Usage and cost, finally on the same chart.
Tokens and model mix across every provider, tied to the dollars each one drives.
- 5 min
- setup, per provider
- 90 days
- history, instantly
- Same-day
- anomaly alerts
- Read-only access
- 14-day free trial
- No credit card required
Daily Spend by Provider
$24,321 total
How does StackSpend handle AI Usage Monitoring?
StackSpend monitors AI usage across OpenAI, Anthropic, Claude, Cursor, Hugging Face, and Grok — tracking requests, token volume, model mix, and the cost they drive. A daily signal plus anomaly detection shows when usage changes the day it happens, so you can act before the invoice.
How does it work in practice?
- 01
StackSpend ties AI usage signals (requests, tokens, model mix) directly to spend across every connected provider.
- 02
A daily signal surfaces usage and cost together; anomaly detection flags when the token/request ratio or model mix shifts.
- 03
Pace-to-forecast turns a usage trend into a month-end number before it becomes an overrun.
What makes this work?
Every 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 worksEvery 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 worksSee this running against your own bill by tomorrow morning.
Read-only · 5 minutes per provider
Who uses this?
- 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.
What does StackSpend track?
- Requests and token volume by provider
- Model mix and token/request ratio
- Cost tied to usage signals
- Daily signals and anomaly alerts
- 90 days of usage and cost history
When does this use case fire?
- Average tokens per request climb after a prompt change
- Retries or background agents quietly multiply request volume
- A model default switches to a premium model
- A launch multiplies usage faster than the team notices
AI usage and cost are tracked in different places, so a usage spike is only connected to its cost after the fact.
Native dashboards show usage per provider, never the combined picture across an AI stack.
Usage shifts — longer prompts, more retries, a new model default — go unnoticed until spend reflects them.
How does StackSpend do this?
Provider usage dashboards is built for different jobs. Here is what StackSpend adds.
Provider usage dashboards
- Usage shown separately from cost
- One provider at a time
- No alert when usage patterns shift
- No forecast from a usage trend
StackSpend
- Usage and cost together across all AI providers
- Anomaly detection on token/request ratio and model mix
- Daily signal instead of manual checks
- Pace-to-forecast from usage trends
Native tools show you last month. StackSpend tells you tomorrow.
AI Usage Monitoring starts from day one — no manual setup and no threshold tuning required.
Read-only access · Flat plans, never a % of your bill · No credit card required
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.
AI Usage Monitoring, answered
How do I monitor AI usage and cost together?
StackSpend ties AI usage signals — requests, token volume, and model mix — directly to the spend they drive across OpenAI, Anthropic, Claude, Cursor, Hugging Face, and Grok. A daily signal surfaces usage and cost in one view instead of separate dashboards, and anomaly detection flags when the token-per-request ratio or model mix shifts, so a usage change is connected to its cost the day it happens.
What is AI usage monitoring?
AI usage monitoring is the practice of tracking requests, token volume, and model mix across AI providers and connecting those usage signals to spend. Because usage and cost are usually tracked in different places, a spike is only linked to its bill after the fact. Usage monitoring closes that gap so longer prompts, more retries, or a premium model default are visible as they change cost, not weeks later.
How does StackSpend attribute AI usage to what is driving cost?
StackSpend breaks AI usage down by provider and model and exposes the token/request ratio and model mix, then attributes the resulting cost by provider, model, project, and key with tagging to team, product, or feature. That shows exactly which model or workload is driving token volume, so a shift toward a premium model or a background agent multiplying requests is identifiable rather than hidden in a single total.
Can I forecast cost from an AI usage trend?
Yes. StackSpend turns a rising usage trend into a month-end number with pace-to-forecast, so climbing tokens per request or growing request volume become a projected overrun before it lands. Anomaly detection tuned to bursty AI bills flags the usage shift same-day and names the likely driver, giving you time to act while the trend is still correctable.
Tomorrow morning: one number, in Slack.
Connect read-only today. AI Usage Monitoring starts from day one — no manual setup, no threshold tuning required.