AI Tokens & Credits Spend Tracker: Unified AI Cost Dashboard

Problem Statement

Teams using multiple AI APIs across different workspaces have no consolidated view of their credit consumption. Without visibility, budgets spiral and it's impossible to identify which projects are driving costs.

Solution

A unified spend tracking dashboard that aggregates token and credit usage across workspaces and projects. Includes monthly trend charts, per-workspace breakdowns, and one-click CSV export — giving teams the visibility they need to manage AI costs effectively.

Our Approach

We designed a normalized data model that could ingest usage records from different AI providers into one consistent workspace-and-project hierarchy, then layered monthly trend charts and per-workspace breakdowns on top so cost owners could answer 'who is spending what, and is it accelerating' without exporting spreadsheets from each provider separately. The build doubled as a submission for the Lovable Builders Sweepstake, so it was scoped to be demo-ready within a tight timeline.

Key Features

• •Normalized usage model that aggregates spend across multiple AI providers and workspaces
• •Monthly trend charts to spot accelerating or seasonal cost patterns per project
• •One-click CSV export for finance and budget-owner reporting

Results

The dashboard turned scattered, provider-specific billing pages into a single trend view, and served as a working proof of concept for how a lightweight internal tool can replace ad-hoc spreadsheet tracking. It was built and shipped within the Lovable Builders Sweepstake submission window.

Challenges

Different AI providers report usage in incompatible units — tokens, credits, requests, and seat-based allowances — so a naive dashboard would have shown apples-to-oranges numbers. We normalized everything into a common cost-equivalent unit before charting, which made cross-provider comparisons meaningful.

Lessons Learned

Unit normalization has to happen at the data-model layer, not the charting layer — trying to reconcile mismatched units in the UI led to inconsistent totals that eroded trust in the dashboard during early testing.

Tech Stack

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