Coordable (Articles sur providers)

We built a free tool to benchmark geocoding provider accuracy

François Andrieux — Fri, 24 Apr 2026 10:00:00 GMT

Why we built it

Most teams pick a geocoding provider once and never look back. Usually Google. It is reliable, well-documented, and familiar. But it is also one of the most expensive options, and on some datasets, it is not even the most accurate.

Accuracy varies a lot depending on the country, the address format, and the quality of the input data. A provider that works great for residential addresses might struggle with POI inputs. And it works the other way too: some free, official APIs (like government address registries) outperform paid commercial ones on their home turf. Beyond accuracy, cost and usage rights also differ significantly between providers.

That is why knowing which provider performs best on your data matters. We built a free tool so you can find out in a few minutes, without writing any code and without providing your own API keys.

How the benchmark tool works

You upload a CSV (or Excel file, up to 500 rows), map the columns that form your addresses, and select the providers you want to compare. We run the geocoding on our side; no account, no API keys required from you. The whole thing takes a few minutes. You get a side-by-side accuracy and cost breakdown for each provider, plus a full report by email.

Upload a dataset, map address columns, select providers, and start. No API keys required on your side.

One important detail: we do not count every response as a success. A result is considered valid only if it returns a house number, a street name, and a confidence above 80%. For Google, we also discard GEOMETRIC_CENTER and APPROXIMATE precision types; these place a point somewhere vaguely in an area, which is not useful for industries that need a precise location (logistics, real estate, route planning, insurance).

Accepting every returned coordinate would artificially inflate accuracy scores and hide the silent errors that eventually cause real problems downstream. That's actually a more common problem than you might think: geocoding providers often return false results, and detecting them is not easy. Using confidence scores and other quality rules is the only way to avoid this.

Example 1: 500 US addresses

The first dataset is 500 well-formed US street addresses, the kind of clean, structured input that geocoding providers generally handle best.

Results for 500 US addresses: per-provider accuracy, estimated cost, and Coordable Cascading with +1.4 pts and -97% cost vs a Google-only approach.

Provider	Accuracy	Cost per request
HERE	97.4%	$0.0008
Mapbox	96.8%	$0.0008
US Census	92.2%	Free
Google Geocoding	91.2%	$0.0050
OpenCage	80.6%	$0.0002
Coordable Cascading	98.8%	~$0.0000

Two things stand out. First, HERE outperformed Google by 6 percentage points, while costing 6x less. Second, the US Census (a free, public API that requires no account or key) scored 92.2%, beating Google on this dataset. That is not a fluke: the Census Geocoder is built on TIGER/Line, the official US address dataset, and it handles clean residential addresses very well. We have written more about this in a dedicated comparison.

Running all five providers through Coordable Cascading brought accuracy to 98.8% (494/500 matched) at almost $0 effective cost per request, because US Census resolved the bulk of the file at no charge and the cascade only paid commercial rates for the small fraction it could not handle. That is +1.4 points over the best single provider and -97% cost compared to routing everything through Google.

See geocoding prices in 2026 for a full price comparison.

Example 2: 300 French addresses (mixed POIs and house addresses)

The second dataset is more representative of real-world messiness: 300 French rows mixing residential house addresses with POIs (streets, neighbourhoods, landmarks, business names). No clean housenumber in many rows.

Results for 300 French rows: Google leads single providers on mixed inputs; Coordable Cascading adds +5.6 pts and cuts cost by 93%.

Provider	Accuracy	Cost per request
Google Geocoding	70.7%	$0.0050
HERE	67.3%	$0.0008
Mapbox	62.0%	$0.0008
French BAN	54.3%	Free
OpenCage	31.3%	$0.0002
Coordable Cascading	76.3%	~$0.0004

Google leads here, which makes sense: it handles POIs, business names, and loose address formats better than most providers. The French BAN (the official national address registry, free to use) scores 54.3%, not because it is inaccurate on house addresses, but because it rejects anything that does not look like a structured housenumber + street address. Streets, neighbourhoods, and POI-style rows all come back empty under our strict quality rules. On a dataset of pure French residential addresses, BAN alone typically reaches well above 90%.

The headline numbers stay in the 70% range for this dataset, and that is honest: our quality bar is strict, and mixed input data is genuinely harder. Coordable Cascading reached 76.3% (229/300), +5.6 points over Google, at around $0.0004 per request (a 93% cost reduction). BAN handled the house addresses it could resolve for free; commercial providers covered the rest.

More context on French provider performance: best geocoding providers for France and how we cut French geocoding cost by 67%.

Conclusion

I hope these two examples show how much results can vary from one dataset to another. There is rarely a clear winner among commercial providers, even though some do a genuinely great job. The better strategy is to combine several providers: take advantage of the strengths of each, and prioritize the cheapest ones first. This approach, cascading geocoders, helps reduce the number of incorrect or unmatched geocodings while drastically cutting costs.

What works best truly depends on your country, your address quality, and your specific use case. For example, a clean dataset of US residential addresses is a very different challenge from a mixed French file containing POIs, street-level inputs, neighborhood names, or business names. The right cascade or provider combination for one scenario might not suit the other.

That’s exactly why we built this tool: your results will depend on your actual data and your context. Run it on your own files and see what happens—you may discover that a free public API covers most of your addresses, or that a cheaper commercial provider outperforms the one you're currently paying for. Ultimately, the best strategy is tailored to your country and input quality: experiment, compare, and don’t assume a single provider (or cascade) will be optimal in every situation.

Open the free benchmark tool

Where to go from here

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.

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.