CRM and sales decision

Snov.io

A competent developer can build a useful subset (find+verify+send) in about a week, but they cannot reproduce Snov.io’s proprietary, reverified B2B database, warm-up pool, broad integrations, and enterprise deliverability infrastructure; keeping the paid product makes sense if you need those assets.

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Subscription$29.25/month ✓ verified
Initial build30 hours
Monthly upkeep8 hours + $60
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

  • Discover prospects, verify contact emails, store prospects, and send personalized email outreach sequences.

What it still won’t have

  • Access to Snov.io’s proprietary, extensive B2B contact database and monthly reverification process
  • Large warm-up pool, premium warm-up infrastructure, and shared deliverability infrastructure
  • Built-in LinkedIn automation extension and browser extension convenience
  • Hundreds/thousands of pre-built integrations and enterprise support/implementation services
  • Scale, fraud prevention, and deliverability monitoring baked into the vendor platform

What remains hard

  • Proprietary dataSmart AI search in an extensive database
  • Proprietary dataExplore a goldmine of B2B data, reverified monthly
  • Brand trustTrusted by 300,000 companies in 180+ countries as their AI lead generation and outreach automation solution
Read the build prompt

First-year cost

Keep paying

Paying ischeaper in year one.

On cash alone, building overtakes the subscription at 3 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 minimal self-hosted lead discovery + verification + email outreach service using Postgres, a Node.js (Express) API, and a React admin UI. In scope: (1) prospect discovery endpoint that scrapes company pages and accepts CSV uploads, (2) email pattern generator and SMTP/SMTP-probe verification job, (3) prospect storage with deduplication and quota counting, (4) campaign engine to send templated personalized emails and schedule follow-ups via a transactional SMTP provider (Mailgun/SendGrid), (5) simple dashboard to view prospects, campaign status, bounces, and replies (webhooks). Out of scope: building a proprietary global contact database, LinkedIn browser extension, shared warm-up pool, or full deliverability analytics. Include error handling, background job retrying, basic automated tests for core endpoints, and deployment scripts (Docker + single cloud VPS).
How we checked3 sources · 3/3 runs agreed · evidence score 59

How the score was reached

  • Partly verdict base52
  • 3 cited sources+3
  • Price verified on pricing page+3
  • 3/3 assessment runs agreed+4
  • Hard moats found in the evidence-3
  • Evidence score59

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.

Integrity checks

What held up, and what did not.

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