PromptSpend

Know the tab before you build.
Estimate, compare and understand what an AI feature will cost — from a catalog that re-checks itself every morning, so the numbers are never a year out of date.

→ Open PromptSpend  ·  free  ·  open source  ·  no accounts, no tracking  ·  Writing: What LLM Cost Calculators Get Wrong  ·  August 2026 Price Movement Report

https://github.com/user-attachments/assets/8ddf3e53-2a97-4d86-ac93-d09507c387de

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Why this exists

Every LLM cost calculator on the web has the same failure mode: it is a snapshot. Someone builds it, hard-codes a dozen model prices, and within a few months the entire premise is wrong — the models it compares have been superseded and the prices it quotes no longer exist.

PromptSpend is built the other way round. The pricing pipeline is the product; the calculator is what sits on top of it. Every morning a GitHub Action re-fetches the catalog from independent sources, merges them under an explicit trust order, runs sanity checks, and either commits the result or opens a pull request for a human. Capture patterns are family-level, so a brand-new model version is picked up automatically without anyone touching code.

What it does

• Receipt — copy a fully visible, one-response cost-audit object into an existing AI conversation. It estimates the visible workload, retrieves current PromptSpend prices, names the two largest cost drivers and suggests cheaper models worth testing without pretending price proves equal quality.
• Estimate — describe one interaction (paste your real prompt or move the sliders), set your scale, and see the monthly, yearly, per-user and margin numbers for up to four models side by side.
• Compare — every tracked model on a price-versus-capability value map, plus a sortable catalog you can select from directly, with the source and verification date for every row.
• Learn — seven short interactive lessons on tokens, why output costs more, how wide the price spread is, compounding chat history, hidden reasoning tokens, caching and batching, and how the data pipeline works.
• Data & Alerts — pipeline health, full provenance for every number with a link to the vendor page it came from, what is currently flagged, and four ways to hear about a price change: an Atom feed, the repository's own pull requests, browser push, and an email digest.

Price alerts

Opt-in, and off unless a deployment is configured for them. Browser push stores nothing personal — a push subscription is an opaque URL the browser issues. Email is double opt-in with one-click unsubscribe, and stores your address, the models you follow, and the date you asked; nothing else. Either channel can watch the whole catalog or just the models you pick.

The delivery service is a Cloudflare Worker in worker/, with the push payload encryption written out against RFC 8291 and checked byte for byte against the RFC's own worked example. docs/ALERTS.md has the architecture, the cost model and the domain cutover.

Things it does that most calculators get wrong

How the data stays current

                    ┌─────────────────────────┐
  LiteLLM catalog ──┤                         │
                    │   allowlist → merge →   │──→ public/data/pricing.json ──→ the app
  OpenRouter API ───┤   validate → diff       │
                    └───────────┬─────────────┘
  data/pricing-overrides.json ──┘            └──→ docs/pricing-changelog.md
       (hand-verified, wins)

The trust ladder, in order:

1. Hand-verified vendor rates (data/pricing-overrides.json) — win every conflict.
2. The LiteLLM community catalog — the automated daily feed; thousands of models, updated within days of a release.
3. The OpenRouter API — an independent cross-check, never a source of record. Reseller pricing differs from first-party list pricing, so a disagreement of more than 20% flags the model rather than changing it.
4. Sanity rules — schema validation, non-negative rates, implausibility checks, a hold on any price that moved more than 50% in a single day, a floor on how many rows a source may return, and a cap on how much the catalog may shrink in one run.

A clean diff is committed and deployed automatically. A newly raised flag becomes a pull request — a long-standing disagreement does not re-open one every morning. A run that loses a source or trips a size guard is degraded: it publishes nothing, records why in public/data/sync-status.json, and fails loudly. Either way the change lands in docs/pricing-changelog.md.

Nothing disappears on one bad morning. A model missing from the feed is kept and marked stale, not deleted; retiring one for good is a deliberate edit to data/models-allowlist.json.