Billing Management

Overview#

AI cost overruns rarely announce themselves in advance. A single department running high-volume analysis against a premium model can exhaust a quarterly budget in days. The Billing Management panel gives administrators real-time visibility into token consumption and cost patterns, so decisions about model selection and usage quotas can be made with current data rather than end-of-month surprises.

Mermaid diagram
flowchart TD
    A[AI Usage Events] --> B[Token Metering]
    B --> C[Cost Calculation by Model]
    C --> D[Consumption Dashboard]
    D --> E{Budget Threshold?}
    E -->|Warning| F[Alert Sent]
    E -->|Exceeded| G[Usage Blocked / Escalated]
    E -->|Normal| H[Usage Continues]
    D --> I[Forecasting Engine]
    I --> J[Projected Month-End Cost]

Key Features#

AI Usage Monitoring: Track token consumption across all AI models in real time, with breakdowns by model, user, department, and feature. Visual dashboards show current spend, usage trends, and progress against monthly quotas.
Cost Analysis: Real-time cost tracking for the current billing period, including projected end-of-period costs. View breakdowns by AI model provider, platform feature, individual user, and time period from daily through monthly.
Budget Alerts: Configure budget thresholds at the organisation, department, and user level. Receive notifications when spending approaches or exceeds defined limits, with multiple alert stages so teams can act before hard caps are reached.
Usage Forecasting: Machine learning projections show end-of-month costs with confidence intervals, helping administrators plan budgets and identify rising trends weeks before they become a problem.
Usage Pattern Analysis: Identify peak usage hours, seasonal trends, usage spikes, and feature adoption rates to optimise AI model selection and resource allocation across teams.
Custom Reporting: Generate monthly usage summaries, custom filtered reports, and exportable data in PDF, Excel, and CSV formats. Save report templates for recurring stakeholder needs.
Per-User Spending Controls: Set individual user or department spending caps to prevent unexpected cost overruns while maintaining uninterrupted platform access for everyone else.

Use Cases#

Cost optimisation by identifying which AI models deliver the best value for different task types and shifting usage patterns accordingly.
Budget management with proactive alerts and forecasting that prevent billing surprises at period end.
Departmental chargebacks using per-department cost allocation and exportable billing reports for internal accounting.
Feature ROI analysis by correlating AI feature usage with cost and user engagement metrics to focus investment where it delivers results.

Getting Started#

Navigate to Admin > Billing to view the main usage dashboard.
Review current month spend, token consumption by model, and top cost drivers.
Configure budget alerts under Budget Settings with monthly limits and notification recipients.
Generate your first usage report under Generate Report to establish a baseline for future comparisons.

Best Practices#

Check dashboards weekly to catch usage spikes and anomalies before they compound.
Match models to task complexity: cost-effective models for simple queries, premium models reserved for complex analysis.
Start with conservative initial budgets and adjust after the first 30 days once normal consumption patterns are clear.
Apply per-user quotas for teams with variable usage to keep monthly costs predictable.

Integration#

Accounting Systems: Export billing data in standard CSV formats or access via API for integration with your financial systems.
Notification Channels: Alerts via email, Slack, Microsoft Teams, SMS, and PagerDuty.

Availability#

Enterprise Plan: Included
Professional Plan: Included

Last Reviewed: 2026-02-23 Last Updated: 2026-04-14

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