globalcountriespostal-codes

How PostalDataPI Supports 70+ Countries With One API Call

The PostalDataPI Team··4 min read

The Global Postal Code Challenge

Postal codes are deceptively simple. In the United States, they are five digits. Easy. But step outside the US and things get interesting fast.

  • United Kingdom: Alphanumeric with a space (SW1A 1AA)
  • Canada: Alternating letters and digits (M5V 3L9)
  • Japan: Seven digits with a hyphen (100-0001)
  • Germany: Five digits, but they call them Postleitzahlen (PLZ)
  • Netherlands: Four digits plus two letters (1012 AB)
  • Ireland: Seven characters, no pattern you would guess (D02 AF30)
Each country has its own format, its own structure, and its own quirks. Building a global application means handling all of them.

The Typical Approach (And Why It Breaks)

Most developers start by finding a postal code API that covers their primary market — usually the US. The API works great. Then the product team says, "We are launching in the UK next quarter."

Now you need a second API. Or you need to upgrade to an expensive tier that includes international coverage. Or you discover your current provider charges per-country rates: $0.01 for US lookups, $0.02 for UK, $0.05 for Japan.

Before long, your address validation logic looks like a patchwork of different APIs, different response formats, and different pricing models.

One API, One Format, One Price

PostalDataPI takes a different approach. Every country uses the same API call:

curl -s -X POST https://postaldatapi.com/api/lookup \
  -H "Content-Type: application/json" \
  -d '{"zipcode": "SW1A", "country": "GB", "apiKey": "YOUR_KEY"}' | jq '{city, state, ST}'

Change the country parameter and the zipcode, and you get consistent JSON responses regardless of which country you are querying. No special endpoints, no country-specific SDKs, no format juggling.

The price is the same too: $0.000028 per query, whether you are looking up a ZIP code in Montana or a postal code in Tokyo.

Postal Code Normalization

One of the trickiest parts of working with global postal codes is normalization. Users type postal codes in all sorts of ways:

  • "sw1a 1aa" (lowercase)
  • "SW1A1AA" (no space)
  • "SW1A 1AA" (extra space)
  • "100 0001" (space instead of hyphen)
PostalDataPI normalizes all of these automatically. We strip extra spaces, handle case differences, and resolve common formatting variations so your application does not have to.

What Data Do You Get Back?

Every lookup returns the basics: city and state/region. But if you need more, the /api/metazip endpoint returns the full picture:

{
  "meta": {
    "postalCode": "10115",
    "country": "DE",
    "placeName": "Berlin",
    "latitude": 52.5323,
    "longitude": 13.3846
  }
}

Latitude and longitude in one call. For US postal codes, metazip also includes county and timezone. This is everything you need for geocoding, shipping zone calculation, or location-based analytics.

Currently Supported Countries

PostalDataPI supports over 70 countries across six continents. The full list includes major markets like the United States, United Kingdom, Germany, France, Japan, Canada, and Australia, plus dozens more across Europe, Asia, South America, and beyond.

You can browse the complete list with example postal codes at postaldatapi.com/countries.

Adding New Countries

Our data pipeline continuously harvests postal code data from authoritative sources including national postal services, government geographic databases, and open data repositories. When new countries are added, they are available through the same API call with no changes to your integration.

Try It With Your Countries

If your application serves users in multiple countries, give PostalDataPI a try. Sign up for free with 1,000 queries — no credit card required — and test every country your application needs.

The API reference at postaldatapi.com/reference has complete documentation for all endpoints, and SDKs are available for Python and Node.js.

How PostalDataPI Supports 70+ Countries With One API Call | PostalDataPI Blog