Put a team's name on
every AI dollar
When ML spend arrives as one blended number, no team owns any of it and finance can't plan around it. MLCostIntel allocates AI/ML spend to teams, projects, and environments, shared platform costs included, and it doesn't require six months of tagging cleanup before the numbers are usable.
Why ML cost allocation keeps failing
Tags get forgotten faster than they get fixed
Every new experiment, notebook, and endpoint is a chance to skip a tag, and ML teams create a lot of all three. An allocation scheme that depends on perfect tagging usually looks fine in month one and has visible holes by month three.
Shared platforms defy simple splits
GPU clusters, feature stores, and orchestration serve five teams at once. Splitting the bill evenly punishes light users and hides heavy ones.
Finance and engineering see different numbers
Finance works from the AWS invoice; engineering works from utilization dashboards. Monthly reconciliation meetings burn hours and end in estimates.
Nobody optimizes a bill they never see
A team that never sees its own spend has no reason to clean it up, and no way to know if it did. Most savings initiatives stall here, before any actual optimization work starts.
How MLCostIntel allocates ML spend
Attribution uses every signal available — ML tags, resource naming, account structure, and Kubernetes workload data — and shows you exactly what's still unallocated so coverage improves instead of decaying.
- ✓ Team, project, and environment attribution — driven by ml:team, ml:project, and ml:environment tags plus naming and account signals
- ✓ Consumption-based shared cost allocation — shared GPU clusters and platform services split by actual usage, not headcount
- ✓ Untagged spend surfaced explicitly — a visible, shrinking bucket instead of a silent "other" category
- ✓ Showback and chargeback ready — start with visibility, graduate to billing teams when the numbers have earned trust
- ✓ Executive-ready reports — finance gets month-end reports without dashboard logins (Standard tier and above)
- ✓ One source of truth — engineering and finance work from the same attributed numbers, so reconciliation meetings get short
What teams get out of it
Of spend accounted for
Allocated to a team or explicitly flagged as unallocated — nothing hides
Saved every month
Month-end ML cost reconciliation replaced by a standing report
Based shared-cost splits
Platform costs allocated by consumption, defensible to every team lead
Source of truth
Finance and engineering working from the same attributed numbers