Know What AI Costs, Who Uses It, and Whether It Is Standardized
Per-team cost attribution, adoption metrics by tool and model, and one place to standardize AI configuration across the entire engineering organization.
AI Spend Without Attribution
- An unallocatable invoice. Provider bills arrive as org-level totals with no per-team or per-developer breakdown.
- Unmeasured adoption. You cannot tell a team that has embedded AI in its workflow from one that installed it and stopped.
- Tool sprawl. Five tools solving the same problem, each with its own configuration and risk profile.
- No standard baseline. Every repo configures AI differently, so quality and guardrails vary by whoever set it up.
Numbers, Not Impressions
Cost attribution that maps to org structure
Per-developer, per-team, per-model token and cost tracking with 30-day trends and chargeback-ready exports for finance.
- Model-aware pricing
- Budget thresholds
- CSV export
Adoption metrics
Which teams use which tools, how often sessions run, and how the tool mix shifts over time - measured from actual sessions, not survey responses.
- Session frequency
- Tool mix trends
- Per-team rollups
Standardization you can push
AI Profiles put CLAUDE.md, .cursorrules, Copilot instructions, and MCP allowlists under central management with drift detection.
- Template library
- Fleet-wide push
- Drift reconciliation
One agent, one bill
AI governance, DLP, and EDR-class endpoint protection in one install at per-endpoint pricing, instead of three products with three renewal cycles.
- $9–19 per endpoint
- 14-day trial
- No per-module pricing
The Questions You Can Now Answer
- Which teams are spending the most on AI, and on which models?
- What is our AI cost per developer, and how has it moved over the last 30 days?
- Which AI tools are actually in daily use, and which ones can we consolidate away?
- Are engineering AI standards applied in every repo, or only where someone remembered?
- What would we tell finance if they asked for an AI chargeback breakdown tomorrow?
Put a Number on AI
See per-team cost attribution and adoption data from a live fleet in 30 minutes.
[email protected]kraitos.io