Stop unexplained AI invoices. Attribute token spend across platforms including AWS, GCP, and Azure
Kanawai AI FinOps turns multi-cloud token consumption into departmental chargeback lines and model right-sizing recommendations, so CFO and CAIO share one ledger. This pilot is for finance and AI leaders who need transparency into AI spend, utilization and optimization.
- Multi-cloud token metering
- Built for CFO and CAIO
- No cost for the pilot period
What is an AI FinOps pilot with Kanawai AI?
A Kanawai AI FinOps pilot is a no-cost 30-day engagement that aggregates AI token consumption across cloud providers and select third party platforms (e.g., Vercel, HubSpot, Datadog), then attributes spend to user, team, and department, and delivers model right-sizing recommendations with an executive briefing, so finance evaluates AI FinOps with a real ledger, not a demo script.
Why FinOps and finance run this pilot
Cloud FinOps taught you to attribute infrastructure. AI broke the model: tokens span AWS, GCP, Azure, and SaaS copilots, often on shared endpoints native bills cannot split by department. CFOs get growth they cannot explain. CAIOs cannot prove which teams should keep frontier models.
Kanawai aggregates AI token consumption across cloud service providers, meters usage by model, person, and department, and produces model optimization and cost intelligence for CFO and CAIO.
Who this pilot is for
- CFOs and FinOps leads who need AI token spend by department
- CAIOs accountable for model cost vs outcome
- Platform teams stuck between gateway metering and finance’s P&L
- Leaders comparing AI FinOps tools, gateways, and governance platforms
Not the primary fit: CISO-led shadow AI / governance-first evaluations. See the AI governance & security pilot.
What you get in 30 days
- 1
Multi-cloud token visibility
Usage aggregated across connected providers, as scoped in week one.
- 2
Attribution
Tokens and cost by model, person, and department where identity mapping exists.
- 3
AI consumption optimization view
Where spend concentrates, and which workloads are candidates for right-sizing.
- 4
Model routing / right-sizing recommendations
Reserve frontier models for complex work and use cheaper models where quality allows. These are recommendations, not automatic changes, unless you ask.
- 5
Executive FinOps briefing
A board-ready narrative of who spent what, on which model, in which cloud.
How the no-cost pilot works
| Week | Focus | Outcome |
|---|---|---|
| 1 | Connect usage feeds | Initial multi-cloud / vendor token visibility |
| 2 | Attribution | Department / person / model views; gap list |
| 3 | Optimize | Right-sizing candidates; budget threshold discussion |
| 4 | Briefing | Executive FinOps readout + go / no-go recommendation |
Commercial terms: 30 days, no cost for the pilot. No purchase obligation. Paid plans are a separate decision after the briefing.
AI FinOps in one paragraph
AI FinOps is the practice of attributing every dollar of AI token and model spend to the user, team, and workload that consumed it, across clouds and vendors, then using that attribution to forecast cost and right-size models without guessing. It extends FinOps principles to tokens and inference, and works best when the same inventory that finds shadow AI also feeds the ledger.
Why native cloud bills are not enough
| Cloud | Typical native hook | Limit for departmental AI FinOps |
|---|---|---|
| AWS | Bedrock Inference Profiles, CUR tags | Shared endpoints still blur org-chart chargeback |
| Azure | Resource tags, separate resources | Call-level split on shared endpoints is weak |
| GCP | Labels, billing export / projects | Labels are not person-level token attribution on their own |
A metering and attribution layer, tied to identity and inventory, is what turns invoices into chargeback.
AI FinOps vs LLM gateway vs cloud FinOps
- Cloud FinOps
- Optimizes infrastructure tags and commitment. Tokens and agents need different units.
- LLM gateways
- Route and rate-limit the traffic you already send through them.
- AI FinOps (Kanawai)
- Attributes cost to people and workloads and connects to the same system of record used for governance. Routing is one lever, not the whole job.
What this is not
- Not a promise of a fixed savings percentage
- Not “install a gateway and you’re done”
- Not a 30-day rewrite of your entire FinOps practice
Frequently Asked Questions
What is AI FinOps?
AI FinOps attributes AI token and model spend to users, teams, and workloads across vendors and clouds, then uses that ledger to forecast and right-size models. It applies FinOps discipline to tokens, not only VMs and storage.
How do CFOs attribute AI token spend across AWS, GCP, and Azure?
They need usage feeds from each provider, mapped to identity and department, plus a normalized ledger. Native tags help but often cannot split shared endpoints; a dedicated AI FinOps layer fills that gap.
Is the pilot really no cost?
Yes. The 30-day pilot is no cost. Any paid Kanawai subscription is optional after the executive briefing.
Will you change our model routing in production?
Not without agreement. The pilot emphasizes visibility, attribution, and recommendations. Production routing changes are a scoped decision, not a default.
How is this different from gateways or cloud FinOps tools?
Many tools excel at bills, gateways, or cloud allocation. Kanawai’s FinOps story sits with inventory and governance: attribute spend and know which AI systems, including unsanctioned ones, created it.
What do we need to start?
A finance or CAIO sponsor, and one scoping call. We deploy the pilot as quickly as 30 minutes.
Request the AI FinOps Pilot
We'll confirm scope in one call, then get to work.
Book your scoping callPrefer email? contact@kanawai.ai
Start your 30-day no-cost AI FinOps Pilot
Attribute AI token spend across clouds, see departmental consumption, and leave with an executive briefing.