Private AI vscloud AI

The privacy question everyone is asking

Every prompt you send to a cloud model leaves your machine. Policies vary. For consumer toys that may be fine. For client data, unpublished product plans, medical context, or regulated work, “trust us” is not a control plane.

What private AI actually means

Private AI means prompts, documents, and generated results default to staying under your authority — ideally on hardware you own or a tightly controlled on-prem environment. It is not a sticker on a SaaS login. Architecture decides privacy.

Cloud AI strengths

Fast start, managed uptime, frequent model upgrades, and low friction for public or non-sensitive tasks. Ideal for experimentation and non-confidential drafting when contracts allow.

Local private AI strengths

Data residency by construction, offline options, no automatic multi-tenant model hop, and capacity that scales with your hardware budget instead of per-seat surprise bills.

Hybrid honesty

Many organisations will mix paths: local for sensitive work, cloud for public research. The critical design is that sensitive paths must not fall back to the cloud by accident.

S◉LOCK stance

S◉LOCK is designed local-first with no automatic cloud model fallback. Founding Preview invites buyers who need real privacy architecture — not another rented chat seat.

FAQ

Is private AI the same as encrypted chat?

Encryption in transit helps, but if the model runs in a vendor’s multi-tenant cloud, the vendor’s stack still processes content. Local inference removes that hop.

Does GDPR require local AI?

Not always — but EU data-protection culture favours minimisation and control. Local-first design aligns with European expectations when you process sensitive personal data.

Related guides

• What is local AI?
• What is an AI operating system?
• How to run an LLM locally
• Open-source AI models for local use
• Looking for a private ChatGPT alternative?