Genderize alternatives: what to compare before switching

Genderize is a mature, compact API. An alternative is worth evaluating when your requirement is not covered by its operating model: a particular script, source-level evidence, a different billing pattern, or a workflow beyond individual HTTP requests.

This page was checked against public vendor documentation on September 8, 2026; accuracy figures were refreshed on September 23, 2026, and prices on October 14, 2026. NameGender's accuracy column was re-measured on October 7, 2026. We operate NameGender, one of the alternatives discussed below. The comparison method is more important than our conclusion, and every check can be repeated with free access.

Establish the Genderize baseline

The official Genderize API reference documents one JSON endpoint that accepts a first or full name and returns gender, probability and count. A two-letter country_id can localise the result, and repeated name[] parameters support batches of up to 100 names.

Genderize's pricing page lists a free plan of 1,000 names per month and paid plans at $20 for 25,000 names, $60 for 250,000, $180 for 2.5 million and $540 for 25 million a month; above that is Enterprise, with no published price. Requests over the limit return 429 Too Many Requests until the next billing cycle; there are no overage fees, and unused names do not roll over. It counts names rather than HTTP calls, so a batch containing 100 names consumes 100 units. One key also works across its gender, age and nationality services. Plan details change, so check the page again before budgeting.

That is a strong baseline: simple request, counted evidence, country support and a large established service. Do not switch merely because another homepage claims a larger database.

Alternatives worth testing

NameGender

NameGender is built around a slightly different output contract. Each response includes probability, sample_size, confidence and source so a downstream system can distinguish counted evidence from an uncounted source or another lookup path. It supports first names, full names, email addresses, usernames, bulk API calls and managed CSV/XLSX jobs.

Purchased credits do not reset at the end of a month. This is most relevant for seasonal or one-time enrichment; a steady daily workload may still compare well under a subscription elsewhere. The public coverage report also separates results by script rather than presenting one global accuracy claim.

Gender-API.com

Gender-API.com's v2 documentation describes endpoints for first names, full names and email input, along with country localisation and batch processing. Its detailed responses include probability and other metadata; its simpler interface documents samples and a normalized name.

It belongs on the test list when parsing a full-name or email field is central to the job. Confirm which API version, response schema and authentication method you will integrate before comparing implementation time.

Price at the same volume

Monthly cost of the cheapest published plan that covers each volume, overage included where a plan sells it. Checked on each vendor's pricing page on October 14, 2026. Currencies are left as published: Genderize and Namsor price in US dollars, Gender-API.com and NameGender in euros.

Two readings matter. At 250,000 names a month, Genderize and NameGender cost about the same once the currency is accounted for, so price is not a reason to switch at that volume. At one million names a month Genderize's cheapest fit is the 2.5 million plan at $180, so a workload of that size pays for capacity it does not use; NameGender's 1M plan is €79.20. Genderize's plans are also cheaper with yearly billing, which this table leaves out. Namsor charges less for repeated names, which this table ignores by assuming every name is different.

NameGender's figures are monthly subscriptions. Credits bought once cost more per name (€99 for one million) but stay on the balance until they are spent, which suits a list enriched once a quarter better than any monthly plan. The pricing page computes the same comparison from our live catalogue.

Accuracy on the same names

Price only matters for answers you can use. We sent the same 991 labelled names, with the same country hint, to Genderize and Namsor on September 18, 2026, and ran NameGender on the same names again on September 28, 2026, after correcting its US counts and adding five national registers, and once more on October 7, 2026, after adding Thai and Arabic given names from Wikidata. The Genderize and Namsor columns are still from September 18. The full benchmark publishes the test names, every name NameGender missed, the method and the confidence intervals; the short version is below.

Overall, Genderize and NameGender are level: the half-point gap is well inside both 95% confidence intervals, so the sample cannot tell the two apart. Genderize is well ahead on Hebrew and Thai; if those markets carry your list, it is the better choice today. On Arabic script the two are now within one name of each other. We are ahead on Chinese names written in Han characters by 39 points and slightly ahead on Korean. Genderize also makes fewer confident mistakes: 0.50% of its answers were wrong with a probability of 0.9 or more, against our 1.31%. Measure on your own list before trusting any of these rows, including ours.

Migrating without touching the rest of your code

Most code that calls Genderize reads three fields: gender, probability as a fraction and count. A small adapter returns NameGender results in that shape, so the business logic behind it does not change. The batch limit is the same 100 names per request.

import requests

API = "https://namegender.com/api/v1/gender/bulk"


def genderize_shaped(names, country_id=None, api_key="YOUR_API_KEY"):
    """NameGender results in the shape Genderize code already reads."""
    body = {"names": names}
    if country_id:
        body["country"] = country_id

    response = requests.post(
        API,
        headers={"Authorization": f"Bearer {api_key}"},
        json=body,
        timeout=10,
    )
    response.raise_for_status()

    return [
        {
            "name": row["query"],
            "gender": row["gender"],
            "probability": row["probability"] / 100,
            "count": row["sample_size"] or 0,
        }
        for row in response.json()["results"]
    ]

Two differences survive the adapter. NameGender's probability is an integer from 0 to 100, hence the division. And sample_size is null when an answer comes from a source without counts; the adapter turns that into 0, which your code may read as "no data". If that distinction matters, keep sample_size and source in your own model instead of flattening them.

The five tests that decide a switch

1. Coverage on your markets

Build a labelled sample that follows your real country and script distribution. If 35% of your customers use Turkish names, 35% of the fixture should be Turkish. Publish or retain the sample so a future data update can be tested against the same rows.

Measure coverage, accuracy when answered and end-to-end accuracy separately. An API can produce a high answered-only score by returning null on everything difficult. It can produce high coverage by guessing. You need both behaviours visible.

2. Ambiguity and country

Test Andrea in Italy and Germany, Kim in several markets, and names that are genuinely used across genders. Compare the result with and without a country hint. The response should give you enough evidence to set a threshold instead of making the decision for you.

Use the country attached to the record. IP location, interface language and email suffix are weak proxies for naming context and can make a result look more precise while answering the wrong question.

3. Unknown and malformed input

Submit nonsense, empty values, company names, initials and product codes. A service that returns unknown has handled the case better than one that produces a confident category from no evidence. Keep unknown nullable throughout your own data model.

4. Workflow cost

If you have 500,000 spreadsheet rows once per quarter, test the spreadsheet workflow and one-time billing. If you enrich new accounts continuously, test API latency, rate behaviour and monthly unit cost. Do not compare a managed file job to a single request as though they were the same task.

5. Migration cost

Response fields differ. Genderize uses a probability between zero and one and a count field. Another API may use an integer percentage, a differently named sample count, or a nested result. Put a small adapter around your provider so business rules consume one internal model. Then switching a vendor does not require changing every downstream feature.

When not to switch

Stay with Genderize if it already wins on your labelled fixture, the monthly model fits the workload, and its response gives your decision rule enough evidence. Familiar production behaviour has value, and a migration introduces new failure modes.

Run an alternative in shadow mode first when the expected gain is coverage or accuracy. Compare both responses without changing customer-visible behaviour, inspect disagreements, and switch only after the improvement survives that review.

NameGender is a sensible candidate when you need source visibility, file processing, purchased credits without a monthly reset or stronger performance in the markets covered by our published fixtures. The honest next step is still the same: test us and Genderize on your data, and keep whichever one wins.

Related: Gender-API.com alternatives and Namsor alternatives.