Track AI coding token usage locally with TokenBoard

AI-powered coding assistants are reshaping software development workflows, yet tracking their usage remains a challenge. Most tools rely on opaque billing dashboards that reveal little about actual development patterns. To address this gap, one developer has created TokenBoard—a lightweight, privacy-focused application designed to surface meaningful metrics from AI coding sessions.

A privacy-first approach to AI usage tracking

TokenBoard tackles a critical pain point for developers who rely on AI coding tools: understanding usage patterns without compromising code privacy. Unlike traditional analytics platforms that process and store prompts or code snippets, TokenBoard operates entirely locally. It aggregates metadata such as token consumption, session duration, and model usage without transmitting any sensitive information. This architecture ensures that developers can monitor their AI coding habits without exposing proprietary code or intellectual property.

The tool currently supports Codex, with plans to expand compatibility to additional AI coding agents like Claude Code. By focusing on metrics rather than content, TokenBoard provides transparency into development workloads while maintaining strict data privacy standards. For teams concerned about vendor lock-in or regulatory compliance, this approach offers a compelling alternative to cloud-based usage tracking solutions.

Key metrics for developers and teams

TokenBoard’s dashboard presents a concise view of AI coding activity through several core metrics. Developers can track token consumption, which directly correlates with usage costs, alongside session counts and message volumes. The platform also categorizes activity by AI model, enabling teams to identify which tools deliver the most value per session. Additional insights include estimated session costs and active development days, which help quantify productivity trends over time.

For engineering managers, these metrics can inform tool selection and budget planning. By comparing token efficiency across different AI models, teams can optimize their workflows while minimizing unnecessary spending. The ability to share anonymized stats cards further extends TokenBoard’s utility, allowing developers to benchmark their usage against peers without revealing sensitive details.

Future enhancements and community feedback

As an early-stage project, TokenBoard continues to evolve based on user input. The current focus is on expanding agent support, with Claude Code slated for integration in upcoming releases. The developer behind TokenBoard has also expressed interest in refining the leaderboard and profile features based on community feedback.

For developers who frequently use Codex, TokenBoard offers a practical way to monitor AI coding habits without sacrificing privacy. The tool’s emphasis on local processing and transparent metrics aligns with the needs of security-conscious teams. Whether used for personal tracking or team-level insights, TokenBoard represents a step toward more accountable and informed AI-assisted development.