Is AI making you margin, or costing it?
Track AI ROI and cost per unit — customer, feature, request — against the value it creates.
- Read-only access
- 14-day free trial
- No credit card required
- 5 min
- setup, per provider
- 90 days
- available history
- Same-day
- anomaly alerts
Spend vs Budget
Forecast $45,000 this month
See the workflow in practice.
Every dollar has an owner.
Auto-tagging rules label costs as they are ingested, matching provider, account, service and project patterns in priority order. By the time someone asks who owns the spend, the answer is already on the data — filterable and groupable in the explorer.
Team plan and above
How it worksEvery model you run, in one lens.
Usage by base model, project and user, in tokens and in API-equivalent value. Estimated and billed usage stay separate, so the numbers never double-count and never pretend to be your invoice.
Business plan
How it worksProduct examples are illustrative. Usage estimates and provider-reported costs are separate measures; availability varies by connected source.
Explore the model viewWhy is this spend hard to control?
- Total AI spend says nothing about whether AI is creating value or eroding margin.
- Provider dashboards report by key and model, never by customer, feature, or workflow.
- Without unit economics, teams cannot tell profitable AI usage from waste.
Know what you are connecting.
The workflow
- 01
StackSpend attributes AI spend to customers, features, requests, and workflows.
- 02
Cost-per-request, cost-per-customer, and margin views show where AI pays off and where it leaks.
- 03
Daily signals and forecasting track unit economics as usage scales.
The source
Billing and usage from your connected providers. Credential types and permission controls vary by provider.
Provider connection guidesThe limits
Provider reporting and scheduled sync determine freshness. Available history and attribution vary by source; review the setup guide for coverage. Alerts notify your team; they do not block requests or enforce a spending cap.
What we track
- Cost per customer, feature, request, workflow
- AI gross margin and ROI signals
- Spend by provider and model
- Pace-to-forecast on AI COGS
- 90 days of history
Who is this for?
- Product and engineering teams that need model-level visibility before AI bills surprise them.
- Buyers consolidating OpenAI, Anthropic, Claude, Cursor, or open-model spend into one operating view.
- Teams that need alerts and forecasting, not just retrospective usage dashboards.
Evaluation checklist
- 01
Start a trial
Open a StackSpend workspace with no credit card required.
- 02
Connect with read-only access
Use the setup guide to connect the provider or workflow with the minimum permissions needed.
- 03
Review the first 90 days
Check history, alerts, anomalies, and forecast so you can decide whether the workflow is worth adopting.
How does StackSpend support this workflow?
Native tools provide provider-specific reporting and controls. StackSpend adds a shared monitoring workflow across connected sources.
Provider usage dashboards and spreadsheets
- Cost reported by key and model, not value units
- No cost-per-request or margin view
- No ROI signal
- Manual, one-off analysis
StackSpend
- AI spend attributed to customers, features, requests
- Cost-per-unit and margin in one view
- ROI tracked as usage scales
- Pace-to-forecast on AI COGS
What do you get when you connect?
- Setup time
- Most teams can connect and validate setup in about 5-10 minutes.
- Access model
- Read-only credentials only. StackSpend does not modify provider resources or billing settings.
- Signals
- Daily Slack or email updates, anomaly alerts, and budget tracking in one workflow.
- History and forecast
- Historical spend context plus pace-to-forecast so overruns are visible before month-end.
Check the details before connecting.
Review connection permissions
Read the provider setup guides before sharing credentials.
Provider setup guidesSee the security details
How credentials, tenant isolation and data handling work.
Security and data handlingTalk to the team
Ask about your stack or requirements before connecting.
Contact StackSpendAbout Andrew DayAI Unit Economics & ROI, answered
When is this workflow useful?
- A power user costs 10x the average and erodes plan margin
- A feature ships with unknown cost-per-customer
- AI cost scales faster than revenue
- The board asks for AI ROI and the number does not exist
How does StackSpend handle AI Unit Economics & ROI?
AI unit economics measures the cost of AI per unit of value — per customer, per feature, per request, per workflow — so teams can tell whether AI spend is profitable or wasteful. StackSpend attributes AI spend to those units across OpenAI, Anthropic, Claude, and more, surfacing cost-per-request, margin, and ROI as usage scales.
Can StackSpend track AI COGS?
Yes. AI COGS is the share of your cost of goods sold that comes from model usage. StackSpend attributes AI spend to features, teams, and customers, so your margin model carries a measured per-customer AI cost instead of an estimate.
What are AI unit economics?
AI unit economics measure the cost of AI per unit of value — per customer, per feature, per request, or per workflow — so you can tell whether AI spend is profitable, productive, or wasteful, rather than just looking at a total.
How do I measure cost per AI request or per customer?
StackSpend attributes AI spend (by model and tokens) to your own units — customer, feature, request, workflow — so cost-per-request and cost-per-customer become live numbers instead of a manual analysis.
How is this different from AI COGS tracking?
AI COGS is the cost side; AI unit economics adds the value/ROI lens — whether that cost is creating margin. StackSpend covers both, tied together in one view.
For the current provider catalogue, see supported integrations.
Tomorrow morning: one number, in Slack.
Connect your providers today and follow spend, budgets and alerts in one place. Review provider permissions before connecting.