Beezi
Beezi provides observability for AI-assisted software work, turning coding tool data into actionable insights on cost, practice, and delivery. The platform tracks per-session metrics like duration, retries, model choice, and MCP server connections, enabling engineering leaders to monitor spend, estimate work, and manage AI adoption across teams.
- Artificial Intelligence
- Data & Analytics
- Developer Tools
- Enterprise Software
- Software Only
Funding
Founders
Product
Problem
Engineering teams adopting AI-assisted coding tools lack visibility into how these tools affect cost, productivity, and delivery. Without per-session data on model usage, retries, and time spent, leaders cannot accurately estimate work, control spending, or answer auditor questions about what data AI tools can access.
Solution
Beezi provides an observability platform that turns coding tool data into insights on cost, practice, and delivery. It records how work gets done per session, per branch, and per model, capturing duration, retries, model choice, and time actively running versus waiting for responses. The platform builds an estimating baseline from weeks of session data, so teams can reference their own historical patterns for what normal work costs and takes. It also tracks rate limits, tier usage, and MCP server connections, giving leaders a clear inventory of what AI tools are connected to and where spend actually goes.
Target Audience
Engineering leaders, engineering managers, and platform teams at software companies that use AI-assisted coding tools and need visibility into cost, performance, and adoption.
Features
- Per-session tracking of duration, retries, model choice, and active versus waiting time across branches
- Automated estimating baseline built from historical session patterns to inform future work estimates
- Spend analysis showing where AI costs accumulate and where tier upgrades may be more economical than engineer wait time
- Rate limit and tier usage monitoring to identify underused or overused subscription levels
- MCP server inventory listing all connected AI tools and the data they can access for audit and compliance purposes
- Adoption tracking per user and team with benchmarking against company averages