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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errors“Private” is not a single deployment setting. A local LLM keeps inference in infrastructure you control, a conventional cloud API places it with a provider, and confidential computing uses hardware-backed protections to reduce exposure while data is being processed. The right choice depends on which privacy boundary you need—and whether you can operate it.
What does “private” mean for an LLM?
Before choosing a deployment, decide which exposure you need to prevent. That could mean keeping prompts on a device, preventing a provider from using them for model training, limiting how long they are retained, reducing infrastructure-operator access, or keeping data in a particular region. Those are separate requirements; a deployment that meets one does not necessarily meet the others.
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It also helps to distinguish three data states: at rest (stored), in transit (moving between systems), and in use (being processed). Encryption for stored or transmitted data does not, by itself, establish protection while a model is handling a prompt. NIST’s initial public draft, IR 8320E, Hardware-Enabled Security: Confidential Computing of Data in Cloud Workloads, published May 29, 2026, describes confidential computing as extending protection to data in active use. NIST says it addresses security and privacy concerns for organizations moving sensitive workloads to the cloud. This is a draft report, not a final standard.
How the deployment options differ
| Option | Privacy boundary | Who operates what | Fits when |
|---|---|---|---|
| Local or self-hosted | Inference runs in infrastructure controlled by you or your organization. The practical boundary still depends on host security, network, model source, logs, and access controls. | Your organization operates the hardware, serving stack, access controls, updates, monitoring, backups, and incident response. | Keeping inference within a controlled environment is a priority and you can support the operational burden. |
| Conventional cloud API | The provider runs inference. Privacy depends on its documented data handling and contract, along with your own controls. | The provider runs the service; you remain responsible for configuration, access, data governance, and controls not covered by provider commitments. | You want hosted-model access and the provider’s policy, contract, and security controls meet the use case. |
| Confidential computing | Hardware-backed trusted execution environments (TEEs) seek to protect data during processing. Attestation can establish that a workload meets required conditions before keys or data are released. | The cloud or platform operator runs infrastructure. Data owners, workload authors, and operators must define the trust boundary and key-release policy. | You need cloud or customer-controlled compute for sensitive workloads and want to reduce infrastructure-operator access to data or model assets. |
What local deployment does—and does not—protect
Running a model locally can keep inference within infrastructure you control. That can be a useful boundary when prompts must not be sent to an external inference service. It does not automatically secure the machine, network, model files, application, or the people who administer them. Nor does the word “local” tell you where logs, embeddings, backups, or outputs are stored.
Self-hosting also transfers operating work to you. You need to choose a model and license, provide suitable hardware and serving capacity, secure access, apply updates, monitor the service, and prepare for incidents. The hardware and throughput required depend on the model and workload; there is no universal configuration implied by the deployment label.
Questions to answer before self-hosting
- Which model are you running, and what does its license permit?
- What hardware and throughput does your workload require?
- Where do prompts, outputs, logs, embeddings, and backups reside?
- Who can administer the host, and how are their actions controlled?
What to verify before sending prompts to a cloud API
A cloud API can spare you from operating inference hardware, but the provider handles the service and may process prompts as part of it. “Cloud” alone says nothing definitive about training use, retention, human access, region, subprocessors, or contractual protections. Check the specific provider’s current documentation and the terms that apply to your account and use case rather than assuming one policy covers every service or plan.
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NIST’s cloud guidance emphasizes that customers remain responsible for controls their provider is not formally responsible for. Selecting a provider does not transfer all data governance or compliance duties. Identify the applicable requirements for your organization and determine which controls the provider commits to handle.
Provider and customer checks
- Are prompts or outputs used for training, and under what terms?
- How long are inputs, outputs, and service logs retained?
- Who can access data, including support personnel and subprocessors?
- Which processing region applies, and can you select or restrict it?
- What do enterprise terms say about security, incident notification, and data handling?
- Which access, configuration, governance, and compliance controls remain yours?
How confidential computing protects data in use
Confidential computing uses a hardware-backed TEE to isolate a workload and seek to protect data while it is being processed. The key concept is attestation: a verifier checks claims about the environment—such as workload, virtual machine, or platform state—against an expected policy. If the claims meet that policy, a key or protected data can be released; if they do not, release can be blocked.
For example, Google Cloud’s Confidential Space overview describes a TEE using attestation and a hardened operating-system image to protect a workload and its data from an operator. It identifies AMD SEV, Intel TDX, and Intel TDX with NVIDIA Confidential Computing among supported Confidential VM technologies, and describes separate roles for data collaborators, workload authors, and operators. Google also points to a Prompt Encryption SDK for serving an LLM in a TEE. These are product-specific descriptions: verify the actual configuration and regional support before relying on them.
NVIDIA’s reference architecture describes GPU-accelerated inference in confidential virtual machines or confidential containers in customer-controlled environments. In its described flow, model assets are encrypted, attestation is checked, and keys are released according to policy. The enterprise data owner controls inputs, outputs, and permitted telemetry while the model provider protects model weights. For the cited VM scope, production key release requires successful attestation against approved CPU, GPU, guest, and policy state. This is a vendor architecture description, not an independent guarantee for every deployment.
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What confidential computing does not settle
A TEE and attestation define a technical trust boundary; they do not answer every privacy, security, or governance question. You still need to understand what is inside the attested workload and what leaves it, including outputs and operational telemetry. You must decide who verifies the attestation, which platform and firmware states are acceptable, how keys are governed, and what happens when a check fails.
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A practical way to choose
- Write down the privacy outcome. Specify whether you need prompts to stay on controlled infrastructure, no provider training, limited retention, reduced operator access, regional residency, or several of these.
- Map the data path. Include prompts, outputs, logs, embeddings, model assets, keys, and backups. Identify which parties can access each one.
- Choose the boundary that matches the requirement. Consider local hosting for controlled inference when you can operate it; a cloud API when provider handling and contract terms meet the need; or confidential computing when reducing infrastructure-operator access during processing is important.
- Verify the specific implementation. For a provider, review its policy, contract, retention, access, region, and subprocessors. For a TEE, verify what is attested, accepted platform states, key-release policy, regional availability, and what exits the boundary.
- Assign the remaining work. Set owners for access control, data governance, updates, monitoring, incident response, and applicable compliance controls. None of the three deployment choices removes every customer responsibility.
There is no workload-independent cost or performance winner established here. Compare those factors using evidence for your model, traffic, hardware or service configuration, and operating requirements rather than inferring them from the deployment category.
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