Hugging Face and DORA Metrics: Fast Code, Slow Response

**Dora Metrics** using **Middleware Open Source**. We’ll cover three key aspects—no more, no less: **Thesis**: More waiting, less building (with long delays and slow recovery times). **Strengths**: Highlighting the rapid roadmap-building process and extensive contributions. ... We’ll explore how Hugging Face’s fast-moving development is being held back by prolonged response times, extended rework cycles, and slow recovery, even after approvals are secured. ## 1. Thesis: Shackled Beast While Hugging Face powers through quick iterations, it finds itself "shackled" by delays in response time, rework, and post-approval wait times. The numbers tell the story: **June 2024**: Deployment frequency hit 201 releases. **July-September 2024**: Deployment dropped slightly, but still maintained a robust 170-188 releases per month. However, the team’s growing workload is evident in longer lead times and rising rework: **Lead Time**: 8 days in June stretched to nearly 12 days by September. **Merge Time**: Grew from 2.9 to 4.7 days over the same period. **Rework**: Jumped from 2.3 to 3.7 days. These delays indicate that while the team is highly productive, much of their effort is spent waiting—for first responses, reviews, and rework to be completed. ### The Cycle: A contributor submits a PR (pull request), but it can take days to get a first response. Then comes the rework cycle—further extending the lead time. Even after approval, the code waits in limbo before deployment. This pattern not only affects development velocity but also hampers the team’s ability to respond quickly to incidents. As the team grows busier, recovery times from incidents have hovered around **4 days**, keeping HF in the less desirable category of the 2023 State of DevOps Report for recovery metrics. … ## 3. Using Strengths to Overcome Weaknesses ... If you refer to the reviewer dependency above (generated using MiddlewareHQ) - Right now, only **three maintainers** bear the brunt of reviewing hundreds of PRs, which inevitably leads to delays. Spreading the load by training more frequent contributors as reviewers could ease this bottleneck and improve response times. … ## Conclusion: Fast Roadmap, Slower Execution Hugging Face excels at shipping features fast, with a high deployment frequency month over month. However, challenges like rework, delayed reviews, and slow incident recovery times are pulling the shackled beast back. By leveraging its strengths—more reviewers, better pre-code consensus, and faster iterations—Hugging Face can continue setting the pace for AI development while reducing operational drag.