Prompt

I'm building a SaaS onboarding flow and want to reject disposable addresses

Technology / Marketing Automation1 observationLast seen Oct 5, 2026

Latest observation

Oct 5, 2026OpenAI APIWeb search: off

A good approach is to block known disposable email domains at signup and combine that with a few light checks so you don’t frustrate real users.

Practical setup

  1. Normalize the email

    • Lowercase the domain
    • Trim whitespace
    • Validate syntax first
  2. Check against a disposable domain list

    • Maintain a curated list of known temporary email providers
    • Update it regularly
    • Cache it in memory or Redis for fast lookups
  3. Add a deliverability step

    • Send a verification email
    • Require confirmation before full account activation
  4. Use risk signals instead of hard-blocking everything

    • Disposable domain
    • Recently created domain
    • Suspicious signup velocity
    • IP reputation / VPN / proxy
    • Repeated aliases from the same pattern

Recommended implementation pattern

  • Soft block on suspicious emails: ask for a different email
  • Hard block only for domains you’re very confident are disposable
  • Keep an allowlist for enterprise domains or partners if needed

Example logic

if email_is_invalid(email):
    reject("Invalid email")

domain = normalize_domain(email)

if domain in disposable_domain_list:
    reject("Disposable email addresses are not allowed")

send_verification_email(email)

Useful API options

If you don’t want to maintain the list yourself, consider an email intelligence API that checks:

  • disposable/temporary domains
  • role accounts
  • typo suggestions
  • MX records
  • SMTP deliverability

Important caveat

Avoid overblocking:

  • Some privacy-focused users use aliasing services
  • Some legitimate companies use shared mail gateways
  • New domains can be false positives

If you want, I can help you with:

  • a Node/Express, Python/Django, or Go implementation
  • a disposable domain detection strategy
  • a database schema / caching design for this check

Observation history

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Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.

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