 ### 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 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 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. ## 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](/tools/aquila-network-aquila/alternatives) and [fastembed alternatives](/tools/qdrant-fastembed/alternatives) ([aquila markdown twin](/tools/aquila-network-aquila/alternatives.md), [fastembed markdown twin](/tools/qdrant-fastembed/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/aquila-network-aquila-vs-qdrant-fastembed.md) 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](/tools/aquila-network-aquila/trust); [fastembed trust report](/tools/qdrant-fastembed/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=aquila-network-aquila`](/api/graphcanon/graph?tool=aquila-network-aquila) - 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/_