For Technology & Data teams
AI Inventory, Usage and Cost Governance
A reliable view of every AI system, model and agent — usage, cost and risk by business context, across your whole AI estate.
AI adoption rarely arrives through one door. Teams pick their own models, orchestration frameworks and tools, and each choice is locally sensible. The result is an estate nobody can describe in full.
Optimisation candidates
The problem
The estate nobody can describe.
Ask most technology leaders how many AI systems are in production and the honest answer is an estimate. Models sit inside applications, agents call other agents, and a meaningful share of usage arrives through tools procured outside IT entirely — shadow AI.
Every downstream problem inherits that gap. Cost cannot be attributed, risk cannot be scored, and duplication goes unnoticed because two teams solving the same problem never appear in the same list.
Accurate for a week
- Manual, so it lags reality
- Misses embedded and shadow AI
- Rebuilt before every audit
Accurate by construction
- Updates as the estate changes
- Finds systems nobody registered
- Always audit-ready
Inventory
A live inventory, not a spreadsheet.
An inventory maintained by hand is accurate on the day it is written. Continuous discovery keeps it accurate afterwards — the only version that supports decisions.
- Every system, model and agent across applications, workflows and orchestration stacks.
- Owner and use case for each, so questions have a destination.
- Environment and data touched, so exposure is visible.
- Model and vendor behind each system, including versions as they change.
Cost
Four levers, once usage is attributable.
Raw token counts tell you little. Consumption mapped to teams, products and use cases turns a vendor invoice into something a budget owner can act on.
Model selection
Where a smaller or cheaper model would serve the same use case just as well.
Routing
Send straightforward requests to lighter models and reserve capable ones for hard cases.
Architecture
Caching, batching and retrieval patterns that remove redundant calls entirely.
Usage patterns
Retire systems nobody uses and consolidate duplicated efforts across teams.
One view
Risk in the same view as cost.
Cost and risk are usually tracked by different teams in different tools, which is why an expensive low-value system and a cheap high-risk one get equal attention. Holding both against the same inventory makes the trade-off explicit — and gives security and compliance colleagues the same picture rather than a reconciled one.
Questions
Technology FAQ
What belongs in an AI inventory?
Every model, agent, application and tool that uses AI — with its owner, use case, environment, the data it touches and the model behind it. Systems bought inside third-party SaaS count, as do automations an employee built. If it can be prompted or can act, it belongs in the inventory.
Why is AI cost so hard to attribute?
Consumption is metered per token or per call at the vendor, while budgets are held per team or product. Without a mapping between AI systems and business context, the invoice arrives as one number nobody can decompose — which makes it impossible to decide what to optimise.
Will this work across multiple model vendors and frameworks?
Yes — the inventory is deliberately vendor and framework agnostic. It sits as a layer across whatever your teams have adopted rather than requiring a single stack, which matters because most estates accumulate more than one.
Is this observability or governance?
Both, and the distinction matters less than the connection. Usage telemetry answers what is running and what it costs; governance decides what should be running and who owns it. Kept separate, the first becomes a dashboard nobody acts on.
How is a live inventory different from a spreadsheet?
A spreadsheet is accurate on the day it is written. Continuous discovery keeps it accurate afterwards, which is the only version that supports decisions — or answers an auditor.
Why hold cost and risk in the same view?
Because they are usually tracked by different teams in different tools, so an expensive low-value system and a cheap high-risk one get equal attention. Against one inventory, the trade-off becomes explicit.
Get Started
See your whole AI estate in one view.
Inventory, usage and cost visibility across models, agents and orchestration stacks.