---
title: "Locke vs Self-Hosted and On-Premise AI"
description: "Self-hosting removes model-provider access to your prompts. It doesn't prevent sensitive data from entering your own model's context or logs. What each layer covers."
url: "https://sonomos.ai/compare/sonomos-vs-self-hosted-ai/"
source: "https://sonomos.ai/compare/sonomos-vs-self-hosted-ai/"
---

# Locke vs Self-Hosted and On-Premise AI

Running your own model (Ollama, LM Studio, a private API deployment, a VPC-hosted endpoint) removes one risk: the provider can no longer read your prompts. That's a real improvement. Sensitive data can still flow through the model context, sit in your own request logs, or escape through the browser-based AI tools your team still uses. Self-hosting changes who can see the data, not whether it can be seen.

## When Self-hosted / on-premise AI models fits

- Your primary concern is model-provider access. Self-hosting closes that gap completely.
- Your compliance environment requires model weights to be stored on-premises (certain FISMA, CMMC, or air-gapped scenarios).
- You need to run inference offline or within a private network perimeter.

## When Locke fits

- Your team uses browser-based AI tools (ChatGPT, Claude, Gemini) in addition to any self-hosted deployment, which covers only the on-prem endpoint.
- Sensitive data still flows into your self-hosted model in plaintext and may be logged on your own servers.
- You want to prevent specific data categories from entering the model context at all, regardless of where the model runs.

## Side-by-side

| Dimension | Locke | Self-hosted AI |
| --- | --- | --- |
| What it protects against | Data entering any model context, through on-device masking before transmission. | Model-provider access to your prompts: the provider never sees them. |
| Does sensitive data leave the device? | No. Masked before any network call is made. | Yes. The full prompt goes to your self-hosted server in the clear. |
| Coverage of browser AI tools | Same controls on ChatGPT, Claude, Gemini, and other browser-based interfaces. | None. Only covers the self-hosted endpoint. |
| Prompt log risk | Logs contain only the masked version. | Your own server logs may contain raw sensitive data. |
| Best for | Controlling what enters any model context, from any interface. | Eliminating model-provider visibility and third-party data retention. |

## Bottom line

Self-hosting and Locke cover different parts of the threat model and work well together. Self-hosting removes provider-side risk. Locke removes device-to-model risk by masking sensitive fields before they enter any model's context, wherever that model runs.

[Download Locke](https://sonomos.ai/downloads/)[See the Locke demo](https://sonomos.ai/locke/#demo)[See pricing](https://sonomos.ai/pricing/)[Talk to the team](https://sonomos.ai/contact/)

## Related comparisons

- [Locke vs Enterprise AI Plans](https://sonomos.ai/compare/sonomos-vs-chatgpt-enterprise/)
- [Locke vs Internal AI Portals and Private AI Deployments](https://sonomos.ai/compare/sonomos-vs-internal-ai-portal/)

[View all comparisons](https://sonomos.ai/compare/)

## Further reading

- [AI Agents and Data Privacy: Operator, Computer Use, and Agentic Browsing in 2026](https://sonomos.ai/blog/ai-agents-data-privacy-2026/)
- [Is DeepSeek Safe for Work? Privacy, GDPR, and HIPAA Risks in 2026](https://sonomos.ai/blog/is-deepseek-safe-gdpr-hipaa-2026/)
- [The Best AI Privacy Tools for ChatGPT, Claude, and Gemini (2026)](https://sonomos.ai/blog/best-ai-privacy-tools-chatgpt-claude-gemini-2026/)
- [What Is PII Redaction? A Plain-English Guide for AI Users](https://sonomos.ai/blog/what-is-pii-redaction-ai/)