Gemini Flash 2.0 is a promising API for analyzing YouTube videos, but it has two major limitations that make it difficult/impossible to use in a production environment.
1. Strict Daily Limits & Lack of Video Duration Metadata
The API is currently limited to 8 hours of video per day, which severely restricts its scalability for real-world applications. Additionally, there is no way to retrieve the duration of a video before processing it, making it impossible to optimize quota usage efficiently. This means developers have to blindly send videos for analysis without knowing whether they are exceeding the daily limit.
2. Inefficient Processing Approach
By default, the model analyzes each frame of the video, rather than prioritizing the transcript when available. This approach is inefficient because:
- Many YouTube videos already have high-quality transcripts that provide a reliable summary of the content.
- Processing every frame consumes significantly more resources, both for Google and for developers.
- If the API leveraged transcripts first and only analyzed frames when necessary, it could dramatically reduce computational costs and allow Google to raise usage limits.
Suggested Improvements
- Expose video duration metadata before analysis to help developers manage quota usage effectively.
- Prioritize transcripts when available to reduce processing load and improve efficiency.
- If frame analysis is still necessary, provide an option to toggle between transcript-based and frame-based analysis to give developers more control over resource usage.
Gemini Flash 2.0 is really good but these limitations make it difficult to integrate into scalable production systems. Addressing these issues would significantly enhance its usability for developers.
Would love to hear thoughts from others—has anyone found workarounds for these problems?