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Free AI cost estimator designed for senior non-technical leaders. Compare API costs using simple business metrics (users, actions, volume) instead of complex code
Over the last year, I've spoken with dozens of VPs, COOs, and founders who all want AI in their workflows. They keep running into the same question: "How much is this actually going to cost us?"
If you have token-level data, the providers have calculators for that. The harder problem is earlier in the process, before any code is written. You have a business case to build, a vendor proposal to evaluate, or a budget to defend, and the number you need isn't buried in a token pricing table.
That gap is where projects stall, or where companies get handed a five-figure API bill they never saw coming.
We built the AI Cost Estimator for the people making those decisions, not the engineers implementing them.
How it works
You input the business metrics you already know: expected monthly usage and the type of tasks they're performing (short customer support chats, long document analysis, or data extraction).
The estimator translates those inputs into, multi-provider cost forecasts, without requiring you to know what a token is.
What you get
Run-rate projections — monthly, quarterly, and annual spend, clearly laid out. Provider comparisons — your costs across OpenAI, Anthropic, Gemini, and open-source models, side by side. Hidden cost visibility — secondary costs like vector storage, processing loops, and guardrail layers are factored in, not buried.
Whether you're budgeting an upcoming AI initiative, want a second opinion on a proposal, or double check your own calculations, give it a spin at galdren.com/aicost.
I'd love your feedback. Which providers or features should we add next?
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About AI Cost Estimator on Product Hunt
“Estimate AI spend before committing budget”
AI Cost Estimator was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #58 on the daily leaderboard. Free AI cost estimator designed for senior non-technical leaders. Compare API costs using simple business metrics (users, actions, volume) instead of complex code
AI Cost Estimator was featured in Analytics (172.7k followers) and Artificial Intelligence (473.1k followers) on Product Hunt. Together, these topics include over 120.9k products, making this a competitive space to launch in.
Who hunted AI Cost Estimator?
AI Cost Estimator was hunted by Michael Vander Donk. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how AI Cost Estimator stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Michael Vander Donk
Hey Product Hunt!
Over the last year, I've spoken with dozens of VPs, COOs, and founders who all want AI in their workflows. They keep running into the same question: "How much is this actually going to cost us?"
If you have token-level data, the providers have calculators for that. The harder problem is earlier in the process, before any code is written. You have a business case to build, a vendor proposal to evaluate, or a budget to defend, and the number you need isn't buried in a token pricing table.
That gap is where projects stall, or where companies get handed a five-figure API bill they never saw coming.
We built the AI Cost Estimator for the people making those decisions, not the engineers implementing them.
How it works
You input the business metrics you already know: expected monthly usage and the type of tasks they're performing (short customer support chats, long document analysis, or data extraction).
The estimator translates those inputs into, multi-provider cost forecasts, without requiring you to know what a token is.
What you get
Run-rate projections — monthly, quarterly, and annual spend, clearly laid out.
Provider comparisons — your costs across OpenAI, Anthropic, Gemini, and open-source models, side by side.
Hidden cost visibility — secondary costs like vector storage, processing loops, and guardrail layers are factored in, not buried.
Whether you're budgeting an upcoming AI initiative, want a second opinion on a proposal, or double check your own calculations, give it a spin at galdren.com/aicost.
I'd love your feedback. Which providers or features should we add next?