Algolytics
Algolytics helps automate business decisions and processes with Machine Learning, AI & Data Science.
19/06/2026
In our new report, we ranked 10 standardization and geocoding services on 2,500 real courier addresses, and the global names came in behind local ones.
The score weighs three things at once: standardization (is the address usable for matching records), geocoding (location accuracy), and trust (how often service's own top-ranked result is actually wrong).
What are the main conclusions?
◾ One service scores above http://0.9: Data Quality, at http://0.94. Built for the Polish registers, it leads on all three indexes at once, so most records can be auto-processed without manual checks.
◾ Esri (http://0.64) and Google (http://0.62) geocode very well but are weaker elsewhere: Esri on standardization and trust, Google on trust, mostly on rural addresses.
◾ HERE (http://0.55), TomTom (http://0.51), and Azure (http://0.51) geocode acceptably, but even their best-rated results can be off by a few hundred meters, so it's worth adding a few safeguards before you rely on their label.
◾ Emapa, Precisely, and Mapbox score lowest.
Download the full report and the cost-simulation Excel - link in the first comment.
The same ML use case can cost 20x more - depending on how it’s built.
We tested a typical production setup: AutoML models, ~150 queries per second.
Model accuracy was similar across platforms.
Cost wasn’t - in some cases over 20x higher.
Where does that difference come from?
◾ models with similar accuracy can have very different inference cost
◾ scoring can run through heavy runtimes and libraries - or as simple compiled code
◾ architecture - containerized Python workers and orchestration overhead vs JVM-based, event-driven ex*****on with Vert.x
At this scale, these differences repeat hundreds of times per second - and that’s what drives TCO.
Watch the full recording: https://eu1.hubs.ly/H0vtblG0
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