--- title: "Made-With-ML vs nndeploy" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gokumohandas-made-with-ml-vs-nndeploy-nndeploy" tools: ["gokumohandas-made-with-ml", "nndeploy-nndeploy"] --- # Made-With-ML vs nndeploy *GraphCanon updated Jul 15, 2026* ## Verdict Pick Made-With-ML when made-With-ML is primarily Jupyter Notebook; nndeploy is C++; pick nndeploy when nndeploy is primarily C++; Made-With-ML is Jupyter Notebook. [Made-With-ML](https://madewithml.com) reports 49k GitHub stars, 7.7k forks, and 27 open issues, last pushed Mar 4, 2026. [nndeploy](https://nndeploy-zh.readthedocs.io/zh-cn/latest/) has 1.8k stars, 226 forks, and 23 open issues, last pushed Apr 25, 2026. Figures are from public GitHub metadata via [Made-With-ML's repository](https://github.com/GokuMohandas/Made-With-ML) and [nndeploy's repository](https://github.com/nndeploy/nndeploy). | | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [nndeploy](/tools/nndeploy-nndeploy.md) | | --- | --- | --- | | Tagline | Learn how to develop, deploy and iterate on production-grade ML applications. | 一款简单易用和高性能的AI部署框架 | An Easy-to-Use and High-Performance AI Deployment Framework | | Stars | 48,703 | 1,847 | | Forks | 7,661 | 226 | | Open issues | 27 | 23 | | Language | Jupyter Notebook | C++ | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | AI Agents, LLM Frameworks, Model Training | Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [nndeploy](/tools/nndeploy-nndeploy.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Steady (60%) | | Days since push | 132d | 80d | | Open issues (now) | 27 | 23 | | Owner type | User | Organization | | Full report | [trust report](/tools/gokumohandas-made-with-ml/trust.md) | [trust report](/tools/nndeploy-nndeploy/trust.md) | ## Shared compatibility - **Python**: [Made-With-ML](/tools/gokumohandas-made-with-ml.md) - Python runtime; [nndeploy](/tools/nndeploy-nndeploy.md) - Python runtime ## Choose when ### Choose Made-With-ML if… - Made-With-ML is primarily Jupyter Notebook; nndeploy is C++. - License: Made-With-ML is MIT, nndeploy is Apache-2.0. - Tags unique to Made-With-ML: data-engineering, data-quality, data-science, distributed-ml. - Also covers AI Agents. ### Choose nndeploy if… - nndeploy is primarily C++; Made-With-ML is Jupyter Notebook. - License: nndeploy is Apache-2.0, Made-With-ML is MIT. - Tags unique to nndeploy: ai, ascend, deployment, diffusers. - Also covers Inference & Serving. ## When NOT to use Made-With-ML - Last GitHub push was 132 days ago (slowing maintenance, Mar 4, 2026). Validate activity before betting a new project on Made-With-ML. - 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. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## When NOT to use nndeploy - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## Common questions ### What is the difference between Made-With-ML and nndeploy? Made-With-ML: Learn how to develop, deploy and iterate on production-grade ML applications.. nndeploy: 一款简单易用和高性能的AI部署框架 | An Easy-to-Use and High-Performance AI Deployment Framework. See the comparison table for live GitHub stats and shared categories. ### When should I choose Made-With-ML over nndeploy? Choose Made-With-ML over nndeploy when Made-With-ML is primarily Jupyter Notebook; nndeploy is C++; License: Made-With-ML is MIT, nndeploy is Apache-2.0; Tags unique to Made-With-ML: data-engineering, data-quality, data-science, distributed-ml; Also covers AI Agents. ### When should I choose nndeploy over Made-With-ML? Choose nndeploy over Made-With-ML when nndeploy is primarily C++; Made-With-ML is Jupyter Notebook; License: nndeploy is Apache-2.0, Made-With-ML is MIT; Tags unique to nndeploy: ai, ascend, deployment, diffusers; Also covers Inference & Serving. ### When should I avoid Made-With-ML? Last GitHub push was 132 days ago (slowing maintenance, Mar 4, 2026). Validate activity before betting a new project on Made-With-ML. 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. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### When should I avoid nndeploy? Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is Made-With-ML or nndeploy more popular on GitHub? Made-With-ML has more GitHub stars (48,703 vs 1,847). Stars measure visibility, not whether either tool fits your constraints. ### Are Made-With-ML and nndeploy open source? Yes - both are open-source projects on GitHub (Made-With-ML: MIT, nndeploy: Apache-2.0). ### Where can I find alternatives to Made-With-ML or nndeploy? GraphCanon lists graph-backed alternatives at [Made-With-ML alternatives](/tools/gokumohandas-made-with-ml/alternatives) and [nndeploy alternatives](/tools/nndeploy-nndeploy/alternatives) ([Made-With-ML markdown twin](/tools/gokumohandas-made-with-ml/alternatives.md), [nndeploy markdown twin](/tools/nndeploy-nndeploy/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/gokumohandas-made-with-ml-vs-nndeploy-nndeploy.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Made-With-ML or nndeploy? Made-With-ML: Slowing. nndeploy: Steady. 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 Made-With-ML and nndeploy? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Made-With-ML trust report](/tools/gokumohandas-made-with-ml/trust); [nndeploy trust report](/tools/nndeploy-nndeploy/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=gokumohandas-made-with-ml`](/api/graphcanon/graph?tool=gokumohandas-made-with-ml) - 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/_