Cloud medical NLP APIs vs on-device de-identification

What is the difference between cloud and on-device medical NLP?

A cloud medical NLP API is a hosted service: you send clinical text to the vendor, and it returns entities, codes or a redacted version. On-device de-identification runs a model inside your own process, on a server, laptop, phone or browser tab, so the text never leaves your network.

How do the options compare?

	Cloud medical APIs	Open-source, on your hardware
Examples	AWS Comprehend Medical, Azure Text Analytics for health, Google Healthcare Natural Language API	OpenMed, Microsoft Presidio, Philter
Where text goes	Vendor cloud region	Stays in your process
Pricing	Pay per volume of text	No licence fee; you run it
Languages	Mostly English	OpenMed: PII models for 35 languages, including Hindi, Marathi, Tamil and Telugu
Runs offline or on a phone	No	OpenMed: yes, through ONNX and mobile runtimes
Setup time	Hours	Days to weeks, including evaluation
Who maintains it	The vendor	You, or a partner

When does a cloud API make sense?

When you need results this week, your text is in English, volumes are modest and your legal team is comfortable sending clinical text to the vendor under a business associate agreement. It is also a sensible baseline to compare an open-source model against.

When does on-device de-identification make sense?

When patient data must not leave your network, when users write in Indian or other non-English languages, when volumes make per-character billing expensive, or when redaction has to happen on the phone or in the browser before anything is sent. It also suits regulated buyers who ask where every byte goes.

How should you choose?

Build a labelled test set of 200 to 500 real, de-identified notes in every language you support. Measure recall per identifier type: names, phone numbers, record numbers, dates and addresses. A missed phone number matters more than a wrongly redacted word. Pick the option that meets your recall target at a cost and data path your compliance team accepts, and re-run the test on every model update.