What are the pros and cons of a local model?
The central advantage is single and clear: data never leaves the organization. No third-party transfer, no dependence on a vendor's policy. The cost: you become an AI infrastructure operator — with everything that entails.
- Full privacy — documents are never sent to any vendor; the retention question disappears entirely.
- Control — the version doesn't change under you; you can fine-tune for your needs.
- But: expensive hardware — large models need serious GPUs; small models that fit modest hardware are markedly weaker.
- But: maintenance and expertise — updates, security, monitoring and tuning need people who know what they're doing.
- But: a quality gap — the strongest models are offered as cloud services; matching them locally takes infrastructure most organizations won't maintain.
What are the pros and cons of a cloud service?
The cloud gives immediate access to the strongest models, pay-per-use, with zero infrastructure. The real price isn't monetary: every document you send travels to the vendor's servers, and data-use policies differ between vendors and tiers — and can change.
Under GDPR, sending a document containing personal data to a cloud AI service is processing and disclosure to a third party — often across borders — requiring a legal basis and usually a data-processing agreement. That is a real compliance chain, covered in our guide to safe AI adoption for business.
Comparison: local vs cloud vs cloud + anonymization
| Local model | Cloud | Cloud + anonymization | |
|---|---|---|---|
| Privacy | Full — data stays with you | Depends on vendor and contract | Identifiers are never sent at all |
| Model quality | Limited by your hardware | Best available | Best available |
| Infrastructure cost | High: GPUs, maintenance, staff | Pay per use | Pay per use |
| Best for | Strict requirements plus budget | Non-sensitive content | Everyday work on sensitive documents |
Why does anonymization dissolve most of the dilemma?
The local-vs-cloud dilemma is sharp only when the document contains identifying data. If identifiers are replaced with consistent tokens (PERSON_001, COMPANY_002) before sending, what reaches the cloud is no longer a sensitive document — and the decision returns to being about quality and cost alone.
That's how you get the best of both worlds: anonymize the document in the browser, work with whichever strong model suits you, and map the results back to the original on your side. It's also the path that fits a company AI policy without running a GPU farm.
When does a local model still make sense?
When even an anonymized document must not leave — for example trade secrets whose value is the content itself, not the identities; classified or air-gapped environments; or steady workloads large enough to justify dedicated hardware. For most organizations those are the exceptions, not the rule.