CRM and sales decision
Amor
Keep paying—Amor's primary advantage is its proprietary, large-scale GitHub dataset and enrichment, which a lone developer cannot realistically reproduce; a smaller DIY tool can provide basic search and exports but will lack the dataset scale and tuned signals.
Visit website↗Built by Jared Rhizor, who ships 4 products in this index
Not priced
No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.
$100one-off56 h to build
$20/mo6 h/mo upkeep
No published price to break even against.
The code exists. It is not what you are paying for.
This project is real, published, and does the core job — and this page still says keep paying. What the subscription buys is proprietary data, and none of that ships in a repository. Fork it anyway if you want to. Go in knowing what it does not carry. What stays hard ↓ · All Amor alternatives, with the arithmetic →
What a replacement has to do
- Crawl and index public GitHub profiles and repos, provide faceted search and candidate profiles, enrich profiles with commit emails/social links, and export selected candidates to Ashby-compatible CSV.
What it still won’t have
- The vendor's large prebuilt dataset (tracked millions of profiles and repos)
- Prebuilt profile-summary heuristics and tuned contribution signals
- Ongoing enrichment (commit-email extraction and social links) at scale
- Operational polished UI and team collaboration features ready out of the box
- Any proprietary candidate-ranking and filtering tuned by their data
What remains hard
- Proprietary data
We track 8,000,000+ developer profiles, 66,000,000+ repositories, and 145,000,000,000+ stars to help you search for the best engineers.
First-year cost
No published price
Amor does not publish a price we could read, so there is nothing to compare against. What building costs is below.
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 GitHub-first candidate sourcing app using Python + FastAPI backend, Postgres for relational data, OpenSearch for faceted search, Redis + RQ (or Celery) for background workers, and a React frontend. Core features in scope: (1) GitHub auth and incremental crawl of public users/repos, (2) parsing and normalization of profile metadata and commit emails, (3) indexing into OpenSearch with language, contribution-frequency, and cleaned location tags, (4) search UI with keyword + advanced filters (language, activity, location), (5) profile pages with auto-generated activity summary, (6) CSV export in Ashby-compatible format, (7) background job for periodic re-crawl and enrichment. Explicitly out of scope: building a multi-million-profile prepopulated dataset, advanced proprietary ranking models, large-scale infrastructure for thousands of daily crawls. Include error handling, retries, rate-limit backoff, tests for API and worker jobs, and Docker-based deployment manifests for a single VPS or small cluster.
How we checked
How the score was reached
- Pay verdict base20
- An open-source build was found+5
- 3 cited sources+3
- Hard moats found in the evidence-3
- Evidence score25
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 productAmor - Find engineers your team will love
- official productAmor - product homepage
- open sourcedeepset-ai/haystack
Integrity checks
What held up, and what did not.
