Analytics and monitoring decision

ArtificialWatch

A competent developer can reproduce the core alerting/watchlist functionality in about a week and maintain it for small-scale use; the product's value-add (phone-call alarm and evaluation harness) can be approximated but not fully replaced without extra engineering.

Visit website
You pay

$12/mo

$144/yr

Read off the official pricing page.

You’d pay instead

$50one-off30 h to build

$25/mo6 h/mo upkeep

On cash alone, building overtakes the subscription at 3 seats.

No open-source build does this yet

Nothing published replaces this one, so a replacement starts from an empty file. Here is what it would have to cover.

What a replacement has to do

  • Poll provider APIs on a schedule, detect a newly-answering model by issuing a test prompt and confirming on a second sweep, send notifications (email/Chrome push free; SMS/phone via paid channels), store and manage user watchlists and per-model routing, display a minimal dashboard with recent detections

What it still won’t have

  • built-in charter pricing and lifetime guarantees
  • the Call/Watchtower specialized evaluation harness and regression history
  • polished product UX and public track record
  • any proprietary subscriptions or negotiated SMS/voice volume discounts

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 3 seats.

Paid seatsseats

Money you would actually spend

Keep paying
—

Subscription price × seats × 12

Build it
—

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 AI-model-launch watcher using Node.js (Express) + Postgres + Redis (for job locks) and a single-worker cron. Core features in scope: 1) a scheduled poller that queries a configurable list of provider APIs every 60s and stores responses; 2) deduplication and confirmation logic that requires a second successful detection before emitting an alert; 3) a test-prompt runner to validate a model answers; 4) user accounts, per-user watchlists, and per-model channel routing preferences; 5) notification integrations: email (SMTP), Chrome push (Web Push), and Twilio for SMS and automated calls; 6) a small React dashboard to manage watchlists and show recent alerts. Out of scope: building a large-scale provider catalog, automated model benchmarking/harness (Watchtower), and Polymarket integration. Require error handling for API failures and rate limits, retries with backoff, unit tests for detection logic, and end-to-end tests for the notification pipeline.
How we checked4 sources · 2/3 runs agreed · evidence score 84

How the score was reached

  • Build verdict base78
  • 4 cited sources+3
  • Price verified on pricing page+3
  • Evidence score84

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! 2 of 3 runs agreed; the verdict is the majority✓ Citations limited to fetched pages! 1 moat recorded