AI spend held to budget, team by team
Cloudaware collects every AI charge across AWS, Azure, Google Cloud and Anthropic and gives each bill line an owner from the CMDB, so budgets and alerts reach the team that can act on them.
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Model costs managed like any other line in the cloud budget
AI service charges from every cloud land on an application, a cost center and an owner from the CMDB, including charges from shared deployments.
Monthly forecasts per team show an overrun while the annual plan still has room to adjust.
Unexpected jumps in token usage or cost reach the owning team while the month is still open.
Commitments planned on actual usage and cost per million tokens show each team where the saving is.
Every AI service charge assigned to an application and an owner
The owner, application and cost center come from the CMDB, the records security and ITSM teams already maintain.
- Azure OpenAI costs split by token type and AI account
- Shared deployments divided across the applications that use them
- Untagged charges kept visible as unallocated
Each team's token costs held to its budget
Token prices fall while bills grow. Forecasts per team show the overrun before month-end.
- Budget, actual and month-end forecast for every team
- Forecasts from historical trends and seasonality
- Anomaly alerts routed to the owning team
Only 20% of organizations say they could trace a doubled AI bill to its cause within hours. Harness, State of AI in FinOps 2026
Savings decided by the team that owns the workload
Each team sees its own model costs and unit cost, so the team that runs the workload decides what to cut.
- Showback and chargeback with a separate line for every team
- Cost per 1M tokens tracked month over month
- Commitments planned from historical usage: Reserved Instances, Savings Plans and reservations
Respondents estimate 26% of their AI spend is waste. Harness, State of AI in FinOps 2026
Capabilities behind the numbers
AWS, Azure, Google Cloud and Anthropic billing in one cost model.
Tokens tracked next to the cost they generate.
Costs split by token type, input and output, and by AI account.
Usage and cost trends for budgeting and allocation.
Costs mapped to business units, cost centers, applications, teams, customers and shared-cost pools.
Budgets per cloud, account, business unit, application or tag group, with forecasts from historical trends and seasonality.
Unexpected changes in cost or usage flagged and routed to the owning team.
Reserved Instances, Savings Plans and reservations planned from historical usage.
Team-level reports, with scheduled exports and APIs to BI, ITFM and ERP systems.
