--- title: "tinyvector vs fastembed" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/m1guelpf-tinyvector-vs-qdrant-fastembed" tools: ["m1guelpf-tinyvector", "qdrant-fastembed"] --- # tinyvector vs fastembed *GraphCanon updated Aug 23, 2026* ## Verdict Pick tinyvector if lightweight Rust-based embedding storage for efficiency; pick fastembed if fastembed is a lightweight and efficient Python library for creating state-of-the-art embeddings. [tinyvector](https://crates.io/crates/tinyvector) reports 439 GitHub stars, 25 forks, and 8 open issues, last pushed Dec 28, 2023. [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 [tinyvector's repository](https://github.com/m1guelpf/tinyvector) and [fastembed's repository](https://github.com/qdrant/fastembed). | | [tinyvector](/tools/m1guelpf-tinyvector.md) | [fastembed](/tools/qdrant-fastembed.md) | | --- | --- | --- | | Tagline | A tiny embedding database in pure Rust. | Fast, Accurate, Lightweight Python library for creating state-of-the-art embeddings | | Stars | 439 | 3,158 | | Forks | 25 | 231 | | Open issues | 8 | 111 | | Language | Rust | Python | | Adopt for | Lightweight Rust-based embedding storage for efficiency. | Fastembed is a lightweight and efficient Python library for creating state-of-the-art embeddings. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 License | | Categories | Vector Databases | Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [tinyvector](/tools/m1guelpf-tinyvector.md) | [fastembed](/tools/qdrant-fastembed.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 969d | 2d | | Open issues (now) | 8 | 111 | | Stars delta | +3 (30d) | +55 (30d) | | Open issues delta | 0 (30d) | -26 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/m1guelpf-tinyvector/trust.md) | [trust report](/tools/qdrant-fastembed/trust.md) | ## Decision facts: tinyvector - **Adopt for:** Lightweight Rust-based embedding storage for efficiency. ## 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 tinyvector if… - tinyvector is primarily Rust; fastembed is Python. - License: tinyvector is MIT, fastembed is Apache-2.0. - Tags unique to tinyvector: machine-learning, rust, search-engines, similarity-search. - tinyvector ships Docker support for self-hosted deployment. - When developing applications requiring efficient similarity searches over embeddings that are written in Rust or integrate well with Rust systems. ### Choose fastembed if… - fastembed is primarily Python; tinyvector is Rust. - License: fastembed is Apache-2.0, tinyvector is MIT. - Requirements: Does not require Docker, making the setup straightforward for Python environments.. - Tags unique to fastembed: openai, rag, retrieval-augmented-generation, vector-search. - Also covers Data & Retrieval. - When you need to generate high-quality embeddings quickly in Python. ## When NOT to use tinyvector - For heavy-scale distributed vector operations as tinyvector is designed to be lightweight and might not scale as expected compared to larger solutions like Faiss or PQ. ## 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 tinyvector and fastembed? tinyvector: A tiny embedding database in pure Rust.. 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 tinyvector over fastembed? Choose tinyvector over fastembed when tinyvector is primarily Rust; fastembed is Python; License: tinyvector is MIT, fastembed is Apache-2.0; Tags unique to tinyvector: machine-learning, rust, search-engines, similarity-search; tinyvector ships Docker support for self-hosted deployment; When developing applications requiring efficient similarity searches over embeddings that are written in Rust or integrate well with Rust systems. ### When should I choose fastembed over tinyvector? Choose fastembed over tinyvector when fastembed is primarily Python; tinyvector is Rust; License: fastembed is Apache-2.0, tinyvector is MIT; Requirements: Does not require Docker, making the setup straightforward for Python environments.; Tags unique to fastembed: openai, rag, retrieval-augmented-generation, vector-search; Also covers Data & Retrieval; When you need to generate high-quality embeddings quickly in Python. ### When should I avoid tinyvector? For heavy-scale distributed vector operations as tinyvector is designed to be lightweight and might not scale as expected compared to larger solutions like Faiss or PQ. ### 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 tinyvector or fastembed more popular on GitHub? fastembed has more GitHub stars (3,158 vs 439). Stars measure visibility, not whether either tool fits your constraints. ### Are tinyvector and fastembed open source? Yes - both are open-source projects on GitHub (tinyvector: MIT, fastembed: Apache-2.0). ### Where can I find alternatives to tinyvector or fastembed? GraphCanon lists graph-backed alternatives at [tinyvector alternatives](/tools/m1guelpf-tinyvector/alternatives) and [fastembed alternatives](/tools/qdrant-fastembed/alternatives) ([tinyvector markdown twin](/tools/m1guelpf-tinyvector/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/m1guelpf-tinyvector-vs-qdrant-fastembed.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, tinyvector or fastembed? tinyvector: 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 tinyvector and fastembed? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [tinyvector trust report](/tools/m1guelpf-tinyvector/trust); [fastembed trust report](/tools/qdrant-fastembed/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=m1guelpf-tinyvector`](/api/graphcanon/graph?tool=m1guelpf-tinyvector) - 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/_