Monitor Hugging Face usage — Inference Endpoints, Spaces, Jobs, and GPU hardware — tied to spend, with idle-resource visibility.
StackSpend monitors Hugging Face usage across Inference Endpoints, Spaces, and Jobs, tying GPU hardware and runtime to cost. A daily signal and anomaly detection flag GPU-backed resources that run without matching traffic — so idle GPU usage is visible.
How it works in practice
StackSpend tracks usage by endpoint, Space, Job, and hardware type, tied to cost.
A daily signal surfaces usage; anomaly detection flags idle or spiking GPU resources.
Hugging Face usage sits beside your other AI spend in one view.
When this use case fires
A GPU endpoint is left running after testing
A Space stays on after a demo ends
A Job runs longer than planned
Traffic shifts from prototype to production
Hugging Face GPU usage is easy to leave running after testing.
Usage and cost are hard to tie to a specific endpoint or hardware type.
There is no signal when a GPU resource runs idle.
How StackSpend does this
Hugging Face billing views is built for different jobs. Here is what StackSpend adds.
Hugging Face billing views
- Usage hard to tie to endpoint or hardware
- No idle-resource signal
- No anomaly alert when usage spikes
- No combined AI view
StackSpend
- Usage by endpoint, Space, and hardware type
- Idle GPU resources surfaced
- Anomaly detection per resource
- Daily signal in one AI view
What we track
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.
Frequently asked
How do I monitor Hugging Face usage?
Can I find idle GPU usage?
What does StackSpend track for Hugging Face?
Set it up in 5 minutes. Know by tonight.
Connect your providers with read-only access. Hugging Face Usage Monitoring starts from day one — no manual setup, no threshold tuning required.