I built AIBillingDashboard to help founders and teams track and optimize all of your AI service expenses.
Hey IH đź‘‹
Companies are hemorrhaging money on inefficient AI usage without even realizing it.
After watching countless teams, and myself, struggle to manage ballooning AI costs across multiple platforms (juggling between billing portals, losing track of spending, getting surprised by bills), I decided to build.
After ~1 week, I launched (first launch) an MVP for AIBillingDashboard.com.
What I built:
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A unified dashboard that tracks spending across all major AI providers (OpenAI, Claude, Gemini, Tavily, Pinecone, Make.com, n8n, HuggingFace, etc).
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Cost comparison tools that show which models give best value for specific use cases
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Usage analytics that identify cost-saving opportunities across your AI stack
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Billing cycle tracking and payment alerts to avoid surprise charges
I'm essentially replacing the chaotic spreadsheets and custom scripts that I and teams use to track AI spending with something more simple / straightforward.
Additionally, I built this because while AI adoption is skyrocketing, cost management tools haven't really kept pace, is extremely siloed, and/or create new points of failure such as having usage/analytical requests sent out first to track usage before sending out the prompt for completion.
Would love feedback from the community and/or any AI power users out there!
Anyone else building tools for the AI ecosystem? What cost management challenges are you facing?
This is a solid MVP and solves a very real pain point . AI billing can get out of hand fast, especially with multi-platform usage.
From a product perspective, I'm curious how you're prioritizing which integrations to build next, are you basing that on user demand, usage data, or another signal?
As a PM working with early-stage teams, I’ve seen how visibility into these hidden costs can be a real game changer. Would love to chat or support if you’re looking to validate use cases or expand features. Great work on the launch.
Thank you! To answer your question, at the moment it depends on either 1) personal research on AI services other AI power users are using and 2) if they submit a feature request on our website
That makes sense, especially at MVP stage where signal quality can vary. Tying personal research with user-driven requests is a smart move.
Curious if you’ve seen any unexpected integration requests yet? Sometimes those edge cases reveal interesting use cases or potential pivots. Loving the direction so far
Cool idea, I could see it being valuable to tie this data with performance data to measure both cost of different models with performance of each model and downstream user behavior.
Your landing page is well done! How did you go about creating it and the pricing section? Did you use a template?