SpendGraph tracks every dollar of AI usage across models, customers, workflows, and teams. See where spend comes from, enforce policies in real time, and make unit economics visible before waste compounds.
Most teams can see a provider bill. Very few can answer which customer, feature, workflow, or model is actually creating the cost and whether it should be allowed.
Break spend down by provider, model, feature, team, workflow, environment, and customer so cost can be tied to business activity instead of sitting in one raw bill.
Set thresholds that alert, throttle, downgrade, or block. Catch recursion, model misuse, and spend spikes before they turn into ugly end-of-month surprises.
Map AI cost to customer revenue and identify where margins are healthy, weak, or actively negative. Know what should be optimized, repriced, or blocked.
SpendGraph gives one control surface for cost visibility, policy enforcement, and margin accountability.
Without attribution, teams know what they spent but not which customer, workflow, or feature created it.
Most cost monitoring tells you what happened. You need controls that can react while spend is still unfolding.
Model choice, retries, recursion, and feature growth create hidden cost layers unless they are measured and enforced.
Set thresholds and define what happens next: alert, throttle, downgrade, or block.
See whether expensive models are being used in the right places and whether routing decisions make economic sense.
Understand whether AI-heavy customers are healthy, weak, or actively margin-negative.
Turn raw usage into defensible reporting by customer, project, workspace, and organization.
Capture requests, tokens, traces, environments, and metadata.
Compute cost at the model and request level with pricing context.
Tie spend back to workflows, customers, teams, and features.
Trigger alerts and policy actions when risk thresholds are crossed.
This is for teams moving past experimentation into repeatable workloads, customer-facing AI, internal copilots, agents, and cost accountability.
Understand whether AI features are improving revenue or silently compressing margin.
See where spend comes from, which workflows misbehave, and which model choices are too expensive for the job.
Get a structure that supports budgeting, reporting, chargeback, and more disciplined AI cost governance.
Start with visibility, grow into governance, and move into true profitability and control as AI becomes more business critical.
For solo builders and small AI products that need visibility fast.
For AI teams that need real attribution, governance, and operational control.
For companies where AI cost, chargeback, and margin control are business critical.
Start with visibility, move into enforcement, and build toward real margin intelligence as usage grows.