SEO and marketing decision
Sonar - ASO tool
A competent developer can build a useful, smaller ASO tool covering keyword suggestions, basic difficulty scoring, and simple rank tracking in about a week, but reproducing Sonar's long-term data quality, proprietary difficulty tuning, AI/MCP integrations, and enterprise polish would be difficult without ongoing investment.
Visit website↗$12.42/mo
$149/yr
Read off the official pricing page.
$100one-off40 h to build
$0/mo6 h/mo upkeep
On cash alone, building overtakes the subscription at 1 seat.
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
- Scrape store autocomplete and app metadata, compute popularity and difficulty, run daily rank-tracking jobs, expose a REST API and simple dashboard for queries and history.
What it still won’t have
- Sonar's proprietary difficulty-calibration and ongoing algorithm tuning
- Polished long-term historical data storage and scaling (unlimited historical retention guarantees)
- Built-in AI/MCP integrations and bundled agent credits
- Prebuilt revenue-estimate model and the product's calibration against crowdsourced signals
- Enterprise features such as white-label reports, priority rank refresh, and multiple team seats
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 1 seat.
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 lightweight ASO research service using Node.js (Express) + PostgreSQL + React. Core features in scope: (1) crawlers that fetch App Store autocomplete and iTunes Search API results and Google Play autocomplete, normalize and store keyword suggestions; (2) a module that computes a 0–100 popularity and difficulty score from top-N ranking apps' metadata (title presence, review counts, installs) and stores results; (3) a daily scheduler (cron on a small server or Heroku/Render job) to run rank-tracking for tracked keywords and persist time-series snapshots; (4) a REST API with endpoints: /apps/lookup, /keywords/suggestions, /keywords/metrics, /apps/:id/rankings, and /apps/revenue (basic estimate); (5) a minimal React UI to add apps, view suggestions, and chart rank history; (6) a small CLI that calls the REST API for common tasks. Out of scope: training proprietary ML models, full enterprise features (white-label reports, multi-seat billing), and advanced revenue modeling. Require error handling, retries/backoff for external requests, input validation, unit tests for core modules, and basic integration tests for API endpoints.
How we checked
How the score was reached
- Partly verdict base52
- 3 cited sources+3
- Price verified on pricing page+3
- Evidence score58
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 productSonar — ASO Tool for App Store & Google Play Keyword Research
- official pricingSonar — Pricing & Plan details
- official docsAPI Reference - Sonar
Integrity checks
What held up, and what did not.
