--- title: "oasysdb vs qdrant" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/edwinkys-oasysdb-vs-qdrant-qdrant" tools: ["edwinkys-oasysdb", "qdrant-qdrant"] --- # oasysdb vs qdrant *GraphCanon updated Jul 11, 2026* ## Verdict Pick oasysdb when tags unique to oasysdb: approximate-nearest-neighbors, ivfpq, mysql, open-source; pick qdrant when qdrant supports self-hosted deployment along with a cloud option at https://cloud.qdrant.io/. [oasysdb](https://github.com/edwinkys/oasysdb) reports 375 GitHub stars, 14 forks, and 0 open issues, last pushed Nov 29, 2024. [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 [oasysdb's repository](https://github.com/edwinkys/oasysdb) and [qdrant's repository](https://github.com/qdrant/qdrant). | | [oasysdb](/tools/edwinkys-oasysdb.md) | [qdrant](/tools/qdrant-qdrant.md) | | --- | --- | --- | | Tagline | In-memory vector store with efficient read and write performance for semantic caching and retrieval system. Redis for Semantic Caching. | High-performance, massive-scale Vector Database and Vector Search Engine | | Stars | 375 | 33,143 | | Forks | 14 | 2,483 | | Open issues | 0 | 631 | | Language | Rust | 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 | Data & Retrieval, Evaluation & Observability, Vector Databases | Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [oasysdb](/tools/edwinkys-oasysdb.md) | [qdrant](/tools/qdrant-qdrant.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 589d | 0d | | Open issues (now) | 0 | 631 | | Owner type | User | Organization | | Full report | [trust report](/tools/edwinkys-oasysdb/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 oasysdb if… - Tags unique to oasysdb: approximate-nearest-neighbors, ivfpq, mysql, open-source. - Also covers Evaluation & Observability. - Leaner open-issue backlog (0). ### Choose qdrant if… - 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: ai-search, embeddings-similarity, hnsw, knn-algorithm. - - When scalability and performance are paramount in handling large-scale embeddings. ## When NOT to use oasysdb - Last GitHub push was 590 days ago (dormant maintenance, Nov 29, 2024). Validate activity before betting a new project on oasysdb. - 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. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## 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 oasysdb and qdrant? oasysdb: In-memory vector store with efficient read and write performance for semantic caching and retrieval system. Redis for Semantic Caching.. 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 oasysdb over qdrant? Choose oasysdb over qdrant when Tags unique to oasysdb: approximate-nearest-neighbors, ivfpq, mysql, open-source; Also covers Evaluation & Observability; Leaner open-issue backlog (0). ### When should I choose qdrant over oasysdb? Choose qdrant over oasysdb when 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: ai-search, embeddings-similarity, hnsw, knn-algorithm; - When scalability and performance are paramount in handling large-scale embeddings. ### When should I avoid oasysdb? Last GitHub push was 590 days ago (dormant maintenance, Nov 29, 2024). Validate activity before betting a new project on oasysdb. 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. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### 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 oasysdb or qdrant more popular on GitHub? qdrant has more GitHub stars (33,143 vs 375). Stars measure visibility, not whether either tool fits your constraints. ### Are oasysdb and qdrant open source? Yes - both are open-source projects on GitHub (oasysdb: Apache-2.0, qdrant: Apache-2.0). ### Where can I find alternatives to oasysdb or qdrant? GraphCanon lists graph-backed alternatives at [oasysdb alternatives](/tools/edwinkys-oasysdb/alternatives) and [qdrant alternatives](/tools/qdrant-qdrant/alternatives) ([oasysdb markdown twin](/tools/edwinkys-oasysdb/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/edwinkys-oasysdb-vs-qdrant-qdrant.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, oasysdb or qdrant? oasysdb: Dormant. 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 oasysdb and qdrant? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [oasysdb trust report](/tools/edwinkys-oasysdb/trust); [qdrant trust report](/tools/qdrant-qdrant/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=edwinkys-oasysdb`](/api/graphcanon/graph?tool=edwinkys-oasysdb) - 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/_