Phala Confidential AI Cloud: Complete Platform Guide

TL;DR: Phala Confidential AI Cloud is a TEE-based confidential AI cloud, purpose-built for secure AI workloads with production-ready GPU TEE. Deploy LLMs, machine learning models, and sensitive workloads with hardware-enforced privacy via NVIDIA H100/H200 TEE, Intel TDX, and AMD SEV-SNP. Dstack SDK simplifies deployment (“Docker for TEE”), Trust Center provides public attestation verification, and production-ready infrastructure delivers 95-99% of native performance. The only cloud platform offering public, cryptographically verifiable privacy via attestation, not promises.

Platform Overview

What is Phala Confidential Cloud?

Phala Confidential AI Cloud is a TEE-based private AI cloud for secure AI workloads (LLMs, training, analytics) with public attestation.

Phala Cloud differentiators:

Core Platform Components

*Architecture of Phala’s TEE-based confidential AI cloud: NVIDIA H100/H200 GPU TEE, Intel TDX/AMD SEV-SNP; Trust Center for public attestation.*

Layer Breakdown:

1. Hardware Infrastructure (Layer 1)
   • GPU TEE: NVIDIA H100 (80GB HBM3), H200 (141GB HBM3e)
   • CPU TEE: Intel TDX (4th/5th Gen Xeon), AMD SEV-SNP (EPYC Genoa)
   • Networking: TEE-aware encrypted transit
   • Storage: Encrypted with TEE integration
2. TEE Runtime (Layer 2)
   • GPU Runtime: NVIDIA Confidential Computing SDK
   • CPU Runtime: TDX/SEV-SNP virtual machines
   • Attestation: Remote attestation generation
   • Key Management: TEE-integrated KMS
   • Memory Encryption: AES-256 hardware encryption
3. Orchestration (Layer 3)
   • Kubernetes with TEE awareness
   • TEE-aware workload placement
   • Autoscaling for confidential workloads
   • Service mesh with attestation
4. Developer Platform (Layer 4)
   • Dstack SDK: Container → TEE deployment automation
   • REST API: Programmatic access
   • CLI: Command-line interface
   • Web Console: Visual deployment management
   • CI/CD Integration: GitHub Actions, GitLab CI/CD
5. Trust Infrastructure (Layer 5)
   • Trust Center: Public attestation verification
   • Attestation Registry: Historical logs
   • Compliance Reporting: HIPAA, SOC 2, PCI-DSS
   • Audit Logging: Comprehensive trails
6. Application Services (Layer 6)
   • LLM Inference: Optimized confidential LLM serving
   • Model Training: Confidential training pipelines
   • Data Analytics: Privacy-preserving analytics
   • API Gateway: Managed endpoints with attestation

Getting Started with Phala Cloud

Account Setup

Get started with Phala’s confidential AI cloud in under 10 minutes — from signup to first TEE deployment:

Step-by-step breakdown:

1. Sign Up → Create account at cloud.phala.network
2. Get API Key → Generate from dashboard settings
3. Authenticate → Initialize PhalaClient with API key
4. Create Deployment → Name your first confidential workload
5. Configure Settings:
   • Container image: python:3.11-slim (or any Docker image)
   • TEE type: Intel TDX or AMD SEV
   • Resources: 2 CPU cores, 4GB memory
   • Attestation: Publish to Trust Center
6. Deploy to TEE → Platform provisions confidential compute environment
7. Wait for Ready → Deployment status changes to “running” (~2-5 minutes)
8. Verify Attestation:
   • Check TEE type confirmation
   • Verify trust level (hardware-backed proof)
   • Access public verification URL
9. View Output → See workload logs and results
   • *Expected output:*
     • Status: running
   • Attestation:
     • TEE Type: tdx
     • Trust Level: high
     • Verification URL: https://trust.phala.cloud/verify/dep_abc123
   • Workload Output:
     • Hello from Phala Confidential Cloud!
     • Running in TEE: Hardware-enforced privacy
     • Platform: Linux-5.15.0-confidential-x86_64-with-glibc2.35

✓ First confidential workload deployed successfully

Dstack SDK Deep Dive

"Docker for TEE" - Container to TEE automation:

> Note: The following is conceptual code showing the deployment workflow. For actual deployment, use the Phala Cloud Dashboard or Phala Cloud CLI.

# Conceptual example showing the deployment workflow
# Actual deployment uses Phala Cloud Dashboard or CLI

# Step 1: Prepare your Docker application
# docker-compose.yml with your services

# Step 2: Deploy via Phala Cloud Dashboard
# 1. Go to https://cloud.phala.network/dashboard/cvm
# 2. Upload docker-compose.yml
# 3. Configure TEE type (Intel TDX, AMD SEV-SNP, GPU TEE)
# 4. Set environment variables and secrets
# 5. Click "Deploy"

# Step 3: Access your deployment
# Your app will be available at:
# https://<app-id>.dstack.phala.network

Dstack SDK key features:

Trust Center: Attestation Infrastructure

Trust Center: Public Attestation for Confidential AI Cloud

A public attestation platform so customers can verify TEE type, trust level, and measurements — no need to ‘trust the vendor’.

Capabilities:

• Verification: Cryptographic proof of TEE configuration
• Historical Attestation: Immutable logs for compliance
• Continuous Monitoring: Real-time TEE status
• Public API: Customer self-service verification

Example Customer Verification:

import requests

attestation_url = "https://trust.phala.cloud/verify/dep_abc123"
response = requests.get(attestation_url)
attestation = response.json()

if attestation['verified']:
    print("✓ TEE verified: Customer data protected by hardware")
else:
    print("✗ TEE verification failed")

Trust Center for Sales Enablement

Using public attestation to win enterprise customers:

Production Deployment Patterns

High-Availability Confidential LLM

Enterprise-grade confidential AI infrastructure:

> Note: For production LLM deployment, use Phala Cloud's Confidential AI API or deploy custom models via CVM.

# High-availability LLM deployment via Phala Cloud Dashboard
# Deploy using docker-compose.yml:

version: "3"
services:
  llm-service:
    image: your-org/llama-70b-server:latest
    ports:
      - "8000:8000"
    environment:
      - MODEL_PATH=/models/llama-3.1-70b
      - MAX_BATCH_SIZE=32
      - ENABLE_STREAMING=true
    volumes:
      - /var/run/dstack.sock:/var/run/dstack.sock  # For attestation
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 4  # 4x H100 GPUs
              capabilities: [gpu]

# Deploy configuration:
# TEE Type: NVIDIA H100 GPU TEE
# Regions: Multi-region deployment via dashboard
# Autoscaling: Configure in dashboard settings
# Attestation: Enable public attestation verification

Multi-Tenant Confidential SaaS

SaaS platform with per-customer TEE isolation:

> Note: Deploy isolated TEE instances per customer using Phala Cloud Dashboard or automate via API/CLI.

# Per-customer TEE deployment configuration
# Create separate CVM deployment for each customer

# Basic Tier Customer
app_name: customer-acme-basic
image: your-company/analytics-platform:latest
tee_type: intel-tdx
resources:
  cpu_cores: 4
  memory_gb: 16
  storage_gb: 100
attestation:
  public: true
  continuous_verification: true

---

# Professional Tier Customer
app_name: customer-acme-pro
image: your-company/analytics-platform:latest
tee_type: intel-tdx
resources:
  cpu_cores: 16
  memory_gb: 64
  storage_gb: 500
attestation:
  public: true
  continuous_verification: true

---

# Enterprise Tier Customer (with GPU TEE)
app_name: customer-acme-enterprise
image: your-company/analytics-platform:latest
tee_type: h100_tee
resources:
  cpu_cores: 32
  memory_gb: 128
  gpu_count: 1  # H100 GPU TEE
  storage_gb: 1000
attestation:
  public: true
  continuous_verification: true

Pricing and Cost Optimization

Pricing Model

Phala Confidential AI Cloud pricing — usage-based for GPU TEE and CPU TEE.

Cost Optimization Strategies

Reducing Phala Cloud costs:

• Reserved Capacity: Commit to 1-year for base load, saving 40%
• Autoscaling Tuning: Scale down aggressively during low traffic, saving 25-35%
• Model Optimization: Quantization, pruning, distillation, saving 50%
• Request Batching: Increase throughput by 30%
• Multi-Region Optimization: Deploy in cheaper regions, saving 15%

Compliance and Enterprise Features

Enterprise-Grade Compliance

Phala Cloud compliance certifications:

Enterprise features:

• Dedicated Infrastructure: Customer-specific hardware pools
• Private Cloud Deployment: Phala Cloud in your datacenter
• Custom Attestation Policies: Organization-specific TEE requirements
• Advanced Key Management: Bring-your-own-key (BYOK)
• Dedicated Support: 99.95% uptime guarantee
• Custom SLAs: Up to 99.99% uptime

Roadmap and Future Developments

2025-2027 Platform Roadmap

Conclusion

Why Phala Confidential Cloud?

Key differentiators:

Who should use Phala Cloud:

1. Healthcare AI companies - HIPAA-compliant AI without compromise
2. Financial services - Protect proprietary models and customer data
3. Enterprise SaaS - Privacy-preserving multi-tenant platforms
4. Government contractors - FedRAMP-ready confidential computing
5. AI startups - Privacy as competitive differentiator
6. Researchers - Collaborative AI without data sharing

Getting Started

Deploy confidential AI in minutes using Phala Cloud's pre-configured LLMs:

import openai

# Use Phala's Confidential AI API (OpenAI-compatible)
client = openai.OpenAI(
    api_key="your-phala-api-key",  # Get from https://cloud.phala.network
    base_url="https://api.phala.network/v1"
)

# Your queries run in GPU TEE - fully confidential
response = client.chat.completions.create(
    model="meta-llama/Meta-Llama-3.1-8B-Instruct",
    messages=[{"role": "user", "content": "Hello from confidential AI!"}]
)

print(f"Response: {response.choices[0].message.content}")
# Get attestation proof: https://proof.t16z.com/reports/<checksum>

For custom deployments: Use Phala Cloud Dashboard to deploy Docker applications with TEE protection.

Resources:

• Documentation: Phala Cloud Documentation
• Dstack SDK: Dstack SDK
• Trust Center: Trust Center
• Community: Phala Community
• Enterprise Contact: [email protected]

What’s Next in Your Learning Journey?

Recommended reading order:

1. New to confidential computing?

• Start: What is Confidential Computing
• Then: Getting Started

1. Ready to deploy?

• Implementation: Deploying Confidential AI
• Best practices: Production Best Practices

1. Industry-specific:

• Healthcare: Confidential AI in Healthcare
• Finance: Confidential Computing in Finance
• Government: Government Confidential Computing

1. Advanced topics:

• LLMs: Confidential LLMs
• Edge: Confidential Edge AI

Start building today:

Deploy on Phala Cloud - Free tier available, production-ready infrastructure.

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