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Private and on-premise AI tools for regulated companies.

Private AI tools run where the customer controls the data: in an isolated network in the customer's own cloud account, known as a VPC, or on the customer's own hardware. Regulated firms choose them when policy, contracts or supervisors rule out shared services for certain material. The trade-off is real: self-hosted models can lag the best hosted ones, and GPUs, upgrades and operations become someone's job.

Why regulated firms go private

The trigger is usually a data classification policy: material marked strictly confidential may not leave the corporate network, whatever the vendor's contract says. Banks and wealth managers carry client confidentiality duties, pharmaceutical companies guard pre-launch data, defence and public sector bodies work under security classifications, and many firms have promised their own clients where data will sit. Financial entities in the EU also answer to the Digital Operational Resilience Act, applying since 17 January 2025, which requires them to manage ICT third-party risk, register ICT service contracts and plan exits from services supporting critical or important functions. Marketing content is in scope more often than expected: unreleased products, customer data used for personalisation and deal communications.

VPC or on-premise

In a VPC deployment the vendor's software runs inside the customer's own cloud account. The customer controls the network boundary, encryption keys and logs, while cloud GPUs provide capacity that scales with demand. On-premise means the customer's own data centre, possibly air-gapped from the internet. It gives the most control and the most work: hardware with long procurement lead times, capacity planning, patching and monitoring all sit with the customer or a managed service. Between the two sits single-tenant hosting, a dedicated environment run by the vendor. A hybrid often makes most sense, sending confidential jobs to private models and routine work to hosted models under zero-retention terms.

Self-hosted models: what you give up

Self-hosting means running open-weight models on your own infrastructure. For many text and image tasks they are strong, but the most capable models in some categories are offered only as hosted services, and video generation is the most GPU-hungry medium to run yourself. Read each model's licence before commercial use, because open weights do not always mean unrestricted use: some licences add usage thresholds or acceptable-use conditions. You also inherit work a vendor would otherwise do: evaluating new versions, deciding when to upgrade, and running the safety filters that stop a model producing content the brand would never publish.

Cost and performance trade-offs

Private capacity is paid for whether it is used or not. Marketing demand is spiky, with launches, seasonal peaks and Black Friday, so GPUs sized for the peak sit idle for much of the year, while hosted services spread that cost across many customers. Latency can favour local deployment for small jobs and hosted capacity at peaks. The sensible approach is to route by confidentiality rather than make everything private. Synthetic White's orchestration layer picks the model per job by format, fidelity, confidentiality and cost; confidential work can run on private or self-hosted models, and deployment is available in the cloud, a private VPC or on-premise.

Updated 25 September 2026

Questions

Private and on-premise AI tools, answered.

What is the difference between private cloud and on-premise AI?

Private cloud usually means the tool runs in an isolated network inside a cloud account you control, so data never enters a shared environment. On-premise means your own hardware in your own data centre, possibly cut off from the internet. On-premise gives the most control and demands the most operational work.

Are self-hosted AI models as good as hosted ones?

Sometimes, depending on the medium and the task. Open-weight models are strong for many text and image jobs, but the most capable models in some categories are only available as hosted services. Routing solves most of this: keep confidential work on private models and use hosted models with zero retention for the rest.

Do banks and insurers need on-premise AI for marketing content?

Not always. They need control proportionate to the data: client data and unreleased information may require private processing, while public campaign imagery may not. In the EU, DORA's third-party risk rules apply to ICT services whatever the deployment, so contracts, registers and, for critical or important functions, exit plans are needed either way.

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