We kept hitting the same problem while building AI features: token spikes, unpredictable costs, and customers asking for budget controls we couldn’t easily give them.
So we built AI Usage Management at Stigg: a real time usage governance layer you embed directly in your product. It lets you meter AI usage, define limits, set alerts, allocate budgets to users or teams, and enforce policies at the moment of consumption instead of discovering it on the bill later.
Think: - low latency metering - allocation per user, team or feature - alerts and enforcement - credit or outcome based models - drop in admin UI or use your own
If you’re shipping AI features and need to keep spend predictable or meet enterprise governance requirements, early access is open.
Thanks for sharing this. Real-time governance at the moment of consumption is exactly the pain point—most teams only notice the “token surprise” when finance forwards the bill.
Curious: how do you handle streaming responses and tool calls in metering (e.g., partial outputs, retries, multi-step agent loops)? And what’s the typical latency overhead you see in production?
Think: - low latency metering - allocation per user, team or feature - alerts and enforcement - credit or outcome based models - drop in admin UI or use your own
If you’re shipping AI features and need to keep spend predictable or meet enterprise governance requirements, early access is open.
Curious: how do you handle streaming responses and tool calls in metering (e.g., partial outputs, retries, multi-step agent loops)? And what’s the typical latency overhead you see in production?