CloudByte PMS

CloudByte PMS tracks AI coding sessions, token spend, commits and security events across Claude Code, Cursor and GitHub Copilot, then turns them into cost attribution, ROI estimates and a searchable project knowledge base for engineering teams.

CloudByte PMS, presented on its site as CloudByte AI, is an observability and governance platform for engineering teams that use AI coding assistants. A lightweight agent runs alongside tools such as Claude Code, Cursor and GitHub Copilot and records sessions, prompts, tool calls and commits without asking developers to change how they work. The data feeds a single dashboard covering activity, spend, security events, shared project knowledge and delivery metrics. The product is aimed at engineering leaders, platform teams and finance or security stakeholders who have rolled out AI coding seats and cannot see what the invoice actually pays for. Spend is split by developer, project and model, with per-seat burn rates, prompt-caching ratios and flags for oversized prompts. Adoption views show which paid seats sit idle, which the vendors' own completion counts do not reveal. An insights area estimates ROI and hours saved, and clusters prompts by type of work such as refactoring, debugging or test writing. In practice, the agent reads each commit diff and a model writes a short summary, so commits can be searched next to AI observations, tickets, pull requests, skills and CLAUDE.md files. This builds a per-project knowledge base meant to help new developers get context quickly. On the security side, the platform is described as catching credentials and personal data in prompts before they leave the machine, and as detecting unapproved plugins, MCP servers and unknown AI tools, with an audit trail. Git hosts and trackers such as GitHub, GitLab, Bitbucket, Linear and Jira are listed as part of the supported stack. Enterprise customers can bring their own Anthropic key. Among alternatives, it sits between vendor-provided usage reports, which stay shallow, and general engineering analytics suites that were not built around AI sessions. Its focus on prompts, tokens and the link from sessions to pull requests is the main differentiator. Buyers should confirm pricing, data retention and agent deployment details with the vendor, since the homepage leaves several of them unstated.