Home / Companies / Didit / Blog / Post Details
Content Deep Dive

Didit vs Identy.io: hosted KYC API vs on-device biometric SDKs

Blog post from Didit

Aggregate trend data notice

Excluded from normalized aggregate trends after staff review: 3056 posts were attributed to March 2026; 671 shared March 14, 2026. The preceding six-month median was 13.5 posts.

Review evidence: 3,056 posts in March 2026; 671 shared March 14, 2026; preceding six-month median 13.5. Reviewed August 9, 2026.

This company's pages remain public, but its content is excluded from normalized aggregate trends. Unfiltered raw trends and advanced filtering are available to Accelerate and Lead accounts.

Post Details
Company
Date Published
Author
Didit
Word Count
1,685
Company Posts That Month
14
Language
English
Hacker News Points
-
Post removed?
No
Summary

Didit is presented as a hosted identity-verification and compliance platform for government and corporate teams that need KYC flows, evidence records, review queues, and ongoing screening, while Identy.io is characterized in public documentation as focusing more heavily on biometric capture SDKs, document verification, and ABIS matching. Didit’s published core KYC bundle costs $0.33 per successful verification and includes document verification, passive liveness, face matching, and device/IP analysis, with separately priced services such as AML screening, NFC, active liveness, transaction monitoring, KYB, wallet screening, and case review. The comparison notes that Didit supports hosted and SDK-based integrations, broad document coverage, screening and monitoring features, national digital IDs, and historical-record imports, whereas Identy.io offers mobile SDKs, on-device matching, mobile NFC, and biometric modalities including face, fingerprint, and iris. Supporting evidence cited for Didit includes iBeta passive-liveness certification, listed SOC 2 Type 1 and Type 2 and ISO/IEC 27001 certifications, a Spanish Treasury sandbox report involving NFC and active-liveness testing, and customer-reported efficiency improvements. The material recommends an evidence-based migration approach that runs both systems in parallel, reconciles imported records and status mappings, tests comparable user populations, establishes review and cost thresholds, and expands traffic only after acceptance criteria are met.

Trends Found in this Post
Trend Post Mentions Total Month Mentions Posts Companies MoM
AI Coding Assistant 4 814 184 89 -58%
MCP 4 4,439 399 125 -63%
Use This Data

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.