Confidential Computing Cloud Comparison: Phala vs AWS vs Azure vs GCP

TL;DR: Confidential computing is available across major cloud providers, but implementations vary widely.

• Phala Cloud — Provides zero-trust TEE infrastructure with Intel TDX, AMD SEV-SNP, and NVIDIA H100/H200 GPU TEE support. Features include public attestation, Dstack SDK, and multi-TEE compatibility for AI workloads.
• AWS Nitro Enclaves — Offers secure enclaves but still requires trust in AWS, with limited transparency and no GPU TEE.
• Azure Confidential Computing — Offers comprehensive TEE support, with Azure Attestation service for verification.
• GCP Confidential VMs — Provides AMD SEV encryption but offers limited attestation visibility and no GPU support yet.

This guide compares security models, TEE support, attestation methods, pricing, and use cases to help you choose the right platform.

Introduction

The confidential computing landscape has evolved from niche technology to mainstream cloud offerings. All major providers now support some form of hardware-protected execution, but the devil is in the details: trust models, attestation transparency, TEE technology support, and pricing structures vary dramatically.

This comprehensive comparison analyzes Phala Cloud, AWS, Azure, and GCP across the dimensions that matter for production confidential AI: security model, TEE hardware support, attestation and verification, developer experience, performance, pricing, and compliance. You’ll learn which platform best fits your requirements and how to navigate the tradeoffs.

What you’ll learn:

• Security model comparison (zero-trust vs. trusted provider)
• TEE technology support (TDX, SEV-SNP, Nitro, SGX)
• Attestation transparency and verification
• GPU TEE support for confidential AI
• Pricing and total cost of ownership
• Migration strategies and multi-cloud patterns

Quick Comparison Matrix

Phala Cloud: Zero-Trust Confidential Computing

Architecture and Trust Model

Core principle: Don’t trust any provider, including Phala.

TEE Technology Support

• CPU TEE:
• ✅ Intel TDX (Trust Domain Extensions) - VM-level isolation
• ✅ AMD SEV-SNP (Secure Encrypted Virtualization) - VM-level encryption
• ✅ Intel SGX (Software Guard Extensions) - Process-level enclaves
• GPU TEE (Unique to Phala):
• ✅ NVIDIA H100 Confidential Computing
• ✅ NVIDIA H200 Confidential Computing
• Performance: 95-99% of native (minimal overhead)

Attestation and Verification

Phala Trust Center:

• Public attestation repository: trust-center.phala.network
• Every deployment gets public attestation URL
• Open source verification tools
• Real-time continuous attestation (5-minute intervals)

Attestation includes:

• Hardware measurements (proving genuine TEE)
• Code hash (Docker image SHA256)
• Configuration hash (environment variables)
• Dstack version
• Timestamp and nonce (freshness)

Client verification:

from phala_trust_center import verify_attestation

result = verify_attestation(
    url="https://trust-center.phala.network/attestations/abc123",
    expected_code_hash="sha256:def456..."
)
if result.valid:
    print(f"✓ Running in genuine {result.tee_type}")
    print(f"✓ Code matches expected hash")
    # Safe to send sensitive data

Developer Experience

Deployment (Docker to TEE):

# 1. Build standard Docker image
docker build -t my-ai-app:v1 .
# 2. Deploy to Phala Cloud with Dstack
dstack deploy \
  --image my-ai-app:v1 \
  --tee-type tdx \
  --gpu h100 \
  --phala-cloud

Pricing (as of Q1 2025):

Strengths and Limitations

✅ Strengths:

• Zero-trust security model (don’t trust Phala)
• Only provider with GPU TEE (H100/H200)
• Public attestation verification
• Open source Dstack SDK
• Purpose-built for confidential AI
• Best performance-to-cost ratio for AI workloads

⚠️ Limitations:

• Smaller ecosystem than AWS/Azure/GCP
• Fewer global regions (expanding)
• Newer platform (less enterprise tooling)

Best for:

• Confidential AI inference and training
• Zero-trust requirements
• GPU-accelerated confidential computing
• Projects requiring attestation transparency

AWS: Nitro Enclaves and Confidential Computing

Architecture and Trust Model

Core principle: Trust AWS as the provider.

TEE Technology Support

• AWS Nitro Enclaves:
• Custom AWS-designed TEE (not standard TDX/SEV-SNP)
• Isolated VM with CPU and memory allocation
• No persistent storage (security feature)
• Communication via vsock (virtual socket)
• No GPU TEE:
• Standard GPU instances available
• But no confidential computing protection for GPUs
• GPU memory not encrypted

Attestation and Verification

AWS Nitro Attestation:

• Attestation document signed by NitroTPM
• Includes: PCR values, enclave measurements, timestamp
• Verification requires AWS SDK
• Not publicly auditable (requires AWS account)

Attestation process:

import boto3
import json

# Get attestation document
attestation = get_attestation_document(
    nonce=user_nonce,
    public_key=user_public_key
)

Developer Experience

Deployment:

# 1. Build enclave image (EIF format)
nitro-cli build-enclave \
  --docker-uri my-app:latest \
  --output-file my-app.eif
# 2. Launch parent EC2 instance
aws ec2 run-instances \
  --instance-type m5.xlarge \
  --enclave-options 'Enabled=true'
# 3. Run enclave from parent instance
nitro-cli run-enclave \
  --eif-path my-app.eif \
  --memory 4096 \
  --cpu-count 2

Pricing (as of Q1 2025):

Strengths and Limitations

✅ Strengths:

• Mature AWS ecosystem integration
• Wide global availability (all regions)
• Strong compliance certifications (FedRAMP, etc.)
• Integration with AWS KMS, IAM, CloudWatch

⚠️ Limitations:

• Must trust AWS (not zero-trust)
• No GPU TEE support
• Closed-source Nitro system
• Complex development workflow
• Limited attestation transparency
• No persistent storage in enclaves

Best for:

• AWS-native architectures
• Compliance-driven requirements (FedRAMP)
• Applications already on AWS
• Scenarios where trusting AWS is acceptable

Azure: Confidential Computing Platform

Architecture and Trust Model

Core principle: Trust Microsoft, but with more transparency than AWS.

TEE Technology Support

• CPU TEE:
• ✅ Intel TDX (DCesv5, ECesv5 series)
• ✅ AMD SEV-SNP (DCasv5, ECasv5 series)
• ✅ Intel SGX (DC-series v2/v3)
• No GPU TEE:
• NCads H100 v5 instances (H100 GPUs)
• But no confidential computing mode
• GPU memory not encrypted

Attestation and Verification

Azure Attestation:

• Public REST API for attestation verification
• Supports custom attestation policies
• JWT-based attestation tokens
• More transparent than AWS

Attestation example:

import httpx

# Get attestation token from VM
attestation_token = get_attestation_token_from_vm()

# Verify with Azure Attestation (publicly accessible)
async with httpx.AsyncClient() as client:
    response = await client.post(
        "https://myattestation.attest.azure.net/attest/TdxVm",
        json={"token": attestation_token}
    )
    attestation_result = response.json()

Developer Experience

Deployment:

# 1. Create Confidential VM with Azure CLI
az vm create \
  --resource-group myRG \
  --name myConfidentialVM \
  --image Ubuntu2204 \
  --size Standard_DC4es_v5 \
  --enable-secure-boot true \
  --enable-vtpm true
# 2. SSH and deploy application
# 3. Configure attestation
az attestation create \
  --name myAttestation \
  --resource-group myRG \
  --location eastus

Pricing (as of Q1 2025):

Strengths and Limitations

✅ Strengths:

• Broadest CPU TEE support (TDX, SEV-SNP, SGX)
• Standard VM experience (easier than AWS Nitro)
• Azure Attestation with public API
• Strong Azure ecosystem integration
• Good documentation and tooling

⚠️ Limitations:

• Must trust Microsoft
• No GPU TEE support
• Higher pricing than Phala Cloud
• Attestation still Microsoft-controlled
• Limited to Azure ecosystem

Best for:

• Azure-native architectures
• Organizations already on Azure
• Need for multiple TEE options (TDX/SEV-SNP/SGX)
• Standard VM workflows preferred

GCP: Confidential VMs

Architecture and Trust Model

Core principle: Trust Google, with minimal transparency.

TEE Technology Support

• CPU TEE:
• ✅ AMD SEV (N2D series)
• ⚠️ Limited to AMD SEV (no TDX, no SEV-SNP, no SGX)
• No GPU TEE:
• A100 and H100 instances available
• No confidential computing mode

Attestation and Verification

GCP Attestation:

• Shielded VM with vTPM
• Minimal public attestation API
• Least transparent among major clouds
• Difficult to verify independently

Limited attestation example:

from google.cloud import compute_v1

# Get integrity monitoring data
client = compute_v1.InstancesClient()
instance = client.get(project=project_id, zone=zone, instance=instance_name)

Developer Experience

Deployment:

# Create Confidential VM
gcloud compute instances create my-confidential-vm \
  --zone=us-central1-a \
  --machine-type=n2d-standard-4 \
  --confidential-compute \
  --maintenance-policy=TERMINATE

Pricing (as of Q1 2025):

Strengths and Limitations

✅ Strengths:

• Simple GCE VM workflow
• Good GCP ecosystem integration
• Competitive pricing for CPU

⚠️ Limitations:

• Must trust Google
• Only AMD SEV (no TDX, SEV-SNP, SGX)
• Minimal attestation support
• Least transparency of major clouds
• No GPU TEE
• Poor documentation for confidential computing

Best for:

• GCP-native architectures
• Simple confidential computing needs
• Cost-sensitive CPU workloads
• Scenarios where limited attestation is acceptable

Side-by-Side Comparison

Security Model Comparison

Winner: Phala Cloud - Zero-trust model with full transparency.

TEE Technology Comparison

Winner: Phala Cloud - Only GPU TEE support; Azure wins for CPU diversity.

Performance Comparison

CPU TEE Overhead:

GPU Performance (H100):

Winner: Phala Cloud - Only GPU TEE option; CPU performance comparable to Azure.

Pricing Comparison (Confidential AI Workload)

Scenario: 4 vCPU, 16GB RAM + 1x H100 GPU (8 hours/day, 30 days)

Winner: Phala Cloud - 92% cheaper than AWS, 45% cheaper than Azure, with GPU TEE protection.

Developer Experience Comparison

Winner: Tie - Phala Cloud simplest; AWS/Azure/GCP have larger ecosystems.

Use Case Recommendations

Confidential AI Inference

Recommended: [Phala Cloud](https://phala.com/solutions/private-ai-inference)

Why:

• Only GPU TEE support (H100/H200)
• Best performance-to-cost ratio
• Zero-trust security model
• Purpose-built for confidential AI

Example:

# Deploy LLM inference on Phala Cloud
dstack deploy \
  --image deepseek-chat:latest \
  --tee-type tdx \
  --gpu h100 \
  --memory 80Gi

Enterprise AWS-Native Architecture

Recommended: AWS Nitro Enclaves

Why:

• Best AWS ecosystem integration
• Compliance certifications (FedRAMP)
• Existing AWS investment
• IAM/KMS integration

Tradeoffs:

• No GPU TEE
• Must trust AWS
• Higher cost
• Complex workflow

Multi-Cloud Strategy

Recommended: Phala Cloud + Azure

Why:

• Phala: GPU TEE workloads (confidential AI)
• Azure: CPU TEE diversity (TDX/SEV-SNP/SGX)
• Both offer good attestation transparency
• Avoid AWS/GCP lock-in

Cost-Sensitive CPU Workloads

Recommended: GCP Confidential VMs

Why:

• Lowest CPU pricing
• Simple workflow
• Adequate for non-critical confidential computing

Tradeoffs:

• Minimal attestation
• Must trust Google
• Only AMD SEV

Healthcare/Financial Compliance

Recommended: Azure Confidential Computing

Why:

• Strong compliance certifications
• Broad TEE support
• Azure Attestation transparency
• Mature security tooling

Alternative: [Phala Cloud](https://phala.com/solutions/confidential-training)

• If zero-trust model required
• Better for regulated AI workloads

Migration Strategies

AWS to Phala Cloud

# Step 1: Export AWS Nitro enclave image
aws s3 cp s3://my-bucket/my-enclave.eif ./
# Step 2: Extract Docker image from EIF
nitro-cli describe-eif --eif-path my-enclave.eif > eif-info.json
# Step 3: Deploy to Phala Cloud
dstack deploy \
  --image my-app:latest \
  --tee-type tdx \
  --phala-cloud

Azure to Phala Cloud

# Step 1: Export Azure VM image
az vm show --name myVM --resource-group myRG
# Step 2: Containerize application
# Step 3: Deploy to Phala Cloud
dstack deploy \
  --image my-azure-app:latest \
  --tee-type tdx \
  --phala-cloud

Multi-Cloud Architecture

# Multi-cloud deployment strategy
# Kubernetes with Phala + Azure
# Phala Cloud: GPU TEE workloads
apiVersion: v1
kind: Pod
metadata:
  name: confidential-ai-training
spec:
  nodeSelector:
    cloud.provider: phala
    tee.type: nvidia-h100
  containers:
  - name: trainer
    image: my-model-training:v1
    resources:
      limits:
        nvidia.com/gpu: 4
# Azure: CPU TEE workloads
apiVersion: v1
kind: Pod
metadata:
  name: confidential-data-processing
spec:
  nodeSelector:
    cloud.provider: azure
    tee.type: intel-tdx
  runtimeClassName: kata-tdx
  containers:
  - name: processor
    image: my-data-processor:v1

Decision Framework

Choose Phala Cloud if:

✅ Need GPU TEE for confidential AI

✅ Require zero-trust security model

✅ Want public attestation transparency

✅ Prefer open source tools (Dstack SDK)

✅ Cost-sensitive for GPU workloads

✅ Building confidential AI products

Choose AWS if:

✅ Existing AWS investment

✅ Need AWS-specific services (IAM, KMS, etc.)

✅ Compliance requires FedRAMP certification

✅ Willing to trust AWS

✅ No GPU TEE requirement

Choose Azure if:

✅ Existing Azure investment

✅ Need CPU TEE diversity (TDX/SEV-SNP/SGX)

✅ Want better attestation than AWS

✅ Healthcare/financial compliance

✅ Standard VM workflows preferred

Choose GCP if:

✅ Existing GCP investment

✅ Cost-sensitive CPU workloads

✅ Simple confidential computing needs

✅ Minimal attestation acceptable

Summary

Key Takeaways

Security:

• Phala Cloud offers zero-trust model with full attestation transparency
• AWS/Azure/GCP require trusting the provider
• Attestation transparency: Phala > Azure > AWS > GCP

Technology:

• Phala Cloud: Only GPU TEE option (H100/H200)
• Azure: Broadest CPU TEE support
• AWS: Proprietary Nitro system
• GCP: Limited to AMD SEV

Cost:

• Phala Cloud: Best for GPU workloads (92% cheaper than AWS)
• GCP: Best for CPU-only workloads
• AWS: Most expensive
• Azure: Mid-range pricing

Ecosystem:

• AWS/Azure/GCP: Mature ecosystems
• Phala Cloud: Growing ecosystem, purpose-built for confidential AI

The Verdict

For Confidential AI:

🏆 Phala Cloud - Only GPU TEE, best security model, best cost

For Enterprise AWS-Native:

🥈 AWS Nitro - Best AWS integration despite limitations

For Regulated Industries:

🥉 Azure Confidential Computing - Strong compliance, good transparency

For Simple CPU Workloads:

GCP Confidential VMs - Low cost, adequate for non-critical use

FAQ

Q: Can I use multiple cloud providers?

A: Yes! Multi-cloud strategies common:

• Phala: GPU TEE workloads
• Azure/AWS: CPU workloads, specific services
• Use Kubernetes for orchestration across clouds

Q: How do I migrate between providers?

A: Generally straightforward:

1. Containerize application (if not already)
2. Update attestation verification code
3. Replace provider-specific services (KMS, etc.)
4. Test thoroughly before cutover

Q: Is Phala Cloud less reliable than AWS/Azure/GCP?

A: Different scale, similar reliability:

• Phala: 99.9% uptime SLA (2024)
• AWS/Azure/GCP: 99.95-99.99% SLAs
• Phala growing rapidly, backed by Flashbots

Q: Can I verify Phala Cloud attestations independently?

A: Yes! Fully transparent:

• Public Trust Center
• Open source verification tools
• No Phala account needed
• This is unique to Phala

Q: What about vendor lock-in?

A: Phala minimizes lock-in:

• Standard Docker containers
• Open source Dstack SDK
• Decentralized KMS (portable keys)
• Migrate to self-hosted or other providers

Q: Do I need a blockchain account for Phala Cloud?

A: No! Phala Cloud is standard managed confidential computing:

• Credit card payment
• No cryptocurrency required
• Blockchain optional (for some advanced features)

What’s Next?

Now that you understand cloud provider options, dive into specific use cases:

• **Confidential AI in Healthcare** - HIPAA-compliant AI deployments
• **Confidential Computing in Finance** - Financial services use cases
• **CISO Guide to Confidential Computing** - Executive decision framework

Ready to deploy confidential AI?

Try Phala Cloud - Start with free tier, scale to production GPU TEE.

Related Resources

• Phala Homepage
• Learning Hub
• Phala Cloud Documentation
• Dstack Documentation
• Success Stories

Next Steps

• Get Started with Phala Cloud
• Deploy Your First Confidential VM
• Explore Confidential AI
• Join Our Startup Program
• Contact Us