How I Built Spendsight to Track Multi-Model AI API Spend (Without Monthly SaaS Subscriptions)

When teams start building with multiple AI models across OpenAI, Anthropic, Google Gemini, and Azure OpenAI, tracking where the budget goes gets messy fast.

Native provider dashboards give you raw token counts, which makes it tough for engineering leads and founders to answer two simple questions:

1. Which team or feature is actually burning our AI budget?
2. Is that AI workflow generating real ROI or losing money?

What Spendsight Does

I built Spendsight to unify multi-model AI usage into one clean financial dashboard:

• Unified Multi-Provider Tracking: Connect OpenAI, Anthropic, Google Gemini, and Azure OpenAI side by side.
• Cost-Per-1K-Tokens Comparison: Instantly compare spend, request volume, and token efficiency across models (for example, seeing how gpt-4o or gemini-3-flash compares against claude-3-7-sonnet per 1,000 tokens).
• Team & Project Attribution: Break down every dollar by team, project, or client and export clean CSV and PDF finance reports.
• Built-In ROI Logger: Measure hours saved and direct revenue against your exact AI API spend.

Try the Live Interactive Demo (No Signup Needed)

You can explore a full populated multi-provider workspace right now without creating an account or entering any API keys:
👉 Open the Live Spendsight Demo

Fair One-Time Pricing (Zero Subscriptions)

Because you connect your own API keys, I skipped recurring monthly fees:

• 24-Hour Free Trial: Test live API sync with your own keys free for 24 hours.
• Solo Founder Lifetime Pass — £15 (~$19) one-time: Pay once, own forever.
• Team & Agency Lifetime Pass — £29 (~$37) one-time: Unlimited teams, projects, and automated Slack budget alerts.

Would love honest feedback from fellow developers and builders—what metrics or alerts do you wish your current AI billing dashboard had?