--- title: "ml-surveys vs weaviate" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/eugeneyan-ml-surveys-vs-weaviate-weaviate" tools: ["eugeneyan-ml-surveys", "weaviate-weaviate"] --- # ml-surveys vs weaviate *GraphCanon updated Jul 12, 2026* ## Verdict Pick ml-surveys when license: ml-surveys is MIT, weaviate is BSD-3-Clause; pick weaviate when license: weaviate is BSD-3-Clause, ml-surveys is MIT. [ml-surveys](https://github.com/eugeneyan/ml-surveys) reports 2.9k GitHub stars, 291 forks, and 2 open issues, last pushed Mar 17, 2023. [weaviate](https://weaviate.io/developers/weaviate/) has 17k stars, 1.3k forks, and 596 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [ml-surveys's repository](https://github.com/eugeneyan/ml-surveys) and [weaviate's repository](https://github.com/weaviate/weaviate). | | [ml-surveys](/tools/eugeneyan-ml-surveys.md) | [weaviate](/tools/weaviate-weaviate.md) | | --- | --- | --- | | Tagline | 📋 Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc. | Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a c | | Stars | 2,900 | 16,572 | | Forks | 291 | 1,343 | | Open issues | 2 | 596 | | Language | - | Go | | Adopt for | - | Weaviate is an open-source vector database that supports both object and vector storage with robust deployment options, making it suitable for applications requiring seamless integration of approximate nearest neighbor ( | | Persona | - | - | | Runtime | - | - | | License | MIT | BSD-3-Clause | | Categories | Computer Vision, Vector Databases | Computer Vision, Inference & Serving, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ml-surveys](/tools/eugeneyan-ml-surveys.md) | [weaviate](/tools/weaviate-weaviate.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 1212d | 0d | | Open issues (now) | 2 | 596 | | Owner type | User | Organization | | Security scan | No lockfile | 12 low (12 low) | | Full report | [trust report](/tools/eugeneyan-ml-surveys/trust.md) | [trust report](/tools/weaviate-weaviate/trust.md) | ## Decision facts: weaviate - **Requirements:** Requires Docker; Support for Kubernetes, AWS Marketplace, GCP Marketplace; Availability of Python client - **Adopt for:** Weaviate is an open-source vector database that supports both object and vector storage with robust deployment options, making it suitable for applications requiring seamless integration of approximate nearest neighbor ( ## Choose when ### Choose ml-surveys if… - License: ml-surveys is MIT, weaviate is BSD-3-Clause. - Tags unique to ml-surveys: computer-vision, deep-learning, embeddings, machine-learning. - Leaner open-issue backlog (2). ### Choose weaviate if… - License: weaviate is BSD-3-Clause, ml-surveys is MIT. - Requirements: Requires Docker; Support for Kubernetes, AWS Marketplace, GCP Marketplace; Availability of Python client. - Tags unique to weaviate: approximate-nearest-neighbor-search, generative-search, grpc, hnsw. - Also covers Inference & Serving. - weaviate ships Docker support for self-hosted deployment. - * When you need to integrate vector search capabilities with structured data filtering within a single system. ## When NOT to use ml-surveys - Last GitHub push was 1213 days ago (dormant maintenance, Mar 17, 2023). Validate activity before betting a new project on ml-surveys. - 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 weaviate - * Avoid using when low-level customization of the underlying vector indexing mechanisms is required beyond what current configuration options offer. - * Not recommended if your application does not benefit from cloud-native fault tolerance and scalability features. - * If real-time data import with automatic vector generation through lightweight models is non-essential for your workflow. ## Common questions ### What is the difference between ml-surveys and weaviate? ml-surveys: 📋 Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc.. weaviate: Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a c. See the comparison table for live GitHub stats and shared categories. ### When should I choose ml-surveys over weaviate? Choose ml-surveys over weaviate when License: ml-surveys is MIT, weaviate is BSD-3-Clause; Tags unique to ml-surveys: computer-vision, deep-learning, embeddings, machine-learning; Leaner open-issue backlog (2). ### When should I choose weaviate over ml-surveys? Choose weaviate over ml-surveys when License: weaviate is BSD-3-Clause, ml-surveys is MIT; Requirements: Requires Docker; Support for Kubernetes, AWS Marketplace, GCP Marketplace; Availability of Python client; Tags unique to weaviate: approximate-nearest-neighbor-search, generative-search, grpc, hnsw; Also covers Inference & Serving; weaviate ships Docker support for self-hosted deployment; * When you need to integrate vector search capabilities with structured data filtering within a single system. ### When should I avoid ml-surveys? Last GitHub push was 1213 days ago (dormant maintenance, Mar 17, 2023). Validate activity before betting a new project on ml-surveys. 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 weaviate? * Avoid using when low-level customization of the underlying vector indexing mechanisms is required beyond what current configuration options offer. * Not recommended if your application does not benefit from cloud-native fault tolerance and scalability features. * If real-time data import with automatic vector generation through lightweight models is non-essential for your workflow. ### Is ml-surveys or weaviate more popular on GitHub? weaviate has more GitHub stars (16,572 vs 2,900). Stars measure visibility, not whether either tool fits your constraints. ### Are ml-surveys and weaviate open source? Yes - both are open-source projects on GitHub (ml-surveys: MIT, weaviate: BSD-3-Clause). ### Where can I find alternatives to ml-surveys or weaviate? GraphCanon lists graph-backed alternatives at [ml-surveys alternatives](/tools/eugeneyan-ml-surveys/alternatives) and [weaviate alternatives](/tools/weaviate-weaviate/alternatives) ([ml-surveys markdown twin](/tools/eugeneyan-ml-surveys/alternatives.md), [weaviate markdown twin](/tools/weaviate-weaviate/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/eugeneyan-ml-surveys-vs-weaviate-weaviate.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ml-surveys or weaviate? ml-surveys: Dormant. weaviate: 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 ml-surveys and weaviate? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ml-surveys trust report](/tools/eugeneyan-ml-surveys/trust); [weaviate trust report](/tools/weaviate-weaviate/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=eugeneyan-ml-surveys`](/api/graphcanon/graph?tool=eugeneyan-ml-surveys) - 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/_