Email Verifier Actor

Validate email lists with a clear two-step pipeline: regex structure check first, then precheck + final validation through external verification APIs.
This is a standalone email validation API workflow for no-code and low-code users on Apify who need reliable output for CRM cleanup, lead qualification, and campaign hygiene.

If you already have a list of emails, run this Actor in minutes: provide emails, start a run, and download structured results from the dataset.

What does this email verification Actor do?

This Actor verifies each email address in your input and returns a structured status for every record. It helps you separate invalid formats from addresses that fail technical checks, and from addresses that pass full validation.

It is a verification workflow Actor, not a mailbox inbox checker and not a sender reputation tool.

Why use this email validation API workflow?

• 📈 Keep CRM records clean before outreach
• 🎯 Improve deliverability for cold email and newsletter campaigns
• 🧹 Remove obviously bad addresses before enrichment
• ⚙️ Automate recurring verification with Apify schedules
• 🔌 Feed verified emails to downstream tools through API/integrations

What can this email verification Actor do?

• ✅ Validate email format with regex before calling any external API
• ✅ Run Precheck for emails that pass regex
• ✅ Run Final Validation only for emails that pass precheck
• ✅ Process emails in fixed batches of 10 for predictable throughput
• ✅ Retry failed API calls up to 3 times with 1-second delays
• ✅ Push all results to Apify dataset with consistent fields
• ✅ Log detailed progress, endpoint path, and params for troubleshooting
• ✅ Handle unexpected failures gracefully and exit with clear error logs

Why use it on Apify platform from day one?

• 📊 Run history, logs, and monitoring in one place
• 🔁 Scheduling and automation-ready runs
• 🔌 Easy API access to trigger runs and fetch results
• 📁 Dataset export in JSON, CSV, Excel, XML, and RSS
• 🧩 Integration with Make, Zapier, webhooks, and custom pipelines

What data can this Actor extract?

The Actor writes one dataset item per input email.

Field	Type	Description
email	string	Original input email
regexValid	boolean	Whether basic email regex passed
status	string	Pipeline outcome (regex_failed, precheck_failed, precheck_rejected, final_validation_failed, final_validation_passed)
precheck	object/null	Raw precheck response
finalValidation	object/null	Raw final validation response
error	string/null	Error details when an API stage fails

How to verify email lists on Apify

Open this Actor page and start from the Input tab:
https://console.apify.com/actors/<YOUR_ACTOR_ID>/input

1. Open the Actor and go to the Input tab.
2. Add your email list to emails (array of strings).
3. Click Start.
4. Wait for run completion, then open the Output tab.
5. Download the dataset in your preferred format.

How to verify email lists in Python

Use Apify API to trigger runs programmatically and pull dataset items after completion.
API docs: https://docs.apify.com/api/v2

Input example

{
  "emails": [
    "john@example.com",
    "invalid-email",
    "sales@company.org"
  ]
}

Output example

[
  {
    "email": "invalid-email",
    "regexValid": false,
    "status": "regex_failed",
    "precheck": null,
    "finalValidation": null,
    "error": "Invalid email structure by regex validation."
  },
  {
    "email": "john@example.com",
    "regexValid": true,
    "status": "final_validation_passed",
    "precheck": {
      "success": true,
      "email": "john@example.com"
    },
    "finalValidation": {
      "valid": true,
      "email": "john@example.com"
    },
    "error": null
  }
]

How much does email verification cost?

Cost depends on runtime and provider-side API usage. On Apify, you mainly pay for consumed compute (CUs), while email volume and response speed impact total run duration.

Practical expectation:

• Small lists (tens to low hundreds) are typically low cost.
• Larger lists scale predictably because processing is batched and retried with limits.
• You can estimate costs by running a sample (for example 100 emails), then extrapolating.

For best budget control, test on a representative subset first and monitor run logs + CU usage.

Tips for best results

• Use clean input with trimmed email strings.
• Remove duplicates before running for lower cost and faster output.
• Start with a sample batch to validate your expected statuses.
• Treat precheck_rejected and final_validation_failed differently in your downstream workflow.
• Re-run only failed subsets if provider/API errors are temporary.

Related links and integrations

• Apify API reference: https://docs.apify.com/api/v2
• Apify scheduling: https://docs.apify.com/platform/schedules
• Apify integrations (Make, Zapier, webhooks): https://apify.com/integrations
• Apify Academy: https://docs.apify.com/academy

If you publish other data collection Actors (for example email discovery/contact scraping), link them here as companion workflows.

FAQ

Is this Actor a standalone email verification API workflow?

Yes. You provide a list of emails, and the Actor handles the full multi-step verification flow and returns normalized output.

Can I use this without coding?

Yes. Use the Apify Console input form and export results from the Output tab.

Can I automate it?

Yes. Trigger runs via Apify API, schedule recurring runs, and connect outputs to downstream tools.

Does this Actor guarantee inbox existence?

No tool can guarantee 100% inbox existence in all cases. Use results as a strong technical validation signal, not absolute deliverability certainty.

What if an API call fails?

Each API call is retried up to 3 times with a 1-second wait. If still unsuccessful, the error is recorded in error and processing continues.

Is this legal to use?

This Actor only processes email addresses that you provide as input. You are responsible for lawful data collection and use in your jurisdiction (including GDPR/CCPA and anti-spam rules where applicable).

Support

If you need custom output fields, stricter validation rules, or integration-specific formats, open an issue or contact the maintainer.
When reporting issues, include:

• Run URL
• Input sample (non-sensitive)
• Expected vs actual output
• Relevant log lines