Social media decision

Social Fetch

A narrow, small-scale scraper + normalized API for a few platforms is feasible to build and run, but matching Social Fetch's 21-platform coverage, reliability, and scraper upkeep is operationally heavy and costly—so paying is reasonable for full production needs.

Visit website
You pay

$29/mo

$348/yr

Read off the official pricing page.

You’d pay instead

$100one-off80 h to build

$200/mo20 h/mo upkeep

On cash alone, building overtakes the subscription at 8 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 Social Fetch alternatives, with the arithmetic →

What a replacement has to do

  • Call heterogeneous platform endpoints, normalize responses into {data,meta}, and bill/consume credits per lookup.

What it still won’t have

  • Coverage and maintenance for 21 platforms and 165 endpoints
  • Engineered reliability (99.8% uptime, hosted throttle handling, and incident tracing)
  • Monitors with signed webhooks and monitor scaling
  • A maintained scraper fleet (DOM-shift fixes, proxy handling, and paid-route accounting)

What remains hard

  • Execution quality99.8% uptime · #3 Product of the Day · 3,200+ developers
Read the build prompt

First-year cost

Keep paying

Paying is—cheaper in year one.

On cash alone, building overtakes the subscription at 8 seats.

Paid seatsseats

Money you would actually spend

Keep paying
—

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 hosted Social Fetch replacement in Node.js (Express) + PostgreSQL + Puppeteer (or Playwright) + a paid residential proxy provider. In scope: (1) an API key protected REST endpoint GET /v1/{platform}/profiles/{handle} that performs a live fetch for X, YouTube, and Reddit; (2) normalize responses into {data, meta} with meta.requestId and meta.creditsCharged; (3) implement simple per-call credit accounting (configurable credits-per-route), logging of raw responses, and a /v1/balance and /v1/whoami free route; (4) basic monitor job scheduler that polls a profile and emits signed webhook on diff; (5) retries, proxy rotation, and clear error codes (lookup_failed, not_found, temporarily_unavailable); (6) tests for normalization, auth, and error flows; (7) Dockerfile and a Terraform script to deploy one small VM + managed Postgres. Out of scope: scaling to 21 platforms, a public SDK catalog, paid billing integrations, and legal/compliance review. Include error handling, request tracing IDs, and automated unit/integration tests.
How we checked5 sources · 3/3 runs agreed · evidence score 64

How the score was reached

  • Partly verdict base52
  • An open-source build was found+5
  • 5 cited sources+3
  • Price verified on pricing page+3
  • 3/3 assessment runs agreed+4
  • Hard moats found in the evidence-3
  • Evidence score64

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 · 5

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 quoted from the page