Catch over-budget runs before they bill
Cost Governance for AI Workflows
Budget before you run. Track every token across every model. Set per-flow limits and let the platform enforce them - so cost overruns are caught before production.
Key Capabilities
Pre-Run Cost Estimation
Before execution starts, the estimator walks the flow graph, counts modules and expected tokens, and returns a min/max cost range based on current provider pricing.
Per-Token Metering
Every LLM call logs input tokens, output tokens, and cost. Drill down from flow-level totals to individual module calls in a single trace view.
Per-Flow Budget Limits
Assign a cost budget to any flow. The platform tracks cumulative spend and pauses execution automatically when the limit is reached.
Cost-Optimised Routing
The model router selects providers by cost, latency, or quality. Cost-priority mode picks the cheapest model that meets the quality threshold.
Usage Credits & Billing
Prepaid credit packages, automatic deductions on each run, and org-level spend dashboards. Every transaction recorded in a tamper-evident ledger.
Alerting & Anomaly Detection
Set spend alerts per flow, per team, or per org. The platform flags runs that exceed historical cost patterns and notifies owners.
How It Works
Estimate
Submit a flow for cost estimation. The platform returns a projected range before any tokens are spent.
Budget
Set a per-flow or per-org budget. Choose whether to warn or hard-stop when the limit approaches.
Route
The model router picks the cheapest provider that meets quality and latency requirements for each module.
Track
Every run logs token counts, model used, and cost. Dashboards show spend by flow, team, model, and time period.
Technical Stack
Ready to build production
Self-host in minutes with Docker, or use the cloud. Either way, you own your data and your models.