--- title: "awesome-production-machine-learning vs ravendb" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ethicalml-awesome-production-machine-learning-vs-ravendb-ravendb" tools: ["ethicalml-awesome-production-machine-learning", "ravendb-ravendb"] --- # awesome-production-machine-learning vs ravendb *GraphCanon updated Jul 11, 2026* ## Verdict Pick awesome-production-machine-learning when license: awesome-production-machine-learning is MIT, ravendb is Other; pick ravendb when license: ravendb is Other, awesome-production-machine-learning is MIT. [awesome-production-machine-learning](https://ethicalml.github.io/awesome-production-machine-learning) reports 21k GitHub stars, 2.6k forks, and 32 open issues, last pushed Jul 3, 2026. [ravendb](https://ravendb.net) has 4.0k stars, 860 forks, and 61 open issues, last pushed Jul 9, 2026. Figures are from public GitHub metadata via [awesome-production-machine-learning's repository](https://github.com/EthicalML/awesome-production-machine-learning) and [ravendb's repository](https://github.com/ravendb/ravendb). | | [awesome-production-machine-learning](/tools/ethicalml-awesome-production-machine-learning.md) | [ravendb](/tools/ravendb-ravendb.md) | | --- | --- | --- | | Tagline | A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning | ACID Document Database | | Stars | 20,719 | 3,971 | | Forks | 2,585 | 860 | | Open issues | 32 | 61 | | Language | - | C# | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | MIT | Other | | Categories | AI Agents, LLM Frameworks, Vector Databases | Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-production-machine-learning](/tools/ethicalml-awesome-production-machine-learning.md) | [ravendb](/tools/ravendb-ravendb.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 8d | 1d | | Open issues (now) | 32 | 61 | | Full report | [trust report](/tools/ethicalml-awesome-production-machine-learning/trust.md) | [trust report](/tools/ravendb-ravendb/trust.md) | ## Choose when ### Choose awesome-production-machine-learning if… - License: awesome-production-machine-learning is MIT, ravendb is Other. - Tags unique to awesome-production-machine-learning: awesome, awesome-list, data-mining, deep-learning. - Also covers AI Agents, LLM Frameworks. ### Choose ravendb if… - License: ravendb is Other, awesome-production-machine-learning is MIT. - Tags unique to ravendb: csharp, database, document-database, dotnet. - More recently updated (last pushed Jul 9, 2026). ## When NOT to use awesome-production-machine-learning - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - 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 ravendb - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## Common questions ### What is the difference between awesome-production-machine-learning and ravendb? awesome-production-machine-learning: A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning. ravendb: ACID Document Database. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-production-machine-learning over ravendb? Choose awesome-production-machine-learning over ravendb when License: awesome-production-machine-learning is MIT, ravendb is Other; Tags unique to awesome-production-machine-learning: awesome, awesome-list, data-mining, deep-learning; Also covers AI Agents, LLM Frameworks. ### When should I choose ravendb over awesome-production-machine-learning? Choose ravendb over awesome-production-machine-learning when License: ravendb is Other, awesome-production-machine-learning is MIT; Tags unique to ravendb: csharp, database, document-database, dotnet; More recently updated (last pushed Jul 9, 2026). ### When should I avoid awesome-production-machine-learning? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. 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 ravendb? Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### Is awesome-production-machine-learning or ravendb more popular on GitHub? awesome-production-machine-learning has more GitHub stars (20,719 vs 3,971). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-production-machine-learning and ravendb open source? Yes - both are open-source projects on GitHub (awesome-production-machine-learning: MIT, ravendb: Other). ### Where can I find alternatives to awesome-production-machine-learning or ravendb? GraphCanon lists graph-backed alternatives at [awesome-production-machine-learning alternatives](/tools/ethicalml-awesome-production-machine-learning/alternatives) and [ravendb alternatives](/tools/ravendb-ravendb/alternatives) ([awesome-production-machine-learning markdown twin](/tools/ethicalml-awesome-production-machine-learning/alternatives.md), [ravendb markdown twin](/tools/ravendb-ravendb/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/ethicalml-awesome-production-machine-learning-vs-ravendb-ravendb.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-production-machine-learning or ravendb? awesome-production-machine-learning: Active. ravendb: 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 awesome-production-machine-learning and ravendb? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-production-machine-learning trust report](/tools/ethicalml-awesome-production-machine-learning/trust); [ravendb trust report](/tools/ravendb-ravendb/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ethicalml-awesome-production-machine-learning`](/api/graphcanon/graph?tool=ethicalml-awesome-production-machine-learning) - 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/_