Automation and integrations decision

Rankhog

A competent developer can build the core monitoring, drafting, browser-control posting, and warm-up scheduler, but reproducing Rankhog's warmed-account network, proprietary safety/humanization, and polished managed service is hard; keep paying for those or accept a narrower DIY workflow.

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You pay

$99/mo

$1,188/yr

Per seat. Read off the official pricing page.

You’d pay instead

$100one-off80 h to build

$100/mo8 h/mo upkeep

On cash alone, building overtakes the subscription at 2 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

  • Monitor keywords/subreddits, synthesize a context-aware draft via an LLM, present draft for approval (or auto-post), post via a controlled real browser session, track rankings/AI citations and account warm-up schedule.

What it still won’t have

  • Proprietary warmed-account network that helps accounts mutually
  • Built-in proprietary safety and humanizing review pipeline
  • Polished cross-platform native apps and auto-update infrastructure
  • Managed growth service and agency support

What remains hard

  • Network effectsA big community of accounts that help each other.
  • Execution qualityGuarded autopilot
Read the build prompt

First-year cost

Keep paying

Paying is—cheaper in year one.

On cash alone, building overtakes the subscription at 2 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 self-hosted Reddit SEO autopilot using Node.js, PostgreSQL, and Puppeteer. Implement: (1) a Reddit stream worker that watches subreddits/keywords and flags buyer-intent threads, (2) an LLM draft generator (OpenAI/compatible) with a humanizer and safety-check pipeline, (3) a browser-control service using Puppeteer to post/comment/upvote through a user-authenticated browser at human pacing, (4) a small React admin UI to review/approve drafts and view activity logs, (5) an account warm-up scheduler that performs benign interactions over ~2 weeks, and (6) persistent storage for audits, drafts, schedules, and ranking/AI-citation records. Out of scope: managed client-facing agency packaging, multi-tenant billing, native auto-update installers, and building a warmed-account network. Include error handling, retries, rate-limit backoff, unit/integration tests for core flows, and deployment scripts for a single VPS.
How we checked3 sources · 2/3 runs agreed · evidence score 23

How the score was reached

  • Pay verdict base20
  • 3 cited sources+3
  • Price verified on pricing page+3
  • Hard moats found in the evidence-3
  • Evidence score23

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! 2 moats quoted from the page