Local models can keep everything on your own hardware, but they also make you the operator of security, scaling, patching, and auditability. Tresor keeps the control story strong without turning every team into an ML platform team.
Works well when
Maximum control
You control the hardware, models, network boundaries, and retention decisions from the metal upward.
Offline or air-gapped options
You can run without external connectivity if you are willing to build and maintain the full supporting environment.
Becomes the better fit when
Confidential by design
You get sealed processing and encrypted memory without having to assemble and harden the underlying stack yourself.
EU-sovereign runtime
Workloads stay in European secure enclaves with the same verifiable control posture across teams and projects.
The decision usually comes down to who can read the data, who owns the keys, and how much proof your team needs when the workflow is reviewed.
Self-hosted or air-gapped AI is still the right answer for some organizations, especially when they already have the people and process to operate it well.
You control the hardware, models, network boundaries, and retention decisions from the metal upward.
You can run without external connectivity if you are willing to build and maintain the full supporting environment.
You can swap and tune open-source models as needed, provided your team can sustain the operational complexity.
Most teams do not actually want to run an AI platform. They want strong privacy and compliance without inheriting patching, incident response, and infrastructure engineering as a side business.
You get sealed processing and encrypted memory without having to assemble and harden the underlying stack yourself.
Workloads stay in European secure enclaves with the same verifiable control posture across teams and projects.
Managed attestation, updates, and audit plumbing remove the day-two work that makes local deployments expensive to sustain.
You keep governance authority over access and retention without becoming the operator of the full AI platform.
The deciding question here is not just privacy. It is whether your organization wants to own the platform layer or buy a service that preserves strong controls without that overhead.
You have a capable infra or ML team, need bespoke or air-gapped deployment options, and are ready to own security, updates, and scaling.
You want strong privacy and compliance without turning maintenance, audit plumbing, and runtime hardening into a permanent internal project.
