Article summary
A 2026 TG checker buying sheet covering real fields, blind samples, billing units, duplicates/unknowns, deletion and exit cost.
Choosing a TG number checker in 2026 begins with “what acceptable output does each billable unit produce?” rather than price per ten thousand. Telegram task width, unknown handling, duplicate billing, deletion and human review all change total cost. The following sheet is designed for a procurement meeting.
Lock the task schema before buying
| AIPUSH task | Main fields | Suitable question |
|---|---|---|
| TG Registration | Phone, registration result | Platform-state observation |
| TG Activity | UserID, username, offline time, active days, names, VIP, frozen and more | Activity/account observation |
| TG Gender and Age | Prior fields plus avatar URL, age and gender | Approved coverage study |
| TG Full Format | Add skin tone, avatar type and people count | Research requiring defined wide fields |
Make the vendor define the billing atom
Is billing based on submitted row, unique phone, successful return, populated field or task? Are duplicates, format errors, unknowns, system failures and retries billed? Write the answer as a formula and recalculate a sample invoice. “Per 10,000” is not comparable by itself.
Prepare an imperfect blind sample
Cover major countries, data sources, old/new cohorts, duplicates, national notation, international notation and exceptions. Retain a small internally known set without disclosing truth to the operator. Every candidate receives the same frozen one-phone-per-line TXT.
Acceptance is more than hit rate
| Dimension | Measure | Failure |
|---|---|---|
| Reconciliation | Joinable returns / valid unique inputs | Row-order dependency or loss |
| Unknown transparency | Unknown, error and negative separate | Everything becomes unregistered |
| Field coverage | Non-null by country/source | Overall rate only |
| Repeat consistency | Reasonably spaced retest | Untimestamped jump |
Measure speed as sustainable throughput
Track first result, complete batch, P95 wait, exception recovery and time until Excel is acceptable. Peak speed is not production capacity. If output returns quickly but needs two days of spreadsheet repair, end-to-end service is slow.
Red lines for field claims
Reject bot detection, high conversion, high net worth, ban prevention or identity verification without a schema and evidence. TG activity and profile observations cannot be renamed intent, wealth or trust scores.
Ask operational security questions
Who has access, where processing occurs, whether subprocessors exist, how transfer and storage are protected, log and backup retention, and incident-notice timing. Submit a deletion request for a test batch and ask how cache and backup are handled; a security badge is insufficient.
Simulate integration cost
Land returned Excel in staging and validate schema version, types, unknowns, idempotency and collisions. A result needing repeated manual renaming, row movement or ID copying may cost more in operation than the purchase-price difference.
Support and change terms
Require advance notice of field changes, traceable historic schemas, response times for incidents and non-duplicative billing for an error rerun. Support should locate an issue by batch_id instead of requesting the entire database again.
Exit cost is often ignored
Confirm export of task history, dictionary, deletion evidence and unfinished batches, plus credential revocation at termination. Avoid vendor-only labels by mapping observations into an internal neutral schema.
A final scoring approach
Score task/field fit, blind-test quality, unknown transparency, end-to-end throughput, secure deletion, integration, support, billing and exit. Unit price should be only part of the weight. Choose the tool whose results can be explained and reversed, not the one with the widest sheet or strongest slogan.
