SEO and marketing decision
SEO STACK
A technically capable developer can build a useful subset (warehousing, filtering, LLM query, basic audits) over several weeks, but reproducing the full product (long-term multi-tenant warehousing, polish, prebuilt experiments, token bundling, and integrations) would require more engineering and ops investment.
Visit website↗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
- Import Google Search Console + GA4, store rows in a database, run filtered queries and aggregations, run LLM-powered natural-language queries/answers over the warehoused data, generate/export reports and simple content audits (NLP).
What it still won’t have
- Polished, production UI/UX and integrated task/project management
- Built-in long-term, multi-tenant warehousing at scale and automated backups
- Bundled AI token quotas and in-app usage limits
- Priority support, team seats & enterprise features
- Prebuilt SEO experiments, forecasts and some proprietary analytics optimizations
What remains hard
- Product polish and ongoing maintenance
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 6 seats.
Money you would actually spend
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
Build a minimal self-hosted SEO-warehouse and AI assistant using: Next.js for frontend, Postgres for warehousing, Node.js/Express backend, and OpenAI (or compatible) for LLM. Implement: (1) OAuth-based import of Google Search Console + GA4 with incremental sync and daily scheduler; (2) raw-row storage in Postgres with date partitioning and simple retention; (3) API endpoints and a small UI to run filters, aggregations, saved queries, and CSV exports; (4) an LLM service that accepts a natural-language query, translates to parameterized SQL/aggregations, runs the query, and returns a human explanation; (5) an NLP content-audit endpoint that scores a page for topic salience and outputs suggested improvements; (6) basic report generation (CSV/PDF). Out of scope: multi-tenant billing, large-scale automated backups, advanced forecasting models, integrated project management, and polished analytics dashboards. Include error handling, input validation, retry logic for ingestion, authentication, and unit tests for ingestion, query translation, and audit pipelines.
How we checked
How the score was reached
- Partly verdict base52
- 3 cited sources+3
- Price verified on pricing page+3
- 3/3 assessment runs agreed+4
- Evidence score62
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.
- official productSEO Stack — homepage
- official pricingSEO Stack — pricing
- official docsSEO Stack — features
Integrity checks
What held up, and what did not.
