Netron vs Umami
| Tagline | Interactive visualizer for neural network and machine learning model graphs | Simple, fast, privacy-focused web analytics in a single lightweight dashboard |
| Category | Product & Web Analytics | Product & Web Analytics |
| Replaces | Google Analytics, Mixpanel, Amplitude | Google Analytics |
| GitHub stars | 33k | 37k |
| Language | Python | TypeScript |
| License | MIT | MIT |
| Self-host difficulty | 1/5 Effortless | 3/5 Moderate |
| Deploy options | Manual | One-Click Docker Docker Compose Manual |
| Managed hosting | ||
| Last updated | yesterday | yesterday |
| View repo | View repo |
Where each falls short
The honest trade-offs — what you give up with each, versus the proprietary tools they replace.
Netron
- Purely a model visualization tool; no runtime analytics, dashboards, or event tracking
- Does not replace web or product analytics SaaS in any meaningful way
- No team collaboration or sharing features beyond exporting images
- No support for real-time or streaming model inference monitoring
Umami
- Deliberately minimal: no heatmaps, session replay, or deep product-analytics like funnels/retention found in Mixpanel/Amplitude.
- Event/custom-property analytics are basic compared to dedicated product-analytics tools.
- No built-in alerting or anomaly detection.
Bottom line
Choose Netron if you want the lower-effort setup; choose Umami for the larger community and ecosystem. Open each guide below for deploy steps and the full feature gap.