
Guides7 min read
How to estimate API credit usage and pick the right Finowl pack
Estimate monthly Finowl API credit usage with simple formulas and worked examples, then pick between Starter, Pro, Growth and Business packs without overbuying.
THE FINOWL BLOG
Breach checks, gender predictions and the engineering around them, written for the developers and teams who ship them.

Guides7 min read
Estimate monthly Finowl API credit usage with simple formulas and worked examples, then pick between Starter, Pro, Growth and Business packs without overbuying.

Guides6 min read
A CRM data enrichment guide: dedupe and normalize contacts, add aggregate gender insight and breach exposure flags, map fields and set a refresh cadence.

Guides6 min read
Use name-based gender predictions for light onboarding personalization only when confidence is high, with neutral defaults and a clear way for users to choose.

Guides6 min read
Set up email breach monitoring for customer accounts: schedule checks, diff new breaches by Name and AddedDate, notify users helpfully, and plan credit costs.

Engineering7 min read
Design a data enrichment pipeline around data minimization: send only the fields you need, keep raw PII out of logs, timestamp results, and limit access.

Engineering6 min read
Learn Finowl's 60 requests per minute limit, tell rate_limited, quota_exceeded, and provider_rate_limited apart, honor Retry-After, and pace requests.

Engineering7 min read
Design retries for metered APIs: what is safe to retry, what may already be charged, backoff with jitter, idempotent bookkeeping, and dead-letter queues.

Engineering5 min read
A step-by-step plan for enriching 100,000 contacts with Finowl's batch endpoint: chunks of 50, steady pacing, resumable runs, item errors, and credit estimates.

Gender prediction7 min read
How to read confidence, confidence_kind, and sample_count in gender predictions, choose thresholds for each use case, and store unknown results the right way.

Gender prediction7 min read
Learn what gender predictions from names can and cannot tell you, which uses are acceptable, and how to handle notice, self-identification, and minimization.

Gender prediction5 min read
Predict gender from usernames and handles with forceToGenderize or ai_mode always, understand the 3-credit price, and read source ai and model_reported results.

Gender prediction5 min read
How gender prediction from email addresses finds a name in the local part, why firstname.lastname@ works but info@ doesn't, and how to handle unknowns.

Gender prediction5 min read
Why names like Andrea, Jean, Kim and Nikita lean differently by country, how to pass a country hint, and how to choose one from billing country or locale.

Gender prediction6 min read
Learn how gender prediction from names works: dataset frequency lookups, AI inference, first-name extraction from full names, confidence scores and unknowns.

Security7 min read
A practical account takeover prevention checklist for SaaS teams: breached-password checks, exposed email monitoring, MFA prompts, rate limiting and alerting.

Security6 min read
A field-by-field guide to the email breach record: DataClasses, trust flags, stealer logs, BreachDate vs AddedDate, PwnCount, and risk rules.

Security6 min read
Credential stuffing reuses leaked logins at scale. Learn how it works and how breached email and password checks reduce account takeover risk in your app.

Security5 min read
Follow NIST SP 800-63B guidance to reject breached passwords at signup and reset, with UX tips, Node and Python code, and advice on failing open or closed.

Security6 min read
How hash-prefix range lookups let you check a password against breach data without revealing it, why padding matters, and how Finowl handles passwords.

Security6 min read
Learn what an email breach check is, how the lookup works behind one API call, what the breach record contains, and when to run checks in your product.