Attribute every AI cost to the customer or workflow that caused it, enforce budgets before overspend, and see where usage is compressing margin.
Start with a base URL swap. Add business metadata for customer, agent, and workflow attribution. No SDK required.
Get an immediate score across attribution, enforcement, containment, margin visibility, and reconciliation.
Map model usage to the customer, agent, workflow, feature, team, or cost centre that caused it.
Set budgets and policies by organisation, team, agent, or customer. Alert, require approval, or block requests when limits are reached.
Compare customer revenue with attributable AI cost and identify where usage is compressing gross margin.
Every prompt, retry, and tool call is a spend decision made by code in production. For AI features, that happens continuously across your user base. In agentic workflows, thousands of those decisions happen overnight with no human in the loop. By the time finance sees the number, the spend has already happened.
Provider dashboards show totals. They do not show which customer, team, or feature caused the cost.
Which customers are actually profitable after AI costs?
Most teams cannot answer this.
The Spendline assessment evaluates six areas. It produces an immediate score and identifies the controls that are missing.
With business metadata attached, every request is mapped to the customer, workflow, or cost centre that caused it.
Every request becomes a financial event.
Observability tools show tokens. Spendline shows gross margin per customer.
Observability tools primarily report what already happened. Spendline acts in the request path — attributing each call, enforcing budgets and approvals, and applying policy before the cost is incurred.
The difference is financial accountability: approvals, customer economics, reconciliation, and month close — not just another dashboard of spend after the fact.
Engineering installs it for visibility. Finance adopts it for control. The business uses it to protect margins.
During investment diligence, we observed an anonymous company generating approximately $32M in revenue while spending approximately $39M on purchased AI services with no meaningful attribution.
Engineers saw tokens. Finance saw invoices. The board saw the gap too late.
The problem was not simply that AI cost was high. No one could connect it to the customers, teams, or workflows that created it.
AI workloads do not map cleanly to seats. Spendline is priced as a base fee plus a percentage of AI spend under management. If we do not improve your margins by more than we cost, the product should not exist.
Pricing is aligned to AI spend because that is the risk being managed.
Enterprise deployment and integrations are available through scoped pilot agreements.
Customers move to Enterprise because they need deployment, compliance, and workflow features, not because spend crossed a threshold. Enterprise is feature-gated, not spend-forced.
Add guardrails once you see the data.
Swap your base URL. No SDK required. Fast time to first visibility. No commitment until you have seen your real AI cost breakdown.
Currently accepting pilot partners across B2B SaaS and enterprise, especially teams where AI spend is becoming a real cost line.