Analytics and monitoring decision

Nightwatch

A small, useful rank-tracking + AI-visibility workflow can be built and self-hosted using prior open-source tools, but Nightwatch's proprietary long-term historical data, large localized access infrastructure, and SOC2/enterprise features are not reproducible by a lone developer, so keep paying for scale or build a limited in-house alternative.

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Subscription$79/month ✓ verified
Initial build80 hours
Monthly upkeep20 hours + $100
Evidence3/3 runs agree

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

  • Collect SERP HTML for keywords, parse positions and SERP features, store raw snapshots, run AI prompt queries against LLMs and detect citations, surface results in a dashboard and exportable reports.

What it still won’t have

  • 54,000+ localized access points and their scale
  • 13 years of historical ranking data
  • Built, verified 99.9% accuracy and the proprietary infrastructure that claims it
  • Built-in integrations, SLA, and enterprise features (SSO, dedicated infra, priority support)

What remains hard

  • Proprietary dataNightwatch is the only platform built on 13 years of ranking data that connects both — because AI cites what ranks.
  • Infrastructure at scaleproprietary infrastructure of 54,000+ localized access points — not shared proxy pools — which is why accuracy reaches 99.9%.
Read the build prompt

First-year cost

Keep paying

Paying ischeaper 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 minimal SEO rank tracker in Node.js (Express) + Postgres + React. Scope: (1) a worker that fetches Google SERPs for a provided list of keywords+locations, stores raw HTML in S3-compatible storage, parses rank and SERP features into Postgres; (2) a background scheduler to run daily checks and support on-demand refresh; (3) an integration that sends a concise prompt to OpenAI (or configurable LLM) to simulate an AI answer for a keyword and stores whether the brand/domain is cited; (4) a lightweight React dashboard showing keyword positions, snapshot history, and CSV/PDF export; (5) a REST API to fetch keyword history. Out of scope: enterprise-scale geo-grid (54k nodes), multi-engine scraping at massive scale, and multi-year historical backfill. Include error handling for network failures, retries, basic rate limiting, unit tests for parsers, and end-to-end tests for the core worker and API.
How we checked3 sources · 3/3 runs agreed · evidence score 21

How the score was reached

  • Pay verdict base20
  • 3 cited sources+3
  • Price verified on pricing page+3
  • 3/3 assessment runs agreed+4
  • Hard moats found in the evidence-9
  • Evidence score21

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 →

Integrity checks

What held up, and what did not.

✓ Price read off the page✓ 3 independent runs, one answer✓ Citations limited to fetched pages! 2 moats quoted from the page