AI assistants and search decision

Sheeter.ai

A single competent developer can build and maintain a service that reproduces the core Sheeter offering (generate Excel/Sheets formulas via an LLM) in about a week; the vendor's durable moats aren't evident from the pages provided.

Visit website
You pay

$1.99/mo

$24/yr

Read off the official pricing page.

You’d pay instead

$100one-off32 h to build

$20/mo6 h/mo upkeep

On cash alone, building overtakes the subscription at 15 seats.

Open-source builds that already do this

Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need - the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship. All Sheeter.ai alternatives, with the arithmetic →

What a replacement has to do

  • User enters a natural-language formula request → backend sends a prompt to an LLM → LLM returns a formula string → UI displays formula, decrements credit, user copies or downloads the formula/add-on.

What it still won’t have

  • Any proprietary models or undisclosed internal prompt/data optimizations the vendor may use
  • Polished UI/UX and branding polish present in the hosted product
  • Vendor-provided lifetime plan or commercial support
  • Hosted add-on distribution and possible one-click install experience

What remains hard

  • Product polish and ongoing maintenance
Read the build prompt

First-year cost

Keep paying

Paying is—cheaper in year one.

On cash alone, building overtakes the subscription at 15 seats.

Paid seatsseats

Money you would actually spend

Keep paying
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Subscription price × seats × 12

Build it
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AI build —APIs + hosting —

Time you would spend

—

—

What you would spend

What we assumed

The verdict above measures whether you could build it. This one is only about money.

Runnable build prompt

Not run yet
Build a minimal self-hosted Sheeter replacement using Next.js for the frontend, Node.js + Express for the backend, Postgres for storage, and the OpenAI API for generation. In scope: (1) a landing page and authenticated UI with an input box for natural-language requests and a results panel, (2) a backend endpoint that formats prompts, calls OpenAI (or another LLM), validates/sanitizes model output into Excel/Google Sheets formula syntax, and returns the result, (3) simple user accounts and per-user monthly credit counters (50 credits default), (4) a copy-to-clipboard button and an export/download option, (5) basic admin pages to view usage. Out of scope: building a commercial payments integration beyond a single Stripe plan, advanced add-on packaging for the Google Workspace Marketplace, and training any custom models. Require: input validation, output sanitization (reject/flag outputs that contain unsafe code), rate-limiting, unit tests for prompt-to-formula mapping, integration tests for the generation API, and error handling/logging for failed LLM calls.
How we checked4 sources · 3/3 runs agreed · evidence score 93

How the score was reached

  • Build verdict base78
  • An open-source build was found+5
  • 4 cited sources+3
  • Price verified on pricing page+3
  • 3/3 assessment runs agreed+4
  • Evidence score93

The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.

How scoring works →

Cited sources · 4

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

✓ Price read off the page✓ 3 independent runs, one answer✓ Citations limited to fetched pages! 1 moat recorded