AI FinOps2026-03-22
Why AI Cost Attribution Is Broken (And How to Fix It)
AI cost attribution is fundamentally broken. Learn why provider dashboards and token metrics fall short, and how modern AI FinOps requires multidimensional visibility.
Explain every pound of AI spend. Control every model.
The AI FinOps platform that gives CFOs and engineering teams finance-grade visibility, token-level cost tracking, and governance across every provider. Powered by a low-latency AI gateway.
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Everything you need, at a glance
Month-to-Date Spend
$18,700
of $22,000 budget
Active API Keys
24
across 4 providers
Requests Today
12,847
$0 avg cost/req
Budget Utilisation
85%
$3,300 remaining
Spend by Team
Illustrative dataBudget vs Actual
Illustrative dataSound familiar?
AI spend is a black box
No clear attribution by feature or department. CFOs ask questions nobody can answer.
Costs are rising unpredictably
Token pricing shifts monthly across providers. Without per-model token tracking, finance teams cannot forecast AI cost exposure.
Governance is manual
No enforced model or provider control. Shadow AI usage spreads unchecked across teams.
The AI SpendOps platform
Three pillars that turn AI from an unpredictable cost centre into a controlled, auditable platform. FinOps for AI, built into a proxy you deploy in minutes.
Visibility
Track token usage across every provider and model. See prompt, completion, and cached token costs by team, feature, and environment in real time.
Governance
Enforce allowed models, providers, and dimensions at call-time. Block what shouldn't run.
Accountability
Budget tracking, burn alerts, and chargeback exports. Give finance the numbers they need.
Quick Time to Value
See value in your first week
- Route traffic through the AI SpendOps AI gateway
- Attribute token usage and spend by team, feature, and environment
- See AI costs and token consumption across all providers instantly
- Export finance-ready reports for CFOs and FinOps teams
Provider Mix
Illustrative dataAI FinOps built for CFOs and engineers alike
Spend attribution
- Break down token usage and cost by team, feature, or environment
- See which models and providers drive the most token spend
- Identify cost anomalies before they escalate
Spend by Team
Illustrative dataBudget vs Actual
Illustrative dataBudget tracking
- Set monthly budgets per team or cost centre
- Get alerts at configurable burn thresholds
- Forecast overrun risk before month-end
Audit trail
- Every request, policy decision, and key change logged
- Exportable for compliance and internal audit
- Real-time visibility into policy enforcement
Audit Log
Illustrative data| Time | Event | User | Detail |
|---|---|---|---|
| 14:23:01 | API key rotated | j.chen@acme.com | Key k_01J... rotated |
| 14:18:42 | Policy violation | system | Blocked gpt-4-turbo (not in allowed list) |
| 14:12:15 | Budget alert | system | Backend team at 85% of monthly budget |
| 13:55:30 | Model added | s.patel@acme.com | Added claude-sonnet-4 to allowed models |
| 13:42:08 | Dimension created | m.jones@acme.com | Added 'cost-center' dimension |
| 13:30:00 | Export generated | f.garcia@acme.com | Monthly chargeback CSV exported |
Start simple, expand as you grow
Visibility
See every pound of AI spend
Budgets
Set and enforce team budgets
Multi-provider
Unified view across all AI providers
Chargeback
Cost allocation by department
Compliance
Audit trails and policy enforcement
From the Blog
Latest insights
AI FinOps2026-02-22
AI Cost Classification in the UK: Accounting Segmentation, Governance, and R&D Supportability
Under IFRS and UK GAAP, AI costs should be analysed based on purpose and stage. Clear segregation between development, production, and internal AI usage supports financial reporting integrity and R&D tax supportability.
AI FinOps2026-02-22
Engineering Visibility for AI: Why Tracking Token Usage by Feature, Region, and Product Matters
Engineering teams need deeper visibility into AI usage. Learn how tracking token consumption across features, products, regions, and environments helps identify inefficient prompts and control AI costs.
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