Confidential Computing for AI Model Protection: AWS Nitro Enclaves vs. Azure Confidential VMs vs. NVIDIA Confidential Computing

Treat this table as a starting filter, not a final answer. The real decision hinges on the architecture and operational-effort sections below.

The single most important distinction in this space is scope, not brand. "Confidential computing" is a category, but the three products protect different amounts of the stack.

AWS Nitro Enclaves creates a separate, hardened, minimal sub-VM carved out of the resources of a parent EC2 instance. According to AWS's own documentation, the Nitro Hypervisor isolates the enclave's vCPUs and memory so that the parent instance, including root and admin users on it, cannot access the enclave's data or applications. The enclave has no persistent storage and no external networking, only a local socket connection back to the parent instance. This is CPU and memory isolation only. There is no GPU inside a Nitro Enclave, and AWS's documentation and community reports confirm there is no path to attach one: Nitro Enclaves is a CPU-workload product, full stop.

Azure Confidential VMs work at a different layer. Instead of carving out a smaller enclave inside an instance, the entire VM's memory is encrypted using processor-level technology, either AMD SEV-SNP or Intel TDX depending on the VM series. Microsoft's documentation describes this as a hardware-enforced boundary between the application and the virtualization stack itself, meaning the hypervisor and host management code are excluded from the trust boundary. Critically, Microsoft also ships a GPU-capable confidential VM series, NCCadsH100v5, which pairs AMD SEV-SNP host-level encryption with NVIDIA H100 GPUs. Azure was the first major cloud to reach general availability on this combination.

That NCCadsH100v5 series is also the direct overlap point with the third product. NVIDIA Confidential Computing extends the trust boundary from the CPU TEE across the PCIe bus into the GPU itself. NVIDIA's own developer documentation describes a CC-On mode in which command buffers and CUDA kernels are encrypted and signed before crossing the PCIe bus between CPU and GPU, with encrypted bounce buffers used for staging data in shared system memory. GPU high-bandwidth memory itself is not separately encrypted, since NVIDIA's threat model treats on-die GPU memory as already resistant to the physical attacks the CC mode is designed against; the protection is specifically about what leaves the GPU package over the bus and what the hypervisor or a malicious host administrator could otherwise observe. NVIDIA Confidential Computing requires a compatible host CPU TEE (AMD Genoa/Milan with SEV-SNP, or Intel Sapphire Rapids with TDX in early access) underneath it. It is not a cloud-specific product; it is a GPU and driver capability that clouds and on-prem operators expose, which is why it shows up both as Azure's NCC H100 series and, separately, on other infrastructure that has enabled H100 or H200 CC mode.

The three options also differ sharply in what unit of infrastructure they attach to.

Nitro Enclaves is an add-on feature of an existing EC2 instance. You do not deploy a Nitro Enclave on its own; you launch a parent instance built on the AWS Nitro System (most current Intel, AMD, and Graviton instance types qualify) and then create up to four enclaves inside it, each running its own minimal Linux image with no interactive access and no SSH.

Azure Confidential VMs are deployed as a VM size family, not an add-on. You pick a confidential VM size (the DCasv5/DCesv6 general purpose series, ECasv5/ECesv6 memory-optimized series, or the NCCadsH100v5 GPU series) at instance creation time, and the confidential OS disk encryption option is set once and cannot be changed after deployment.

NVIDIA Confidential Computing is a firmware and driver mode, not a separate instance type you provision by name on most infrastructure; it is switched on for supported H100 or H200 hardware and then exposed through whatever VM or container layer the cloud or on-prem operator has built around it. On Azure specifically, that mode is what backs the NCCadsH100v5 series. Elsewhere, it depends entirely on whether the operator (a GPU cloud, a colo, or an on-prem cluster running KVM/QEMU or Kubernetes with Kata containers) has exposed CC mode at all, which is worth confirming before assuming portability.

Each option's integration story is really a statement of which ecosystem it locks a team into, since none of the three works meaningfully outside its own cloud or hardware family.

This is where the three products diverge the most in practice, and where teams most often underestimate the work involved.

Nitro Enclaves generally requires real re-architecture. There is no networking into or out of an enclave except a local vsock channel back to the parent instance; any application code that expects normal IP sockets has to be modified to use vsock directly or to route through a vsock-to-HTTP proxy. There is no persistent storage inside the enclave, no interactive shell access for debugging, and the enclave image itself is built and measured as an Enclave Image File (EIF) whose cryptographic hash becomes part of the attestation document. Setting up the AWS KMS condition-key policy that gates decryption on those measurements is an additional one-time integration step. None of this is exotic, but it is real engineering work, not a checkbox.

Azure Confidential VMs are explicitly designed to minimize this burden. Microsoft's own positioning is that these support "cloud migrations without modifying your code": since the whole VM's memory is encrypted at the processor level, an existing application generally runs unmodified once it is deployed on a confidential VM size and passes the platform's attestation check at boot (which will refuse to start the VM if required settings like SEV-SNP are missing). The operational lift is closer to choosing the right VM SKU, choosing whether to enable confidential OS disk encryption up front (a decision that cannot be changed later), and deciding who owns the encryption keys, rather than rewriting application internals.

NVIDIA Confidential Computing sits close to the Azure model for the GPU layer specifically: NVIDIA's documentation states that CUDA applications should run without any changes once the hardware, driver, and attestation are correctly in place, since the CUDA driver handles the encryption workflow transparently in CC-On mode. The real operational work is provisioning correctly (getting a host with a compatible CPU TEE, enabling CC-On mode, and running an attestation check before releasing any sensitive input to the GPU) and building the attestation verification step into your inference pipeline so a compromised or misconfigured GPU is refused rather than silently trusted. A third mode, CC-DevTools, exists specifically so teams can profile performance with security features off before committing to CC-On in production.

AWS states plainly that there is no additional charge for using Nitro Enclaves; you pay standard EC2 instance pricing for the parent instance and standard pricing for any other AWS services (like KMS) the enclave calls. It is supported in all AWS regions including GovCloud, with the caveat that it is not supported on Outposts, Local Zones, or Wavelength Zones, and a parent instance is capped at four enclaves.

Azure Confidential VMs are priced by VM size like any other Azure VM, through the standard Azure Pricing Calculator; there is no separate confidential-computing surcharge on compute itself, though Microsoft notes the small VMGS (VM guest state) disk that stores security state may carry a modest monthly storage cost, and that encrypted OS disk pricing changes are scheduled to take effect March 30, 2026. Confidential VM sizes, including the NCCadsH100v5 GPU series, are only available in specific regions where the underlying specialized hardware has been deployed, which should be checked against your required region before committing to a design.

NVIDIA Confidential Computing itself carries no separate license fee; CC mode is a firmware capability of the H100/H200 hardware, not a paid add-on SKU. What you actually pay for is the GPU instance itself, at whatever rate the specific cloud or infrastructure provider charges for that instance type. On Azure, that means the NCCadsH100v5 series pricing. On performance, NVIDIA has published throughput overhead figures showing most LLM inference workloads see roughly 2 to 5 percent throughput overhead in CC-On mode, with an independent academic benchmark study (arXiv:2409.03992) similarly reporting overhead below 5 percent for typical query workloads and the primary bottleneck being CPU-GPU I/O over PCIe rather than the GPU's own compute. Attestation overhead is one-time at instance startup, not per-inference-request, and reported in the low single-digit seconds. Treat these as the most credible published figures available rather than guaranteed numbers for your specific model architecture and sequence lengths; validate against your own workload before relying on them for capacity planning.

Nitro Enclaves is the right question only when the workload doing the protecting is itself CPU-bound; when it is, the AWS-native attestation-gated decrypt pattern is one of the more mature patterns in this space.

Azure Confidential VMs trade some flexibility for the lowest integration effort of the three options and, through the NCCadsH100v5 series, the only generally available GPU-inclusive confidential VM on the market today.

NVIDIA Confidential Computing is the only one of the three that reaches into GPU compute itself, which makes it the relevant option whenever the thing being protected is the model inference on the GPU rather than a CPU-side component around it.

Hardware-based confidential computing is not the default answer to "we need to protect our model." It solves a specific threat: an untrusted or partially trusted infrastructure operator, hypervisor, or co-tenant with the technical ability to inspect memory on the host. If that is not actually your threat model, a simpler and cheaper control usually gets you further.

If the real concern is external attackers reaching the model over the network, standard network segmentation, a private endpoint, and strict IAM on who can call the inference API addresses that without any enclave. If the concern is model weights sitting on disk being copied by an insider with storage access, standard encryption at rest with strict key management and access logging covers that case, since a TEE does not additionally protect data at rest any better than a properly configured KMS-backed disk encryption already does. If the driver is a compliance checkbox rather than a concrete adversary in the threat model, it is worth asking the auditor or customer exactly what control they require; some frameworks are satisfied by encryption at rest and in transit plus strong access controls, and jumping straight to confidential computing without confirming the actual requirement adds real integration cost (vsock rewrites, VM SKU migration, attestation pipeline work) for a control that was not actually asked for. Confidential computing earns its cost when the specific concern is: a cloud provider's own employees, a compromised hypervisor, or a malicious co-tenant with root-equivalent access to the physical host. If none of those are in your threat model, that cost is better spent elsewhere.

Run these checks before committing to any of the three options in production; each one is designed to surface a real integration cost or feature gap before it becomes a migration surprise.