What is Finitizer Tokenomics
Finitizer Tokenomics shows what your AI spend costs, who owns it, and what it returns, read from your own bills and exports.
Finitizer Tokenomics is Finitizer's product for AI spend. It groups that spend into named workloads, gives each one an owner, and sets what each workload returns beside what it costs.
It is sold on its own, or together with Finitizer CloudIQ on the same login.
Why a bill is not enough#
AI spend arrives through several doors at once:
- provider APIs, such as OpenAI and Anthropic
- cloud token services, such as Amazon Bedrock and Vertex AI
- tools with AI built in, such as GitHub Copilot and Cursor
- rented GPUs, and hardware you own
Each one bills differently, and none of them names the workload the money served. Seeing the spend still does not tell anyone what it returned.
| Survey finding | Share |
|---|---|
| Not confident, or only slightly confident, they can tie AI spend to an outcome their CFO would accept | 3 in 4 enterprises |
| Name proving value as their biggest AI spend challenge | 43% |
| Of those confident in their spend visibility, the share that still cannot show an outcome | 60% |
Source: Tokenomics Foundation, State of Tokenomics, September 2026, 472 respondents, licensed CC BY 4.0.
Tokenomics closes that gap: spend grouped into owned workloads, and each workload's outcomes and value beside its cost.
The seven screens#
| Screen | The question it answers |
|---|---|
| Overview | Who answers for AI spend, what it returns, and what it costs this month |
| AI Value | What each workload returns, as a cost per outcome |
| Efficiency & Routing | How well the tokens are used, and what to change |
| Forecast & Planning | What the next three to twelve months will cost |
| Data sources | Where the numbers come from, and what is not visible |
| Cost Allocation | Who spent it: by team, workload, person, model and more |
| Budgets & Governance | Whether spend is on budget, and what a budget cap cannot catch |
The seven screens describes each one.
Where to start#
- Name an owner. On the Overview, name the person accountable for AI economics.
- Bring in the data. On Data sources, pull the cloud bills and import the provider exports.
- Name the workloads. On Cost Allocation, name each workload and give it an accountable owner.
- Add one outcome. On AI Value, add one outcome metric per workload, then record the first value claim for finance to book.
Next#
- Data sources and access: where the data comes from, who sees what, and what Tokenomics cannot see
- The seven screens: each screen in a short list
See your own AI spend
Finitizer Tokenomics reads AI spend from the bills and exports you already have. Get in touch to see it on your own data.