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Know exactly what every LLM API call will cost before you make it. Pre-flight cost estimation for OpenAI, Anthropic, and Google models.

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| Website | calcis.dev | opalstack.com |
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| Company | Startup from Australia · 1 - 9 employees · 2026 | — |
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In their own words, as submitted to SaaSHub.


Calcis is a pre-flight cost estimator for LLM APIs. Paste any prompt, pick a model, and instantly see token counts, input and output cost breakdowns, confidence intervals, and projections at scale. Supports 24+ models from OpenAI, Anthropic, and Google. Ships with a GitHub Action that...
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As answered by people managing Calcis.dev and Opalstack.
Calcis.dev's answer
Calcis is the only tool built specifically for estimating LLM API costs before you make the call. Other tools track spending after the fact. Calcis tells you what a prompt will cost before you send it, across 24+ models from OpenAI, Anthropic, and Google. It also ships a GitHub Action that posts cost breakdowns directly in your pull requests.
Calcis.dev's answer
Calcis estimates LLM API costs before you make the call. Paste any prompt, pick a model, and see exact token counts, cost breakdowns, and at-scale projections. No other tool does pre-flight cost estimation specifically for LLM APIs. Also ships a GitHub Action that posts cost breakdowns as PR comments, so teams catch spend surprises before they merge.
Calcis.dev's answer
Developers and engineering teams who use LLM APIs in their products and want to understand costs before committing to API spend. Useful for solo developers watching their budget and for teams who need cost visibility baked into their CI/CD workflow.
Calcis.dev's answer
The name comes from the Latin word "calcis" meaning pebble. Roman merchants used to count with pebbles before making transactions. Calculus, calculate, and calculator all trace back to the same root. Calcis does the same thing for modern developers: count before you commit.
Calcis.dev's answer
Next.js, React, TypeScript, Vercel, Clerk for authentication, Stripe for payments, Upstash Redis, and Neon Postgres with Prisma. The cost estimation engine uses a regression model trained on open datasets, with optional LLM-assisted prediction for higher accuracy.
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