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I built a tool that got 16K downloads, but no one uses the charts. Here's what they're missing.
r/aiagents
8/15/2025
Content Summary
The author built a developer tool called DoCoreAI that tracks prompt usage and cost patterns across OpenAI's API. Despite reaching 16K+ downloads, most users don't engage with the telemetry dashboard, especially the charts that provide valuable insights. The author realized this issue and improved the documentation and created a getting started blog to guide users to the dashboard. The tool helps developers save time, reduce token costs, and monitor prompt health. It works with both OpenAI and Groq, and no original prompt data leaves the user's machine.
Opinion Analysis
Mainstream opinion seems to be that the tool has potential but needs better user onboarding and education to drive engagement with the dashboard. Some users may find the concept of tracking prompt usage and cost useful, but others might see it as redundant or overly complex. A few comments suggest that the value of the charts and metrics isn't immediately clear, which could be a barrier to adoption. There's also a debate around whether these metrics are truly meaningful or if they're subjective and arbitrary, as one user pointed out.
SAAS TOOLS
| SaaS | URL | Category | Features/Notes |
|---|---|---|---|
| DoCoreAI | https://docoreai.com | Developer Tool | Tracks prompt usage and cost patterns across OpenAI's API, includes telemetry dashboard with charts for insights, works with OpenAI and Groq, no original prompt data leaves your machine |
| Groq | https://groq.com | AI Inference Platform | Integrated with DoCoreAI for tracking prompt usage |
| OpenAI | https://openai.com | AI API Provider | Integrated with DoCoreAI for tracking prompt usage |
USER NEEDS
Pain Points:
- Users are not utilizing the dashboard features of the tool despite high download numbers
- Lack of understanding or awareness about the value of the dashboard and its charts
- Difficulty in tracking and optimizing prompt usage and costs
Problems to Solve:
- Developers want to make their LLM usage less of a black box
- Developers need better visibility into prompt efficiency and cost savings
- Developers seek tools that help reduce token costs and improve model performance
Potential Solutions:
- Improving documentation and onboarding process to guide users to the dashboard
- Creating a getting started blog to educate users on the benefits of the dashboard
- Highlighting the value of the charts through examples and use cases
GROWTH FACTORS
Effective Strategies:
- Focusing on improving user onboarding and education through documentation and blog content
- Building a tool that addresses a specific pain point (tracking prompt usage and cost)
- Leveraging community feedback to refine the product and increase user engagement
Marketing & Acquisition:
- Utilizing Reddit as a platform to share the tool and gather feedback
- Sharing detailed setup guides and dashboard screenshots to demonstrate value
Monetization & Product:
- The tool is free to use, but could explore monetization through premium features or enterprise solutions
- Emphasizing the value of the dashboard and charts as key differentiators
- Ensuring the product meets the needs of developers by focusing on transparency and optimization
User Engagement:
- Encouraging user feedback and discussion through Reddit and other channels
- Providing visual insights through charts to keep users engaged and informed