--- title: "NumKong vs qdrant" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ashvardanian-numkong-vs-qdrant-qdrant" tools: ["ashvardanian-numkong", "qdrant-qdrant"] --- # NumKong vs qdrant *GraphCanon updated Jul 11, 2026* ## Verdict Pick NumKong when numKong is primarily C; qdrant is Rust; pick qdrant when qdrant is primarily Rust; NumKong is C. [NumKong](https://ashvardanian.com/posts/numkong) reports 1.8k GitHub stars, 124 forks, and 30 open issues, last pushed Jul 9, 2026. [qdrant](https://qdrant.tech) has 33k stars, 2.5k forks, and 631 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [NumKong's repository](https://github.com/ashvardanian/NumKong) and [qdrant's repository](https://github.com/qdrant/qdrant). | | [NumKong](/tools/ashvardanian-numkong.md) | [qdrant](/tools/qdrant-qdrant.md) | | --- | --- | --- | | Tagline | SIMD-accelerated distances, dot products, matrix ops, geospatial & geometric kernels for 16 numeric types — from 6-bit floats to 64-bit complex — across x86, Arm, RISC-V, and WASM, with bindings for P | High-performance, massive-scale Vector Database and Vector Search Engine | | Stars | 1,845 | 33,143 | | Forks | 124 | 2,483 | | Open issues | 30 | 631 | | Language | C | Rust | | Adopt for | - | High-performance vector database with support for distributed deployment. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Qdrant is available under the Apache License 2.0. | | Categories | Vector Databases, Data & Retrieval, Evaluation & Observability | Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [NumKong](/tools/ashvardanian-numkong.md) | [qdrant](/tools/qdrant-qdrant.md) | | --- | --- | --- | | Days since push | 1d | 0d | | Open issues (now) | 30 | 631 | | Owner type | User | Organization | | Full report | [trust report](/tools/ashvardanian-numkong/trust.md) | [trust report](/tools/qdrant-qdrant/trust.md) | ## Decision facts: qdrant - **Hosting:** self hosted - Qdrant supports self-hosted deployment along with a cloud option at https://cloud.qdrant.io/. - **Requirements:** - Distributed deployment with sharding and replication is supported.; - No specific minimum RAM requirement provided. Performance and resource use will depend on the scale of embedding collections. - **Adopt for:** High-performance vector database with support for distributed deployment. - **License detail:** Qdrant is available under the Apache License 2.0. ## Choose when ### Choose NumKong if… - NumKong is primarily C; qdrant is Rust. - Tags unique to NumKong: matrix-multiplication, assembly, blas, cpp. - Also covers Evaluation & Observability. ### Choose qdrant if… - qdrant is primarily Rust; NumKong is C. - Qdrant supports self-hosted deployment along with a cloud option at https://cloud.qdrant.io/. - Requirements: - Distributed deployment with sharding and replication is supported.; - No specific minimum RAM requirement provided. Performance and resource use will depend on the scale of embedding collections.. - Tags unique to qdrant: knn-algorithm, vector-search-engine, vector-database, embeddings-similarity. - - When scalability and performance are paramount in handling large-scale embeddings. ## When NOT to use NumKong - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## When NOT to use qdrant - - Avoid if your project requires more traditional relational database features as Qdrant focuses exclusively on vectors. - - If minimalistic setup is crucial, since Qdrant's capability for distributed deployment may introduce complexity that is not necessary for smaller-scale applications. - - For use cases where non-Rust environments significantly limit the feasibility of integrating external tools. ## Common questions ### What is the difference between NumKong and qdrant? NumKong: SIMD-accelerated distances, dot products, matrix ops, geospatial & geometric kernels for 16 numeric types — from 6-bit floats to 64-bit complex — across x86, Arm, RISC-V, and WASM, with bindings for P. qdrant: High-performance, massive-scale Vector Database and Vector Search Engine. See the comparison table for live GitHub stats and shared categories. ### When should I choose NumKong over qdrant? Choose NumKong over qdrant when NumKong is primarily C; qdrant is Rust; Tags unique to NumKong: matrix-multiplication, assembly, blas, cpp; Also covers Evaluation & Observability. ### When should I choose qdrant over NumKong? Choose qdrant over NumKong when qdrant is primarily Rust; NumKong is C; Qdrant supports self-hosted deployment along with a cloud option at https://cloud.qdrant.io/; Requirements: - Distributed deployment with sharding and replication is supported.; - No specific minimum RAM requirement provided. Performance and resource use will depend on the scale of embedding collections.; Tags unique to qdrant: knn-algorithm, vector-search-engine, vector-database, embeddings-similarity; - When scalability and performance are paramount in handling large-scale embeddings. ### When should I avoid NumKong? Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### When should I avoid qdrant? - Avoid if your project requires more traditional relational database features as Qdrant focuses exclusively on vectors. - If minimalistic setup is crucial, since Qdrant's capability for distributed deployment may introduce complexity that is not necessary for smaller-scale applications. - For use cases where non-Rust environments significantly limit the feasibility of integrating external tools. ### Is NumKong or qdrant more popular on GitHub? qdrant has more GitHub stars (33,143 vs 1,845). Stars measure visibility, not whether either tool fits your constraints. ### Are NumKong and qdrant open source? Yes - both are open-source projects on GitHub (NumKong: Apache-2.0, qdrant: Apache-2.0). ### Where can I find alternatives to NumKong or qdrant? GraphCanon lists graph-backed alternatives at [NumKong alternatives](/tools/ashvardanian-numkong/alternatives) and [qdrant alternatives](/tools/qdrant-qdrant/alternatives) ([NumKong markdown twin](/tools/ashvardanian-numkong/alternatives.md), [qdrant markdown twin](/tools/qdrant-qdrant/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/ashvardanian-numkong-vs-qdrant-qdrant.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, NumKong or qdrant? NumKong: Very active. qdrant: 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 NumKong and qdrant? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [NumKong trust report](/tools/ashvardanian-numkong/trust); [qdrant trust report](/tools/qdrant-qdrant/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ashvardanian-numkong`](/api/graphcanon/graph?tool=ashvardanian-numkong) - 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/_