SEO and marketing decision
Semrush
A competent developer can build a useful subset (keyword tracking, basic backlink checks, LLM-driven briefs) using open-source components, but Semrush’s proprietary datasets and large-scale crawling are durable advantages you cannot reproduce, so keeping Semrush is reasonable for full-scale needs.
Visit website↗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 Semrush alternatives, with the arithmetic →
What a replacement has to do
- Provide keyword and competitor research, daily rank checks, basic backlink discovery, and prompt-level AI visibility monitoring for a small set of sites/keywords.
What it still won’t have
- Semrush’s proprietary, large-scale datasets (keywords/backlinks/geo coverage)
- Breadth and freshness of historical SEO and backlink data
- Polished enterprise UI, integrations, and scheduled reporting features
- Scale and reliability of Semrush-hosted daily crawls and APIs
What remains hard
- Proprietary data
The world's most powerful traffic, visibility, and market dataset. Inside your AI assistants.
- Infrastructure at scale
28B Keywords More keywords means more ways to win.
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 2 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 self-hosted minimal SEO & AI-visibility service using Python (FastAPI) + Postgres + Redis (jobs) + React for UI. Core features in scope: 1) scheduleable keyword + prompt checks that fetch SERP snapshots and AI-answer outputs for N keywords/prompts, 2) store historical ranks and visibility metrics in Postgres, 3) basic backlink discovery by crawling referrers or using public link APIs, 4) keyword difficulty and traffic estimate heuristics, 5) integration with an LLM API to produce content briefs and score pages. Out of scope: matching Semrush’s full datasets, global scale crawling, enterprise reporting/white-labeling, and paid integrations. Include background workers, error handling, retry logic, request rate limits, tests for API endpoints and workers, and docker-compose deployment scripts for a small VPS.
How we checked
How the score was reached
- Partly verdict base52
- An open-source build was found+5
- 4 cited sources+3
- Price verified on pricing page+3
- Hard moats found in the evidence-6
- Evidence score57
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 · 4
Every page the run actually retrieved.
- official productSemrush (homepage)
- official pricingSEO & AI Search Plans and Pricing | Semrush
- official docsSemrush features
- open sourceAICMO/ai-cmo (repo)
Integrity checks
What held up, and what did not.
