Social media decision

UnfollowersTrack

A competent developer can build a useful self-hosted replacement (scraping, snapshots, diffing, UI, payments) in a few weeks; the vendor's main advantages are scale, mobile apps, and brand rather than proprietary data or hard-to-replicate infrastructure.

Visit website
You pay

$14.99/mo

$180/yr

Read off the official pricing page.

You’d pay instead

$100one-off50 h to build

$50/mo6 h/mo upkeep

On cash alone, building overtakes the subscription at 4 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

  • Periodically scrape a public Instagram profile's follower and following lists, store timestamped snapshots, compute diffs between snapshots to surface unfollowers and not-following-back, and present results in a web UI with optional scheduled scans and browser push alerts.

What it still won’t have

  • Polished native mobile apps (iOS/Android) and app-store presence
  • High-scale operations and dataset built from millions of users
  • Brand, reviews, and marketplace reach that drives organic installs
  • Ongoing accuracy/anti-bot countermeasures and edge-case scraping rules

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 4 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 an unfollower-tracking web app using: Python (FastAPI) backend, Postgres database, Redis for job queue, a worker (RQ or Celery), and a minimal React frontend. In-scope: (1) a scraper module that fetches public Instagram follower/following lists with pagination, rate-limit/backoff, and retries; (2) Postgres schema and API endpoints to store timestamped snapshots and to compute diffs producing unfollower and not-following-back events; (3) a React UI to enter a username, trigger scans, view follower/following lists, view unfollower history, and export CSV/JSON; (4) a scheduler/worker to run periodic scans and send browser push notifications when new unfollowers are detected; (5) Stripe integration for subscriptions and gating saved histories; (6) tests for scraper, diff logic, and API endpoints, and error handling and logging for all external calls. Out of scope: native mobile apps, large-scale multi-tenant autoscaling, and advanced engagement ranking beyond recent likes. Provide unit and integration tests, retry and circuit-breaker behavior for scraping, and a README with deployment instructions (Docker + docker-compose).
How we checked3 sources · 2/3 runs agreed · evidence score 58

How the score was reached

  • Partly verdict base52
  • 3 cited sources+3
  • Price verified on pricing page+3
  • Evidence score58

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 · 3

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