Private inference is the practice of running AI model inference on sensitive data in a way that keeps the input data confidential from the AI model's operator, the cloud provider hosting the model, or any external party. It addresses a fundamental tension in enterprise AI deployment: the most capable AI models are typically hosted by cloud providers, but the data on which enterprises need to run those models — patient records, legal documents, financial transactions, proprietary business intelligence — is often too sensitive to send to those providers. Private inference describes the technical approaches and architectures that allow organizations to benefit from AI capabilities without exposing the data those capabilities require.
Think of it like using a locksmith to open a safe without revealing what's inside. The locksmith has the technical expertise you need; you have an asset you don't want them to see. A trusted locksmith arrangement requires you to either let them see the contents (full trust) or find a mechanism by which they can do the work without the contents being visible (private inference). In AI terms, the model is the locksmith, the sensitive data is the safe's contents, and private inference is the mechanism that lets the work happen without the data being visible to the service provider.
For enterprise leaders in regulated industries — healthcare, financial services, legal services, pharmaceuticals, defense — private inference is the architectural category that determines whether AI can be used on their most valuable and sensitive data. HIPAA restricts what patient data can leave a covered entity; GDPR limits cross-border data transfers; attorney-client privilege concerns restrict what can be shared with outside vendors; trade secret law governs proprietary formulas and designs. Private inference is the set of technical solutions that make AI applicable within these constraints.
Imagine a pharmaceutical company that wants to use AI to screen proprietary drug compound libraries for promising candidates. The compound library is the company's core intellectual property — sharing it with an external AI provider would be sharing the crown jewels. Private inference means the AI does the screening without the provider ever seeing the compound structures in plaintext. Depending on the approach used, this might mean the AI runs entirely within the company's own infrastructure, or it runs in a special hardware environment where even the cloud provider can't read the memory, or — in the most cryptographically advanced approach — the computation happens on encrypted data that is never decrypted during processing.
There are four main technical approaches to private inference, ordered from most deployed to most experimental: (1) On-premises inference — the AI model runs on hardware owned and operated by the organization within its own data center. No data leaves the organizational perimeter. This is the most widely deployed form of private inference; it requires the organization to acquire and maintain GPU servers but provides straightforward data containment. Model quantization makes this feasible for organizations without hyperscale GPU infrastructure. (2) Confidential computing — specialized hardware creates a Trusted Execution Environment (TEE) that encrypts data and computation in memory so that even the cloud provider operating the server cannot read the plaintext. Intel SGX, AMD SEV, and NVIDIA's Confidential Computing feature on H100 GPUs provide this capability; the cloud provider can verify they are not reading the data, but the user can verify the integrity of the secure enclave. This approach enables private inference on cloud infrastructure without requiring on-premise hardware. (3) Homomorphic encryption (HE) — inference is performed directly on encrypted data that is never decrypted. The model operates on ciphertext and produces ciphertext outputs that only the data owner can decrypt. This provides the strongest theoretical privacy guarantee but adds 1,000-10,000x computational overhead; practical deployment is limited to specific narrow models and use cases. (4) Secure multi-party computation (SMPC) — computation is distributed across multiple parties such that no single party has access to the complete data. Research-stage for LLM inference; deployed for specific narrow statistical computations.
In healthcare, private inference enables clinical AI applications on patient data that HIPAA's privacy rule would otherwise constrain. A hospital system that has built an on-premises GPU inference cluster can run AI models for clinical note summarization, diagnostic decision support, and treatment protocol lookup directly against electronic health records — without any patient data leaving the facility's network. The combination of model quantization (making capable models fit on available server hardware) and on-premises deployment (satisfying data handling requirements) is the standard architecture for hospital AI programs that operate on identifiable patient data. Healthcare systems including Mayo Clinic, Mass General Brigham, and Kaiser Permanente have published on their on-premises AI inference strategies, reflecting the industry-wide recognition that cloud AI inference on identifiable patient data requires architectural and contractual solutions that on-premises private inference avoids entirely.
In financial services, private inference addresses the tension between AI's value for fraud detection, credit modeling, and risk analysis — all of which require access to detailed transaction-level data — and regulatory requirements for data protection and the competitive sensitivity of proprietary models. Banks and financial institutions running AI inference on-premises or in confidential computing environments can apply AI to transaction data without that data leaving their control. Several major European banks have implemented confidential computing infrastructure specifically for AI inference on customer financial data, satisfying GDPR's data minimization and transfer restriction requirements while enabling AI-powered fraud detection on live transaction streams that would not be permissible if routed through external cloud AI APIs.
The term "private inference" emerged from the intersection of two prior fields: privacy-preserving computation (homomorphic encryption, secure multi-party computation — active research areas since the 1970s-1980s) and practical machine learning deployment (which became a serious engineering discipline in the 2010s). Early privacy-preserving machine learning research focused on training rather than inference — using techniques like federated learning and differential privacy to protect training data. As inference on sensitive data became the primary enterprise AI deployment pattern in 2020-2023, private inference emerged as a distinct applied research and engineering domain.
Confidential computing hardware became commercially available from Intel (SGX, launched in 2015), AMD (SEV, 2016), and most significantly for AI workloads, NVIDIA (Confidential Computing for H100, 2022-2023). The NVIDIA H100's Confidential Computing support was a notable inflection point: it brought TEE-based private inference to the GPU hardware that runs most commercial AI workloads, enabling cloud providers including Microsoft Azure (2023-2024) to offer confidential AI inference as a production service. Simultaneously, the proliferation of open-weight models from 2023 onward made on-premises private inference more feasible by providing capable models that could be self-hosted without provider dependency. As of 2025, on-premises private inference using quantized open-weight models is the most widely deployed production approach; confidential computing is the growing alternative for organizations that need cloud-scale elasticity without on-premises hardware investment.
Private inference enables AI model inference on sensitive data while keeping that data confidential from the model's operator, cloud provider, or external parties. The primary production-ready approaches are on-premises inference (model runs on the organization's own GPU hardware; no data leaves the perimeter) and confidential computing (TEE hardware on cloud infrastructure encrypts data and computation from the provider). Homomorphic encryption and SMPC offer stronger theoretical privacy guarantees but remain too computationally expensive for production LLM deployment. The right approach depends on data sensitivity, regulatory constraints, hardware investment tolerance, and latency requirements.
For enterprise leaders, private inference resolves the core tension between AI capability and data protection — but the resolution requires deliberate architectural decisions made before deployment, not added as compliance afterthoughts. Organizations that determine their regulatory requirements mandate data residency should evaluate on-premises and confidential computing paths with specific reference to their current hardware infrastructure and operational capability. The key question is not whether private inference is achievable — it is — but which approach fits the organization's hardware constraints, required model quality, latency tolerance, and operational maturity. Organizations that have not mapped this decision to their specific regulatory and security requirements are likely underestimating the compliance risk of their current cloud AI inference deployments.
