Coordable (Posts about api)

US Census + Google Maps: How to cut geocoding costs by 90% for US addresses

François Andrieux — Sat, 21 Mar 2026 10:00:00 GMT

The setup

We wanted to test the effectiveness of a cascading geocoding strategy for US addresses. We took 1,000 real US addresses (a mix of residential and business addresses, of variable quality but overall correct) and ran three geocoding tests.

Here is the summary of the results:

Test	Provider(s)	Addresses resolved	Cost per 1,000
1	US Census only	91.9%	$0.00
2	Google Maps only	92.6%	$5.00
3	US Census → Google Maps (cascade)	97.3%	$0.41

The cascade delivers 97.3% addresses found at $0.41, compared to $5.00 for Google Maps alone. That is a 90% cost reduction with a +5.4 percentage point improvement in addresses resolved. Amazing right?

Read on to see how each test was run and what the results look like.

Table of contents:

Test 1: US Census Geocoder

What is the US Census Geocoder?

The US Census Geocoder is a public and free API provided by the US federal government. It is not open source (the underlying software and data are maintained by the Census Bureau), but it is freely accessible, requires no API key, and has no published usage limits or cost.

It is built on TIGER/Line data, the official US address dataset. Results are interpolated on the street rather than placed at the exact building location (no rooftop precision). For most use cases (delivery routing, territory analysis, customer mapping), street-level interpolation is entirely sufficient.

The output data is in the public domain, with no licensing restrictions on how you store or use the coordinates.

Important limitation: US Census only understands structured street addresses. It cannot geocode points of interest, business names, or company names. If your dataset contains entries like "Starbucks, Chicago" or "Boeing headquarters", US Census will return nothing for those rows. In that case, the 90% cost reduction figure will not hold: a commercial provider will need to handle a much larger share of your requests. This strategy works best for datasets of clean street addresses ; see below for a solution.

Results

Accuracy: 91.9% (919 out of 1,000 addresses resolved)
Cost: $0.00

Test 1, US Census only: 919/1,000 addresses resolved, at no cost.

91.9% is a strong result for a free API. The 8.1% of unresolved addresses are typically due to non-standard formatting, missing components, or addresses that simply do not appear in the TIGER/Line dataset.

Test 2: Google Maps Geocoding API

What is Google Maps Geocoding?

Google Maps is one of the most widely used geocoding providers. At $5.00 per 1,000 addresses (after the free quota), it is also one of the most expensive.

It returns different precision levels: ROOFTOP for exact building locations, and RANGE_INTERPOLATED for street-level interpolation. For a fair comparison with US Census, which only returns street-level results, we accept both ROOFTOP and RANGE_INTERPOLATED in this test.

Licensing note: Google Maps results come with significant restrictions beyond the price. Google's Terms of Service do not allow you to store geocoded coordinates long-term or use them outside of a Google Maps display context. If you need to build a database of geocoded addresses, use the results in a non-Google mapping tool, or export coordinates for analysis, you may be violating Google's terms. This is an important constraint to factor in before committing to Google as your primary geocoding provider.

Note: Google free quota is excluded from these cost figures, as its effective value depends on overall usage across all Google Maps products. The $5.00 figure reflects the standard pay-as-you-go rate.

Results

Accuracy: 92.6% (926 out of 1,000 addresses resolved)
Cost: $5.00

Test 2, Google Maps only: 926/1,000 addresses resolved at ROOFTOP or RANGE_INTERPOLATED level, at $5.00 per 1,000.

Google Maps edges out US Census by 0.7 percentage points (92.6% vs 91.9%), but at a cost of $5.00 per 1,000 addresses vs $0. For this dataset, paying for Google Maps buys very little extra accuracy on its own.

Test 3: US Census + Google Maps (cascade)

How the cascade works

The cascading strategy is straightforward:

Send every address to US Census first. If US Census returns a valid result, use it. Cost: $0.
For addresses that US Census could not resolve, send them to Google Maps. Cost: $5.00 per 1,000 for this subset only.

In our test, approximately 90% of addresses were resolved by US Census (free), and only ~8% required Google Maps, at a total cost of $0.41. The remaining ~2.7% were not resolved by either provider.

This is why the cost drops so dramatically: instead of paying $5.00 for every address, you only pay Google's rate for the small fraction that US Census could not handle.

The two-provider cascade configured in Coordable: US Census first, then Google Maps for unresolved addresses, accepting both ROOFTOP and RANGE_INTERPOLATED results.

Results

Accuracy: 97.3% (973 out of 1,000 addresses resolved)
Cost: $0.41
Provider split: ~90% resolved by US Census · ~8% resolved by Google Maps · ~2.7% unresolved

Test 3, US Census → Google Maps cascade: 973/1,000 addresses resolved, at $0.41 per 1,000.

The cascade delivers a +5.4 percentage point improvement over either provider alone, at 8% of the cost of Google Maps. The key insight is that US Census and Google Maps fail on different addresses. Addresses that US Census cannot parse or match are often resolved by Google's more robust parsing engine, and vice versa.

Set up cascading geocoding with Coordable

Going further: adding a third provider

This two-provider cascade already delivers strong results, but you can push accuracy even higher by adding a third provider.

For example, adding HERE as a second fallback (after Google Maps) would catch some of the 2.7% of addresses that neither US Census nor Google resolved. HERE has strong US address coverage and is significantly cheaper than Google Maps at $0.83 per 1,000 addresses. A three-provider cascade (US Census → Google Maps → HERE) would likely push accuracy above 97.3% while keeping the overall cost low, since HERE would only handle the tiny fraction of addresses that both previous providers missed.

We ran a similar experiment with French addresses using the BAN API (the French government geocoder), Google Maps, and HERE, and achieved 97.7% accuracy at $1.62 per 1,000, a 67% cost reduction compared to Google alone. The principle is identical: route easy addresses through the cheapest provider, and reserve expensive providers for the hard cases. See How to reduce geocoding costs by 67% for the full breakdown.

Conclusion

For US address geocoding, the US Census Geocoder is a powerful and underused tool. It is free, requires no API key, and handles the vast majority of clean street addresses correctly. Pairing it with Google Maps as a fallback gives you the best of both worlds: near-complete coverage at a fraction of the cost.

Test	Provider(s)	Found	Cost per 1,000
1	US Census only	91.9%	$0.00
2	Google Maps only	92.6%	$5.00
3	US Census → Google Maps (cascade)	97.3%	$0.41

The key caveat: this strategy works for street address datasets. If your data contains business names, POIs, or unstructured inputs, US Census will not help and you will need a commercial provider for a larger share of requests.

However, it would have been possible to plug a low-cost provider to handle the remaining addresses as well : HERE, Nominatim (OSM), OpenCage, etc.

If you want to implement this cascade without writing custom code, Coordable lets you configure multi-provider workflows with quality rules at each step, so only good results pass through.

Try cascading geocoding with Coordable

Where to go from here

See how the same cascading approach cuts costs by 67% for French addresses: How to reduce geocoding costs by 67%
Compare geocoding prices across all major providers: Geocoding prices in 2026
Learn more about Google Maps Geocoding: Google Maps Geocoding API

How to reduce geocoding costs by 67%

François Andrieux — Sun, 08 Mar 2026 10:00:00 GMT

The use case: a logistics company paying way too much for geocoding

If you work in logistics, insurance, or routing software, you know the drill: geocoding volumes are massive, and the bills that come with them are too.

In this article, we show how Coordable helped a logistics company cut geocoding costs by 67% while also increasing its share of valid geocoding results from 94% to 97.7%. Total savings: $20,280 per year. 🤩

Let's dive in.

Table of contents:

The problem: choosing between cost and quality

You have probably faced this tradeoff: premium providers like Google Maps deliver excellent results, but they come at a price. Cheaper alternatives may leave some addresses unresolved.

Most of the time, though, you do not actually need the premium option; addresses are clean and unambiguous, so any decent geocoder will do the job. The real challenge appears when your address data is messy: in those cases, you are forced to choose between paying for Google Maps to guarantee correct results, or accepting that a cheaper provider will miss some.

The solution: cascading providers

Good news: there is a strategy that gives you the best of both worlds, cascading geocoders. The principle is straightforward:

Use an open-source or low-cost provider for ~90% of geocoding requests (the clean, unambiguous addresses).
Use a premium geocoder (or several) to resolve the remaining ~10%.

This approach raises geocoding success rates while keeping costs down. In our case, it reduced costs by 67% and increased the share of successfully geocoded addresses by +3.7%. Here is how we did it.

The workflow: 3 providers

Our logistics client needed to geocode mostly residential addresses in France. France has an excellent government API for this purpose, the Base Adresse Nationale (BAN), which covers French residential addresses very well, though it does not handle points of interest or addresses outside France. Many commercial providers actually use BAN data to improve their own results.

The downside of BAN's API is that it is less robust at parsing and matching poorly formatted input. So we still needed to pair it with at least one commercial provider to ensure no addresses slip through. For that, we chose Google Maps. Finally, we added HERE as a second fallback because HERE also leverages BAN data, making it a strong option for the French addresses that Google could not resolve to rooftop level.

Here is our strategy:

French BAN: confidence score > 0.75 required.
Google Maps API: ROOFTOP location type required (if not resolved in step 1).
HERE: confidence score > 0.75 required (if not resolved in step 2).

The three-provider cascade configured in Coordable: BAN → Google Maps → HERE, each step gated by a quality rule.

We apply quality rules at each step to discard results that are not good enough. For French BAN and HERE, the confidence score reflects how closely the geocoder matched the input string. Although computed slightly differently across providers, both scores are a reliable way to filter out poor results. Setting the threshold at 0.75 is fairly strict, but the cascade makes this safe; any address rejected at one step simply moves to the next provider.

Google Maps does not provide a confidence score, so instead we filter on the location type returned (ROOFTOP, APPROXIMATE, GEOMETRIC_CENTER). For residential addresses, we require ROOFTOP only, to ensure the coordinates are placed in front of the building rather than somewhere in the middle of the street.

The results

To measure the impact, we ran two geocoding sessions on the same dataset and compared them:

Google Maps only: the client's initial setup.
Cascading strategy: BAN → Google Maps → HERE.

The test dataset contains 1,000 residential addresses, mostly clean and well-formed. That is large enough to get a reliable picture of how this strategy performs at scale.

In practice, the client geocodes about 500,000 addresses per month.

Session 1: Google Maps only

Session 1, Google Maps only: 940/1,000 addresses resolved at ROOFTOP level, at $5 per 1,000.

Results:

94% valid geocoding results (addresses resolved to ROOFTOP).
Estimated cost: $5.00 per 1,000 addresses.

Note: these costs ignore the Google free quota, which is shared across all Google Maps products. Its effective value depends on each client's overall usage, so we exclude it for a fair comparison.

Session 2: BAN + Google Maps + HERE (cascading)

Session 2, Cascading strategy: 977/1,000 addresses resolved, at $1.62 per 1,000.

Results:

97.7% valid geocoding results, a +3.7% improvement.
Estimated cost: $1.62 per 1,000 addresses.

Provider breakdown: BAN 64.5% · Google Maps 31.7% · HERE 3.8%

Using BAN means that 64.5% of addresses cost nothing to geocode. Our strict rules then sent 31.7% of addresses to Google Maps, and HERE handled the remaining 3.8%.

Total cost reduction: 67%, while improving geocoding quality.

Yearly cost savings

At 500,000 addresses per month, the client was spending about $2,500/month on Google Maps alone. With the cascading strategy, that bill drops to $810/month, without any loss of quality; on the contrary.

	Monthly cost	Annual cost
Google Maps only	$2,500	$30,000
Cascading (BAN + Google + HERE)	$810	$9,720
Savings	$1,690	$20,280

That is $20,280 saved per year, while resolving +3.7% more addresses correctly.

Conclusion

If you are paying Google Maps rates for every address, you are likely overpaying. This real-world example shows how large the gap can be: by routing the bulk of clean French addresses through the free BAN API, reserving Google Maps for harder cases, and using HERE as a final safety net, the client went from 94% to 97.7% geocoding success while paying 67% less.

The key is combining the right providers in the right order, and enforcing quality rules at each step so only genuinely good results pass through. The result is a system that is both cheaper and more reliable than relying on a single provider.

Set up cascading geocoding with Coordable

Where to go from here

Learn more about the providers used in this workflow: Google Maps Geocoding API, HERE Geocoding API.
See how providers compare in France: Best geocoding providers for France.
Compare Google and HERE head-to-head: Google vs HERE for geocoding.
See current geocoding prices at a glance: Geocoding prices in 2026.

HERE price increase by 6% in 2026

François Andrieux — Fri, 06 Mar 2026 12:00:00 GMT

HERE Technologies is updating its pricing from April 2026. This post summarizes what matters for geocoding users: effective date, what changes, and who is affected.

Table of contents:

What HERE announced

From April 1, 2026, the price of the HERE Base Plan will increase by 6% for new contracts, renewals, and extensions. Existing contracts remain unchanged until renewal or extension.

HERE Technologies communicated the following:

The prices of all 35 HERE Location Services and Data IO using the pay-per-transaction model will increase by 6%.
This price increase applies to all new subscriptions and (auto-)renewals from April 1, 2026 onwards.
The price of the Developer technical support plan remains unchanged.
These pricing updates will be reflected in all future contracts and invoices from April 1, 2026 onwards.

What this means for you

If you sign a new contract or renew on or after April 1, 2026, you will see the new 6% pricing. For coverage, rate limits, licensing, and current pricing details, see our HERE Geocoding API guide.

Where to go from here

HERE Geocoding API — coverage, rate limits, and licensing.
Geocoding prices in 2026 — compare HERE with other providers.
Google vs HERE for geocoding — head-to-head comparison.

Geocoding prices in 2026

François Andrieux — Fri, 06 Mar 2026 10:00:00 GMT

Here’s a clear overview of what the main geocoding services cost in 2026, and whether anything changed recently. Use the table and the interactive chart below to compare prices at a glance and to simulate your costs. 🙂

Table of contents:

Pricing overview

Roughly what you pay after the free tier (per 1,000 requests, in USD). Free tiers and volume discounts vary; check each provider for exact terms.

Provider	Free tier	Price per 1,000 (after free)	Note
Google Maps	10,000/month	$5.00	Volume discounts; see March 2025 changes below.
HERE	30,000/month	$0.83	Volume discounts; 6% increase from April 2026 for new contracts and renewals.
TomTom	2,500/day (~75k/month)	$0.75	Volume discounts.
Mapbox (Temporary)	100,000/month	$0.75	Temporary results only; no long-term storage.
Mapbox (Permanent)	—	$5.00	Allows indefinite storage of results.
OpenCage	2,500/day (~75k/month)	from ~$0.15–0.17	Subscription-based; volume discounts. Permissive storage.
Nominatim (OSM)	Public API rate-limited	Free	Self-hosting has infra costs.

Many countries also have free or low-cost official APIs (e.g. France's BAN, Spain's CartoCiudad, UK's OS Places under certain schemes). For local addresses, check our country-specific comparisons linked at the end.

Compare costs at your volume

The difficulty is that most providers have different pricing tiers and different free tiers. So you need to know exactly how many addresses you need to geocode to know which provider is the cheapest. Also, prices typically decrease as volume increases, so your per-request cost depends heavily on your total usage.

You can simulate your costs with the interactive chart below. Just select the number of addresses you need to geocode and the chart will show you the cost per provider!

Did prices change?

Google (March 2025): Google replaced the previous $200/month credit with per-SKU free usage (e.g. around 10,000 free billable events per month for the Geocoding API). Volume discount tiers were expanded for high usage. So the effective free tier for geocoding is lower than before, but heavy users can see lower marginal costs at scale. Details: Google Maps Geocoding API guide and Google Maps Platform March 2025 changes.

HERE (April 2026): From April 1, 2026, the HERE Base Plan and all pay-per-transaction Location Services and Data IO increase by 6% for new contracts, renewals, and extensions. Existing contracts are unchanged until renewal. See HERE price increase by 6% in 2026 for details.

Other major providers: TomTom, Mapbox, and OpenCage have kept their published pricing stable into 2026. Our source dates in the repo are October 2025 / January 2026; always confirm current prices on each provider’s site before committing.

How to reduce geocoding cost

Relying on a single premium provider (e.g. Google at $5 per 1,000 requests) can become very expensive at scale. A proven way to cut costs without sacrificing quality is cascading geocoders: use several providers in a defined order, and only fall back to the next when the current one fails or does not meet your criteria.

What is cascading? You define an ordered list of providers (e.g. OpenStreetMap or OpenCage → HERE → Google). For each address, you try the first provider; if that fails or the result does not pass validation, you automatically try the next one, and so on. Cheaper or free providers handle the bulk of easy addresses; premium providers are used only for the harder cases. The result: higher success rates (often 98–99% instead of ~90% with one provider) and lower total cost, because most requests are served by the least expensive option.

Reduce your costs with Coordable cascading geocoding

The diagram below illustrates the idea: send 100% of requests to a free or low-cost provider first (e.g. OSM); only the unresolved share (e.g. 10%) goes to a mid-tier provider (e.g. HERE); and only the remainder (e.g. 4%) hits the premium provider (e.g. Google). You still reach a very high overall geocoding rate (e.g. 99.9%) while paying the premium rate for a small fraction of traffic.

Cascading geocoders: prioritize cheap or free providers, fall back to premium only when needed. Quality and cost scale with each step.

You can implement cascading in your own code (call provider A, then B on failure, etc.) or do it easily with Coordable: choose your providers, define the cascade order and fallback rules, and optionally validate results so only good coordinates reach your database. Either way, the principle is the same: prioritize cheaper solutions and only fall back to premium when it is truly necessary.

Try Coordable to easily implement cascading geocoding for your business.

Price is not everything

Pricing does not always reflect quality. The cheapest option is not necessarily the worst, and some geocoders can match or beat large commercial providers depending on region and data type (addresses vs POIs, local vs global). To see how providers actually perform where you operate, check the best geocoding provider for your country. We have in-depth comparisons for:

Where to go from here

For more detail on a single provider (coverage, rate limits, licensing), see our guides: Google Maps Geocoding API, HERE Geocoding API, Mapbox Geocoding API. For a head-to-head in 2026, see Google vs HERE for geocoding.

How to Geocode Addresses with BAN (Base Adresse Nationale) API

François Andrieux — Wed, 15 Jan 2025 10:00:00 GMT

If you're working with French addresses, the Base Adresse Nationale (BAN) is your best friend. It's France's official address database, maintained by local authorities and IGN (Institut Géographique National), and it's completely free to use. Whether you need to geocode a single address or process thousands, BAN provides authoritative, accurate results without the licensing restrictions or costs of commercial providers.

Unlike commercial geocoding services that charge per request and restrict how you can use the data, BAN offers unlimited free geocoding with a permissive open license. This makes it ideal for French organizations, government agencies, and anyone building applications focused on France.

Important update: As of 2025, the BAN API has been migrated to the Géoplateforme service managed by IGN. The old endpoint api-adresse.data.gouv.fr is deprecated and will be decommissioned in January 2026. This tutorial covers the new Géoplateforme service endpoints. (Source)

Note: BAN only handles addresses - it doesn't geocode business names, landmarks, or POIs (Points of Interest). For those, you'll need a commercial provider like Google Maps or HERE. But for pure address geocoding in France, BAN is unmatched.

Table of contents:

Introduction: Why use BAN?

Before we dive into the how-to, let's understand why BAN is special and when it makes sense to use it.

What is BAN?

The Base Adresse Nationale (BAN) is France's official national address database. It contains over 25 million addresses compiled from local authority data across all French communes, including overseas territories. What makes BAN unique is that it's:

Authoritative - maintained by official French authorities (IGN and local governments)
Comprehensive - covers every commune in France, including rural areas and small localities
Accurate - provides exact rooftop coordinates rather than interpolated guesses
Free - completely free to use with no quotas or charges
Open - fully open license allowing storage, reuse, and redistribution

Here is an example of a BAN address, shown in the official visualization tool:

Example for "118 rue st charles", Paris (screenshot from the BAN interface).

When to use BAN

BAN is perfect when you: - Need to geocode French addresses (metropolitan France and overseas territories) - Want authoritative, government-maintained data - Need to avoid commercial licensing restrictions - Are building applications that require address validation - Want to geocode large volumes without cost concerns - Need to store and reuse geocoded coordinates permanently

Why BAN works so well: Since BAN is updated by local authorities who know their addresses intimately, it provides exact coordinates rather than estimates. This is especially valuable for rural addresses and small localities that commercial providers might struggle with.

When BAN isn't the right choice

BAN won't work if you need to: - Geocode addresses outside of France - Find businesses, landmarks, or POIs by name - Handle natural language queries or typos (BAN requires well-formatted addresses) - Need real-time business information (hours, ratings, etc.)

For these use cases, you'll want to consider commercial providers like Google Maps, HERE, or Mapbox.

Single address geocoding

Let's start with the simplest use case: geocoding a single address. This is perfect when you need to convert one address into coordinates, perhaps for a form validation or a single lookup.

The new endpoint & parameters (2026)

The BAN geocoding service is now provided through the Géoplateforme service managed by IGN. The endpoint for forward geocoding is:

https://data.geopf.fr/geocodage/search

Important: The old endpoint api-adresse.data.gouv.fr is deprecated and will be decommissioned in January 2026. You should migrate to the new Géoplateforme endpoint. (Source)

This /search endpoint accepts the following parameters:

q (required): The address to geocode
limit (optional): Number of results to return (default is 10)
autocomplete (optional): Enables autocomplete mode (default is true)

Other parameters are available to precise the geocoding context (e.g. city, postal code, etc.), but I won't cover them here. For the full list of parameters, consult the OpenAPI documentation.

Example request

Here's an example with the address "118 Rue St Charles, Paris":

GET https://data.geopf.fr/geocodage/search?q=118+Rue+St+Charles,+Paris&limit=1

And it returns:

{
  "type": "FeatureCollection",
  "features": [
    {
      "type": "Feature",
      "geometry": {
        "type": "Point",
        "coordinates": [2.283496, 48.844484]
      },
      "properties": {
        "label": "118 Rue Saint-Charles 75015 Paris",
        "score": 0.7424048484848483,
        "housenumber": "118",
        "id": "75115_8513_00118",
        "name": "118 Rue Saint-Charles",
        "postcode": "75015",
        "citycode": "75115",
        "x": 647415.92,
        "y": 6860731.63,
        "city": "Paris",
        "district": "Paris 15e Arrondissement",
        "context": "75, Paris, Île-de-France",
        "type": "housenumber",
        "importance": 0.83312,
        "street": "Rue Saint-Charles",
        "_type": "address"
      }
    }
  ],
  "query": "118 rue st charles, Paris"
}

In this example, we added a limit of 1 to get only one result. The API returns its best guess for the address, ordered by the match score.

What happens? The API searches its database for addresses matching your query and returns a list of possible matches. The response includes the exact coordinates, formatted address, and additional metadata like postal code and city.

The response

The API returns a GeoJSON FeatureCollection. Each feature contains:

ID - the unique identifier of the address in the BAN database (properties.id)
Coordinates - latitude and longitude in the geometry.coordinates field (format: [longitude, latitude]). They are also available as x and y in the properties object.
Formatted address - the standardized address label in properties.label
Address components - housenumber, street, city, postcode, citycode, etc. The housenumber and street name will be mising if not found.
Importance - estimated importance of the address in the database (0-1 scale)
Score - relevance score indicating match quality between your query and the result (0-1 scale)

Note: Results are returned ordered by score (highest first), so the first result is typically the best match. However, always verify the address components (especially city and postal code) to ensure the result is correct, as we'll explain in the next section.

The match score

The API returns a confidence score between 0 and 1. However, a high score doesn't mean the address is correct, and it definitely doesn't mean the address exists.

Take our "118 Rue St Charles, Paris" example. The top result scores 0.74, which sounds reasonable. But peek at the second result at 0.62 - it points to "Rue Charles Paris 33130 Bègles". That's 500km away in Bordeaux's suburbs!

Even trickier: search for "17 Rue St Charles, Paris" and you'll get a respectable 0.74 score... except this address doesn't exist. The API found something that looks similar, but it's matching strings, not verifying reality.

The takeaway? Always sanity-check geocoding results. The score tells you how well your query matched something in the database, not whether that something is what you actually wanted.

Batch geocoding: processing multiple addresses

When you have many addresses to geocode - whether it's a spreadsheet with customer addresses or a database export - you'll want to use batch geocoding. BAN offers two approaches: a web interface for one-time tasks and an API for automation.

Method 1: Using the BAN web interface

The easiest way to geocode a CSV file is through BAN's web interface. This is perfect if you're not a developer or if you're doing a one-time batch job.

Here is how to do it:

Prepare your CSV file - Include columns with address data (street number, street name, postal code, city). Export from Excel if needed.
Upload to BAN CSV tool - Select which columns contain your address components (e.g., Numéro, Rue, Ville).
Download results - Get your original data plus coordinates, formatted addresses, and result_status column.

The BAN CSV tool allows you to upload a CSV file and select the columns containing the address data.

And you're done: once you specified

Method 2: Using the BAN API

For automation, integration into applications, or regular batch processing, you'll want to use the BAN API directly. This gives you full control over the process and lets you build custom workflows.

Understanding batch geocoding with the API

The Géoplateforme service provides a dedicated batch geocoding endpoint that accepts CSV files. This is the recommended approach for processing multiple addresses programmatically.

The batch endpoint is available at https://data.geopf.fr/geocodage/batch/.

The request should be a POST request with the CSV file in the request body. It accept several important parameters: - columns: the columns to concatenate the address. It can be left empty to use all columns. - indexes: the column to use as index. It's useful to be able to identify the address in the original CSV file.

Note: You still can make multiple requests to the /search endpoint, one for each address, but this will be less efficient than using the batch endpoint.

Example request

Here is an example of a CSV file with the addresses to geocode:

id,rue,street,city,postal_code
1,12.0,Avenue des Champs-Élysées,Paris,75008
2,45.0,Rue de la République,Lyon,69002
3,78.0,Boulevard de la Croisette,Cannes,6400
4,,,???????,
5,156.0,Rue Saint-Honoré,Paris,75001

In this case, the columns to concatenate the address are: rue, street, city, postal_code, so we can specify columns=rue,street,city,postal_code.

The request should be:

POST https://data.geopf.fr/geocodage/search/csv
data=addresses.csv
columns=rue,street,city,postal_code

The response will be a CSV file too, returning both the original data and the geocoded data with new columns all prefixed with result_ (result_score, result_score_next, result_label, result_type, result_id, result_housenumber, result_name, result_street, result_postcode, result_city, result_context, result_citycode, result_oldcitycode, result_oldcity, result_district, result_status).

id;rue;street;city;postal_code;longitude;latitude;result_score;result_score_next;result_label;result_type;result_id;result_housenumber;result_name;result_street;result_postcode;result_city;result_context;result_citycode;result_oldcitycode;result_oldcity;result_district;result_status
1;12;Avenue des Champs-Élysées;Paris;75008;2.302859;48.871285;0.8596027272727272;0.6467871900826445;Avenue des Champs Elysées 75008 Paris;street;75108_1733;;Avenue des Champs Elysées;Avenue des Champs Elysées;75008;Paris;75, Paris, Île-de-France;75108;;;Paris 8e Arrondissement;ok
2;45;Rue de la République;Lyon;69002;4.835859;45.761928;0.9157595187165776;0.6972321052631578;45 Rue de la République 69002 Lyon;housenumber;69382_6005_00045;45;45 Rue de la République;Rue de la République;69002;Lyon;69, Rhône, Auvergne-Rhône-Alpes;69382;;;Lyon 2e Arrondissement;ok
3;78;Boulevard de la Croisette;Cannes;06400;7.032336;43.54684;0.9329795565410199;;78 Boulevard de la Croisette 06400 Cannes;housenumber;06029_0880_00078;78;78 Boulevard de la Croisette;Boulevard de la Croisette;06400;Cannes;06, Alpes-Maritimes, Provence-Alpes-Côte d'Azur;06029;;;;ok
4;;;???????;;;;;;;;;;;;;;;;;;;skipped
5;156;Rue Saint-Honoré;Paris;75001;2.339989;48.862052;0.9164409090909089;0.6147660173160173;156 Rue Saint-Honoré 75001 Paris;housenumber;75101_8635_00156;156;156 Rue Saint-Honoré;Rue Saint-Honoré;75001;Paris;75, Paris, Île-de-France;75101;;;Paris 1er Arrondissement;ok

The result_status column contains "ok" if the address was geocoded successfully, "skipped" if the address was not geocoded successfully.

Interesting! In batch CSV mode, the API only returns one result per row. However, you can still now the score of the second best match in the result_score_next column.

Usage and cost

BAN is completely free to use, with no hidden costs or quotas. This makes it an excellent choice for organizations of any size.

Cost structure

API usage: FREE - No charges for API requests
No quotas - Process as many addresses as you need
No subscription fees - No monthly or annual costs
Database download: FREE - You can download the entire BAN database for free

Why BAN is free: BAN is a public service maintained by French government agencies. It's funded by taxpayers and provided as open data to benefit French society and economy.

If you're processing millions of addresses or need offline access, you can download the complete BAN database and run your own geocoding locally.

For a detailed comparison with commercial providers (Google Maps, HERE, Mapbox...), check out my Best Geocoding Providers for France guide.

Rate limits

BAN allows 50 requests per second per IP. If you exceed this, you'll get HTTP 429 errors. Throttle to ~40/second and you'll be fine.

Wrapping up

BAN is free, accurate, and open. For French addresses, it's hard to beat. Just remember: migrate to the new Géoplateforme endpoint (data.geopf.fr) before January 2026 if you're still using the old one.

Full API reference: Géoplateforme OpenAPI documentation.

To get a full overview of the geocoding providers for France and a comparison between them, check out our guide: Best Geocoding Providers for France.

Last updated: 2025-01-15 | Updated by: François Andrieux | Sources: BAN API Documentation, CSV Geocoding Tool, API Migration Announcement, Géoplateforme Capabilities, Géoplateforme OpenAPI Documentation

Disclaimer: This guide is for informational purposes. Always refer to official BAN and Géoplateforme documentation for current endpoints and terms.