SEO and marketing decision

Serpstat

A small team can build a useful subset (keyword research, basic rank tracking, site audits) but cannot cheaply reproduce Serpstat's proprietary, large-scale dataset and polished product experience — keep paying for full coverage or build a narrow internal tool.

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You pay

$69/mo

$828/yr

Not verified against a pricing page.

You’d pay instead

$100one-off80 h to build

$300/mo20 h/mo upkeep

On cash alone, building overtakes the subscription at 5 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

  • Crawl or fetch SERPs and keyword volumes, store and index keywords/domains, run scheduled rank checks, run on-page/site audit crawls, expose results via a small web UI and simple JSON API

What it still won’t have

  • Breadth and freshness of proprietary index and coverage
  • Scale and historical dataset depth (billions of keywords/backlinks)
  • Polished integrations, white-label reporting, and team features
  • Browser extension and other convenience tools
  • Enterprise support, training, and SLA-backed API

What remains hard

  • Proprietary data1.50B Domains 8.62B Keywords 529B Backlinks 5.6B Keywords suggestions 230 Countries 2.21B Google SERPs
  • Brand trusttrusted by 1.1M+ users
Read the build prompt

First-year cost

Keep paying

Paying is—cheaper in year one.

On cash alone, building overtakes the subscription at 5 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 self-hosted minimal SEO analytics service using: Node.js (Express) backend, Postgres database, Redis for scheduling, and a React UI. In scope: 1) SERP fetcher module with proxy rotation and parsers for Google/Bing that stores SERP snapshots; 2) Keyword and domain storage schema with historical rank table; 3) Scheduler workers to run periodic rank checks and site-audit crawls; 4) Basic site-audit scanner (HTTP status, meta tags, hreflang, canonical, sitemap check) and report exporter (CSV/JSON); 5) REST API endpoints for keyword lookup, rank history, and on-page audit results; 6) Docker-based deployment and documented env/config. Out of scope: building a billion-row proprietary index, browser extension, white-label reporting, and enterprise-grade multi-tenant billing. Include error handling for network/proxy failures, retries, rate-limit backoff, and unit/integration tests for the crawler, scheduler, and API.
How we checked2 sources · 2/3 runs agreed · evidence score 50

How the score was reached

  • Partly verdict base52
  • 2 cited sources+1
  • Hard moats found in the evidence-3
  • Evidence score50

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 · 2

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

! Price not confirmed on the page - this pricing page renders its price in the browser! 2 of 3 runs agreed; the verdict is the majority✓ Citations limited to fetched pages! 2 moats quoted from the page