The 10 Best Confidential Computing Platforms for AI in 2027

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The 10 Best Confidential Computing Platforms for AI in 2027

As AI moves into healthcare, finance, government, and other regulated domains, teams need to run inference and training on sensitive data and protect valuable model weights — without trusting the cloud operator or the host machine. Confidential computing platforms provide hardware-based trusted execution environments (TEEs) that keep data and code encrypted even while in use, verified by remote attestation.

The market spans cloud providers offering confidential VMs and GPUs, chip vendors supplying the silicon, and specialized platforms that make confidential AI easier to deploy. This ranking covers the ten confidential computing platforms AI teams rely on most in 2027.

Direct Answer

Microsoft Azure confidential computing is the best overall platform because it offers the broadest managed lineup — confidential VMs on Intel TDX and AMD SEV-SNP plus confidential GPU VMs with NVIDIA H100 — with mature attestation tooling. Google Cloud Confidential Computing is the best value because Confidential VMs add protection with minimal configuration and little to no price premium on supported machine types, making confidential computing nearly free to turn on.

Your choice depends on whether you want a managed cloud, the underlying chip technology, or a specialized confidential-AI platform.

How We Ranked These

We evaluated each platform on five criteria: TEE coverage (CPU and especially GPU confidential computing for AI), attestation (strength and ease of remote attestation and key release), AI workload fit (support for training and inference, frameworks, and accelerators), operability (managed vs.

Raw hardware, integration, and tooling), and ecosystem/standards (interoperability and open standards). Because protecting AI workloads now hinges on confidential GPUs and verifiable attestation, we weight TEE coverage and attestation most heavily.

flowchart LR DATA[Sensitive data + model] --> TEE[Confidential VM / GPU TEE] TEE --> ATT[Remote attestation] ATT -->|verified| KEYS[Key release / run] ATT -->|failed| STOP[Refuse] KEYS --> AI[Confidential training / inference]

1. Microsoft Azure Confidential Computing 🏆 BEST OVERALL

Azure confidential computing is the most complete managed offering. It provides confidential VMs built on Intel TDX and AMD SEV-SNP, application enclaves via Intel SGX, and confidential GPU VMs pairing AMD SEV-SNP CPUs with NVIDIA H100 confidential computing — so entire AI workloads, including GPU acceleration, run protected.

Microsoft Azure Attestation provides a unified attestation service for releasing secrets only to verified environments.

What it is: managed confidential VMs, enclaves, and confidential GPUs. Strengths: broadest TEE lineup, confidential H100 GPUs, strong attestation tooling, deep enterprise integration. Best for: enterprises wanting end-to-end confidential AI in the cloud. Pricing/availability: pay-as-you-go; some confidential SKUs carry a premium.

2. Google Cloud Confidential Computing 💎 BEST VALUE

Google Cloud Confidential Computing lets you run Confidential VMs on AMD SEV-SNP and Intel TDX with encryption of data in use, often with little to no performance or price premium on supported machine types — so it is remarkably easy and cheap to enable. Confidential Space adds a hardened environment for multi-party data collaboration with attestation, ideal for joint AI on combined datasets.

What it is: confidential VMs plus Confidential Space for multi-party workloads. Strengths: easy to enable, low/no premium, strong multi-party collaboration story, attestation built in. Best for: teams wanting confidential computing with minimal friction.

Pricing/availability: standard VM pricing on supported types; Confidential Space included.

3. AWS (Nitro Enclaves + Nitro System)

AWS takes a distinct architectural approach. The Nitro System offloads virtualization to dedicated hardware and is designed so that even AWS operators cannot access customer data, and AWS Nitro Enclaves create isolated compute environments carved from an EC2 instance with cryptographic attestation for processing highly sensitive data.

It integrates with KMS for attestation-gated key release.

What it is: isolated enclaves and an operator-excluded virtualization system. Strengths: strong isolation model, KMS-integrated attestation, huge AWS ecosystem. Best for: AWS-centric teams protecting sensitive data. Pricing/availability: Nitro Enclaves available at no extra charge on supported instances.

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4. NVIDIA Confidential Computing (H100 / H200)

NVIDIA Confidential Computing is the silicon that makes confidential AI possible on accelerators. On H100 and H200 GPUs, it encrypts and isolates GPU memory and supports device attestation, so model weights and data stay protected during accelerated training and inference.

It is the foundation underneath the confidential GPU offerings of the major clouds.

What it is: GPU-level confidential computing technology. Strengths: protects accelerated AI workloads, hardware attestation, basis for cloud confidential GPUs. Best for: any confidential workload needing GPU acceleration. Pricing/availability: via cloud confidential-GPU SKUs and supported hardware.

5. Intel (TDX, SGX) + Tiber Trust Services

Intel supplies two key technologies: TDX for VM-level confidential computing and SGX for fine-grained application enclaves, plus Intel Tiber Trust Services (attestation as a service) to verify TEEs independently of where they run. Intel's stack underpins confidential VMs across multiple clouds and on-prem deployments.

What it is: confidential-computing silicon plus independent attestation service. Strengths: VM and enclave options, vendor-neutral attestation, broad cloud support. Best for: teams wanting Intel-based TEEs and portable attestation. Pricing/availability: in hardware; attestation service tiers.

6. AMD SEV-SNP

AMD SEV-SNP (Secure Encrypted Virtualization — Secure Nested Paging) encrypts VM memory and adds integrity protection against malicious hypervisors, powering confidential VMs across Azure, Google Cloud, and others. Because it enables lift-and-shift of whole VMs with minimal changes, it is one of the most widely deployed foundations for confidential AI compute.

What it is: VM-memory-encryption technology for confidential VMs. Strengths: lift-and-shift simplicity, integrity protection, broad cloud availability. Best for: running existing VM workloads confidentially. Pricing/availability: via cloud confidential-VM SKUs.

7. Anjuna Security

Anjuna is a confidential computing software platform that makes it easy to run unmodified applications inside TEEs across clouds. It abstracts the complexity of enclaves and attestation so teams can deploy confidential workloads — including AI — without rewriting code, targeting enterprises that want confidential computing without deep low-level work.

What it is: software platform that runs apps in TEEs without code changes. Strengths: no-rewrite deployment, multi-cloud, simplified attestation. Best for: enterprises wanting confidential workloads fast. Pricing/availability: commercial, enterprise licensing.

8. Edgeless Systems (Constellation / Continuum)

Edgeless Systems builds open-source-rooted confidential computing tools, including Constellation (a confidential Kubernetes that runs entire clusters in TEEs) and Continuum, an AI platform that keeps prompts and model data confidential during inference. It is a strong fit for teams that want confidential AI on Kubernetes with verifiable attestation.

What it is: confidential Kubernetes and confidential AI inference platform. Strengths: confidential k8s, AI-specific inference protection, attestation, open foundations. Best for: cloud-native teams wanting confidential AI clusters. Pricing/availability: open-source plus commercial offerings.

9. Fortanix (Confidential Computing Manager)

Fortanix offers a confidential computing platform and Data Security Manager that help organizations run and orchestrate enclave-based workloads with centralized key management and attestation. It is widely used in finance and regulated industries to protect data in use alongside encryption and key lifecycle management.

What it is: confidential computing orchestration plus key management. Strengths: enterprise key management, enclave orchestration, regulated-industry focus. Best for: organizations needing confidential compute with strong KMS. Pricing/availability: commercial, enterprise licensing.

10. IBM Cloud Hyper Protect Services

IBM Cloud Hyper Protect provides confidential computing built on IBM's Secure Execution technology (rooted in LinuxONE/Z), offering a high-assurance "technical assurance" model where IBM cannot access workload data. It is favored in highly regulated sectors needing strong, auditable confidentiality guarantees for sensitive AI and data workloads.

What it is: high-assurance confidential cloud services. Strengths: strong technical-assurance model, regulated-industry pedigree, key protection. Best for: banks and regulated enterprises with strict assurance needs. Pricing/availability: IBM Cloud service pricing.

How to choose the right confidential computing platform

Start with your cloud and your workload. If you are already on a hyperscaler, its native offering (Azure, Google Cloud, or AWS) is usually the path of least resistance — and if you need GPU-accelerated confidential AI, prioritize platforms exposing NVIDIA H100/H200 confidential GPUs, since CPU-only TEEs cannot protect accelerated training and inference.

If you want minimal change, AMD SEV-SNP-based confidential VMs let you lift-and-shift; if you need the tightest boundary, enclave approaches (SGX, Nitro Enclaves) help. For multi-cloud or no-rewrite deployment, specialized platforms like Anjuna, Edgeless, or Fortanix abstract the complexity.

Whatever you choose, make attestation central: ensure secrets and data are released only after the environment is cryptographically verified, and confirm the platform's attestation integrates with your key management.

flowchart TD A[Choosing a platform] --> B{Need GPU-accelerated AI?} B -->|Yes| C[Azure / NVIDIA H100 confidential GPUs] B -->|No| D{On a hyperscaler?} D -->|Yes| E[Azure / Google / AWS native] D -->|Multi-cloud / no rewrite| F[Anjuna / Edgeless / Fortanix]

Frequently Asked Questions

Do I need confidential GPUs or are confidential CPUs enough?

If your AI workload runs on GPUs — most training and large-model inference — you need confidential GPUs (NVIDIA H100/H200), because a CPU-only TEE leaves data exposed the moment it moves onto the accelerator. For CPU-bound workloads, confidential VMs suffice.

Is confidential computing only available in the cloud?

No. The major clouds make it easiest to consume, but the underlying technologies (Intel TDX/SGX, AMD SEV-SNP, NVIDIA confidential GPUs) and platforms like Fortanix, Anjuna, and IBM Secure Execution also support on-premises and hybrid deployments.

How important is attestation when picking a platform?

Critical. Encryption without verification is meaningless — attestation is what proves your workload is in a genuine, unmodified TEE before any secrets are released. Favor platforms with strong, well-integrated, ideally independent attestation services.

Will confidential computing slow down my AI workloads?

There is some overhead from memory encryption and attestation, but it has fallen substantially and is modest for many AI workloads, especially with confidential GPUs. Benchmark your specific case, as the impact varies by platform and model size.

Can multiple organizations train a model on shared data confidentially?

Yes. Platforms like Google Confidential Space and confidential Kubernetes environments are designed for multi-party computation, letting several parties combine data inside a TEE so no party — or the host — sees the others' raw data.

Which platform is best if I already use a major cloud?

Use that cloud's native confidential computing first: Azure for the broadest lineup including confidential GPUs, Google Cloud for easy low-cost confidential VMs and Confidential Space, and AWS for Nitro Enclaves and its operator-excluded Nitro System.

Sources

Microsoft Azure confidential computing documentation and Azure Attestation.
Google Cloud Confidential VMs and Confidential Space documentation.
AWS Nitro Enclaves and AWS Nitro System security documentation.
NVIDIA Confidential Computing (H100/H200) documentation.
Intel TDX, SGX, and Tiber Trust Services documentation.
AMD SEV-SNP technical documentation.
Anjuna, Edgeless Systems (Constellation/Continuum), Fortanix, and IBM Cloud Hyper Protect product documentation.
Confidential Computing Consortium (Linux Foundation) resources.

Keep reading

![Confidential computing platforms for AI cover](https://image.pollinations.ai/prompt/confidential%20computing%20platforms%20for%20AI%20encrypted%20enclaves%20confidential%20GPUs%20attestation%20cloud%20data%20centers%20glowing%20emerald%20diagram?width=1280&height=720&nologo=true)

# The 10 Best Confidential Computing Platforms for AI in 2027

As AI moves into healthcare, finance, government, and other regulated domains, teams need to run inference and training on sensitive data and protect valuable model weights — without trusting the cloud operator or the host machine. **Confidential computing platforms** provide hardware-based trusted execution environments (TEEs) that keep data and code encrypted even while in use, verified by remote attestation. The market spans cloud providers offering confidential VMs and GPUs, chip vendors supplying the silicon, and specialized platforms that make confidential AI easier to deploy. This ranking covers the ten confidential computing platforms AI teams rely on most in 2027.

### Direct Answer
**Microsoft Azure confidential computing** is the best overall platform because it offers the broadest managed lineup — confidential VMs on Intel TDX and AMD SEV-SNP plus confidential GPU VMs with NVIDIA H100 — with mature attestation tooling. **Google Cloud Confidential Computing** is the best value because Confidential VMs add protection with minimal configuration and little to no price premium on supported machine types, making confidential computing nearly free to turn on. Your choice depends on whether you want a managed cloud, the underlying chip technology, or a specialized confidential-AI platform.

## How We Ranked These
We evaluated each platform on five criteria: **TEE coverage** (CPU and especially GPU confidential computing for AI), **attestation** (strength and ease of remote attestation and key release), **AI workload fit** (support for training and inference, frameworks, and accelerators), **operability** (managed vs. Raw hardware, integration, and tooling), and **ecosystem/standards** (interoperability and open standards). Because protecting AI workloads now hinges on confidential GPUs and verifiable attestation, we weight TEE coverage and attestation most heavily.

```mermaid
flowchart LR
    DATA[Sensitive data + model] --> TEE[Confidential VM / GPU TEE]
    TEE --> ATT[Remote attestation]
    ATT -->|verified| KEYS[Key release / run]
    ATT -->|failed| STOP[Refuse]
    KEYS --> AI[Confidential training / inference]
```

## 1. Microsoft Azure Confidential Computing 🏆 BEST OVERALL
**Azure confidential computing** is the most complete managed offering. It provides confidential VMs built on **Intel TDX** and **AMD SEV-SNP**, application enclaves via **Intel SGX**, and **confidential GPU VMs** pairing AMD SEV-SNP CPUs with **NVIDIA H100** confidential computing — so entire AI workloads, including GPU acceleration, run protected. Microsoft Azure Attestation provides a unified attestation service for releasing secrets only to verified environments.

**What it is:** managed confidential VMs, enclaves, and confidential GPUs. **Strengths:** broadest TEE lineup, confidential H100 GPUs, strong attestation tooling, deep enterprise integration. **Best for:** enterprises wanting end-to-end confidential AI in the cloud. **Pricing/availability:** pay-as-you-go; some confidential SKUs carry a premium.

## 2. Google Cloud Confidential Computing 💎 BEST VALUE
**Google Cloud Confidential Computing** lets you run **Confidential VMs** on AMD SEV-SNP and Intel TDX with encryption of data in use, often with little to no performance or price premium on supported machine types — so it is remarkably easy and cheap to enable. **Confidential Space** adds a hardened environment for multi-party data collaboration with attestation, ideal for joint AI on combined datasets.

**What it is:** confidential VMs plus Confidential Space for multi-party workloads. **Strengths:** easy to enable, low/no premium, strong multi-party collaboration story, attestation built in. **Best for:** teams wanting confidential computing with minimal friction. **Pricing/availability:** standard VM pricing on supported types; Confidential Space included.

## 3. AWS (Nitro Enclaves + Nitro System)
**AWS** takes a distinct architectural approach. The **Nitro System** offloads virtualization to dedicated hardware and is designed so that even AWS operators cannot access customer data, and **AWS Nitro Enclaves** create isolated compute environments carved from an EC2 instance with cryptographic attestation for processing highly sensitive data. It integrates with KMS for attestation-gated key release.

**What it is:** isolated enclaves and an operator-excluded virtualization system. **Strengths:** strong isolation model, KMS-integrated attestation, huge AWS ecosystem. **Best for:** AWS-centric teams protecting sensitive data. **Pricing/availability:** Nitro Enclaves available at no extra charge on supported instances.


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## 4. NVIDIA Confidential Computing (H100 / H200)
**NVIDIA Confidential Computing** is the silicon that makes confidential AI possible on accelerators. On **H100 and H200** GPUs, it encrypts and isolates GPU memory and supports device attestation, so model weights and data stay protected during accelerated training and inference. It is the foundation underneath the confidential GPU offerings of the major clouds.

**What it is:** GPU-level confidential computing technology. **Strengths:** protects accelerated AI workloads, hardware attestation, basis for cloud confidential GPUs. **Best for:** any confidential workload needing GPU acceleration. **Pricing/availability:** via cloud confidential-GPU SKUs and supported hardware.

## 5. Intel (TDX, SGX) + Tiber Trust Services
**Intel** supplies two key technologies: **TDX** for VM-level confidential computing and **SGX** for fine-grained application enclaves, plus **Intel Tiber Trust Services** (attestation as a service) to verify TEEs independently of where they run. Intel's stack underpins confidential VMs across multiple clouds and on-prem deployments.

**What it is:** confidential-computing silicon plus independent attestation service. **Strengths:** VM and enclave options, vendor-neutral attestation, broad cloud support. **Best for:** teams wanting Intel-based TEEs and portable attestation. **Pricing/availability:** in hardware; attestation service tiers.

## 6. AMD SEV-SNP
**AMD SEV-SNP (Secure Encrypted Virtualization — Secure Nested Paging)** encrypts VM memory and adds integrity protection against malicious hypervisors, powering confidential VMs across Azure, Google Cloud, and others. Because it enables lift-and-shift of whole VMs with minimal changes, it is one of the most widely deployed foundations for confidential AI compute.

**What it is:** VM-memory-encryption technology for confidential VMs. **Strengths:** lift-and-shift simplicity, integrity protection, broad cloud availability. **Best for:** running existing VM workloads confidentially. **Pricing/availability:** via cloud confidential-VM SKUs.

## 7. Anjuna Security
**Anjuna** is a confidential computing software platform that makes it easy to run unmodified applications inside TEEs across clouds. It abstracts the complexity of enclaves and attestation so teams can deploy confidential workloads — including AI — without rewriting code, targeting enterprises that want confidential computing without deep low-level work.

**What it is:** software platform that runs apps in TEEs without code changes. **Strengths:** no-rewrite deployment, multi-cloud, simplified attestation. **Best for:** enterprises wanting confidential workloads fast. **Pricing/availability:** commercial, enterprise licensing.

## 8. Edgeless Systems (Constellation / Continuum)
**Edgeless Systems** builds open-source-rooted confidential computing tools, including **Constellation** (a confidential Kubernetes that runs entire clusters in TEEs) and **Continuum**, an AI platform that keeps prompts and model data confidential during inference. It is a strong fit for teams that want confidential AI on Kubernetes with verifiable attestation.

**What it is:** confidential Kubernetes and confidential AI inference platform. **Strengths:** confidential k8s, AI-specific inference protection, attestation, open foundations. **Best for:** cloud-native teams wanting confidential AI clusters. **Pricing/availability:** open-source plus commercial offerings.

## 9. Fortanix (Confidential Computing Manager)
**Fortanix** offers a confidential computing platform and Data Security Manager that help organizations run and orchestrate enclave-based workloads with centralized key management and attestation. It is widely used in finance and regulated industries to protect data in use alongside encryption and key lifecycle management.

**What it is:** confidential computing orchestration plus key management. **Strengths:** enterprise key management, enclave orchestration, regulated-industry focus. **Best for:** organizations needing confidential compute with strong KMS. **Pricing/availability:** commercial, enterprise licensing.

## 10. IBM Cloud Hyper Protect Services
**IBM Cloud Hyper Protect** provides confidential computing built on IBM's **Secure Execution** technology (rooted in LinuxONE/Z), offering a high-assurance "technical assurance" model where IBM cannot access workload data. It is favored in highly regulated sectors needing strong, auditable confidentiality guarantees for sensitive AI and data workloads.

**What it is:** high-assurance confidential cloud services. **Strengths:** strong technical-assurance model, regulated-industry pedigree, key protection. **Best for:** banks and regulated enterprises with strict assurance needs. **Pricing/availability:** IBM Cloud service pricing.

## How to choose the right confidential computing platform
Start with your cloud and your workload. If you are already on a hyperscaler, its native offering (Azure, Google Cloud, or AWS) is usually the path of least resistance — and if you need GPU-accelerated confidential AI, prioritize platforms exposing **NVIDIA H100/H200 confidential GPUs**, since CPU-only TEEs cannot protect accelerated training and inference. If you want minimal change, AMD SEV-SNP-based confidential VMs let you lift-and-shift; if you need the tightest boundary, enclave approaches (SGX, Nitro Enclaves) help. For multi-cloud or no-rewrite deployment, specialized platforms like Anjuna, Edgeless, or Fortanix abstract the complexity. Whatever you choose, make **attestation** central: ensure secrets and data are released only after the environment is cryptographically verified, and confirm the platform's attestation integrates with your key management.

```mermaid
flowchart TD
    A[Choosing a platform] --> B{Need GPU-accelerated AI?}
    B -->|Yes| C[Azure / NVIDIA H100 confidential GPUs]
    B -->|No| D{On a hyperscaler?}
    D -->|Yes| E[Azure / Google / AWS native]
    D -->|Multi-cloud / no rewrite| F[Anjuna / Edgeless / Fortanix]
```

## Frequently Asked Questions

### Do I need confidential GPUs or are confidential CPUs enough?
If your AI workload runs on GPUs — most training and large-model inference — you need **confidential GPUs** (NVIDIA H100/H200), because a CPU-only TEE leaves data exposed the moment it moves onto the accelerator. For CPU-bound workloads, confidential VMs suffice.

### Is confidential computing only available in the cloud?
No. The major clouds make it easiest to consume, but the underlying technologies (Intel TDX/SGX, AMD SEV-SNP, NVIDIA confidential GPUs) and platforms like Fortanix, Anjuna, and IBM Secure Execution also support on-premises and hybrid deployments.

### How important is attestation when picking a platform?
Critical. Encryption without verification is meaningless — attestation is what proves your workload is in a genuine, unmodified TEE before any secrets are released. Favor platforms with strong, well-integrated, ideally independent attestation services.

### Will confidential computing slow down my AI workloads?
There is some overhead from memory encryption and attestation, but it has fallen substantially and is modest for many AI workloads, especially with confidential GPUs. Benchmark your specific case, as the impact varies by platform and model size.

### Can multiple organizations train a model on shared data confidentially?
Yes. Platforms like Google Confidential Space and confidential Kubernetes environments are designed for multi-party computation, letting several parties combine data inside a TEE so no party — or the host — sees the others' raw data.

### Which platform is best if I already use a major cloud?
Use that cloud's native confidential computing first: Azure for the broadest lineup including confidential GPUs, Google Cloud for easy low-cost confidential VMs and Confidential Space, and AWS for Nitro Enclaves and its operator-excluded Nitro System.

## Sources
- Microsoft Azure confidential computing documentation and Azure Attestation.
- Google Cloud Confidential VMs and Confidential Space documentation.
- AWS Nitro Enclaves and AWS Nitro System security documentation.
- NVIDIA Confidential Computing (H100/H200) documentation.
- Intel TDX, SGX, and Tiber Trust Services documentation.
- AMD SEV-SNP technical documentation.
- Anjuna, Edgeless Systems (Constellation/Continuum), Fortanix, and IBM Cloud Hyper Protect product documentation.
- Confidential Computing Consortium (Linux Foundation) resources.

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The 10 Best Confidential Computing Platforms for AI in 2027

The 10 Best Confidential Computing Platforms for AI in 2027

Direct Answer

How We Ranked These

1. Microsoft Azure Confidential Computing 🏆 BEST OVERALL

2. Google Cloud Confidential Computing 💎 BEST VALUE

3. AWS (Nitro Enclaves + Nitro System)

4. NVIDIA Confidential Computing (H100 / H200)

5. Intel (TDX, SGX) + Tiber Trust Services

6. AMD SEV-SNP

7. Anjuna Security

8. Edgeless Systems (Constellation / Continuum)

9. Fortanix (Confidential Computing Manager)

10. IBM Cloud Hyper Protect Services

How to choose the right confidential computing platform

Frequently Asked Questions

Do I need confidential GPUs or are confidential CPUs enough?

Is confidential computing only available in the cloud?

How important is attestation when picking a platform?

Will confidential computing slow down my AI workloads?

Can multiple organizations train a model on shared data confidentially?

Which platform is best if I already use a major cloud?

Sources

What does the score mean?