Free B2B Database Alternatives: The DIY Stack for 2026

The r/gtmengineering perennial thread asking for a "free B2B database that isn't useless" ran again this week. The honest 2026 answer is that every truly free database is gated (Apollo caps exports at 25/day), partial (Hunter finds emails but no firmographics), or outdated (LinkedIn scrapes that are six months stale). The actual solution is to build your own from public web sources. Here is how.

What Traditional B2B Databases Give You

Apollo, ZoomInfo, Lusha, and Clearbit assemble four data layers: company firmographics, contacts, technographics, and intent signals. Each layer comes from a different source, and each source is scrapeable from the public web if you are willing to do the work.

The DIY Stack

A five-hour build replaces 60% of the Apollo value for 2% of the cost. The tradeoff is that you own the pipeline, which means you maintain it. For a lean GTM team, that tradeoff is usually worth it.

• Firmographics: Google SERP for "site:company-domain.com about" plus a homepage fetch.
• Contact names and titles: LinkedIn via site:linkedin.com/in/ SERP queries.
• Email patterns: Hunter.io free tier (25/mo) for pattern confirmation, then generate.
• Intent signals: Reddit mentions, YouTube interviews, recent news.

The Scavio Layer

Scavio's 100 free signup credits cover the full enrichment for roughly 50 companies as a proof of concept. Upgrade to $30/mo when the pipeline is proven. One API key covers all four data layers.

import os, requests
API_KEY = os.environ['SCAVIO_API_KEY']

def enrich_company(domain: str) -> dict:
    # Firmographics from homepage SERP
    serp = requests.post('https://api.scavio.dev/api/v1/search',
        headers={'x-api-key': API_KEY},
        json={'query': f'{domain} about company'}).json()

    # Contacts from LinkedIn via site operator
    linkedin = requests.post('https://api.scavio.dev/api/v1/search',
        headers={'x-api-key': API_KEY},
        json={'query': f'site:linkedin.com/in/ {domain}',
              'num_results': 20}).json()

    # Intent: Reddit mentions
    reddit = requests.post('https://api.scavio.dev/api/v1/search',
        headers={'x-api-key': API_KEY},
        json={'query': domain, 'platform': 'reddit'}).json()

    return {
        'domain': domain,
        'firmographics': serp.get('organic_results', [])[:3],
        'contacts': linkedin.get('organic_results', [])[:10],
        'intent_signals': reddit.get('posts', [])[:5]
    }

print(enrich_company('anthropic.com'))

The Email Pattern Step

For each contact from LinkedIn, generate probable email patterns and verify the top 3. Hunter's free tier gives 25 lookups per month, which covers a small outbound pipeline. Over that, Neverbounce and other verification APIs are cheap per check.

Quality Versus Apollo

For ICP-fit companies under 500 employees, the DIY data typically matches or beats Apollo. Apollo's data is stale for fast-moving startups (people who moved jobs in the last 3 months often still appear at their old company). Public LinkedIn profiles reflect the current state.

For enterprises (5,000+ employees), Apollo still wins because the breadth of contacts matters more than freshness. Most lean GTM teams do not sell to those anyway.

The Cost Comparison

Apollo Team plan starts at $99/user/mo with strict export limits. Scavio $30/mo plus a $10/mo email verification plan lands the DIY stack at around $40/mo total for a solo GTM engineer. For 1,000 enriched contacts per month, the DIY stack has no marginal cost beyond that. Apollo's equivalent export volume requires the Growth tier at $348/user/mo.

Where to Start

Pick 10 target companies. Run the enrichment script above. Compare against Apollo. If the DIY output covers your outbound needs, commit. If it does not, spend the $99 on Apollo without guilt. The exercise takes a weekend and settles the question definitively. The best free B2B data enrichment API comparison covers other options in the same category.