Phala Cloud Documentation — Private AI on TEE

Overview

The on-demand Private AI API provides an OpenAI-compatible interface for confidential inference. Requests go through Phala’s ACI gateway at https://inference.phala.com/v1. The gateway runs in a TEE, publishes an attestation report, and signs a per-response receipt that you can verify.

Prerequisites

Private AI is paid from your workspace Balance, the same Balance that pays for CVMs and GPU instances. There is no separate top-up and no minimum: if your Balance has money, the API works. Gifted credits do not pay for Private AI. In the dashboard, go to Private AI → API Keys and create your first API key. Click the key to copy it.

Once you get the API Key, you can start making requests to the Private AI API.

Make Your First Request

Replace <API_KEY> with your actual API key. The examples below use z-ai/glm-5.2; use List Models to choose a model for your workload.

# Install OpenAI SDK: `pip3 install openai`

from openai import OpenAI

client = OpenAI(
    api_key="<API_KEY>",
    base_url="https://inference.phala.com/v1",
)

response = client.chat.completions.create(
    model="z-ai/glm-5.2",
    messages=[
        {"role": "system", "content": "You are a helpful assistant"},
        {"role": "user", "content": "What is your model name?"},
    ],
)

print(response.choices[0].message.content)

import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'https://inference.phala.com/v1',
  apiKey: '<API_KEY>',
});

async function main() {
  const completion = await client.chat.completions.create({
    model: 'z-ai/glm-5.2',
    messages: [
      {
        role: 'user',
        content: 'What is the meaning of life?',
      },
    ],
  });
  console.log(completion.choices[0].message);
}

main();

curl -X 'POST' \
  'https://inference.phala.com/v1/chat/completions' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer <API_KEY>' \
  -d '{
  "messages": [
    {
      "content": "You are a helpful assistant.",
      "role": "system"
    },
    {
      "content": "What is your model name?",
      "role": "user"
    }
  ],
  "model": "z-ai/glm-5.2"
}'

The response is a standard OpenAI chat completion. In raw HTTP responses, Phala also returns verification headers:

Header	Meaning
`x-receipt-id`	Receipt id for this response. Use it with `GET /v1/aci/receipts/{id}`.
`x-aci-identity`	Attested gateway workload identity.
`x-aci-keyset-digest`	Digest of the gateway keyset used for receipt verification.

Confirm the Response Was Attested

Fetch the receipt with the x-receipt-id header:

curl -s "https://inference.phala.com/v1/aci/receipts/$RECEIPT_ID" \
  -H "Authorization: Bearer <API_KEY>" | \
  jq '.event_log[] | select(.type=="upstream.verified") | {provider, result, required, session_id}'

For a confidential response, result is verified and required is true. To verify the gateway identity and receipt signature end to end, follow Verify a Response.

Choose a Model

The live catalog is authoritative; query it before hardcoding model IDs:

curl https://inference.phala.com/v1/models

The catalog is public and authoritative for availability, pricing, context limits, modalities, and capabilities. Embedding models are listed separately at GET /v1/embeddings/models.

TEE provider presence and confidential serving are not identical for every provider and model. Use is_tee from /v1/models to find models that can be served confidentially, then verify the actual response with its x-receipt-id.

Confidential inference and zero data retention are separate properties. is_tee constrains who can read memory during execution. zdr constrains whether the upstream stores prompt or completion content after serving. Use GET /v1/models?zdr=true and provider: {"zdr": true} when retention is part of your policy.

Verify Your AI is Running Securely

Before trusting receipts, fetch a fresh Attestation Report. Then fetch the Receipt for a response and verify that its workload_id and workload_keyset_digest match the report. The legacy Signature endpoint remains available for older clients, but new integrations should use GET /v1/aci/receipts/{id}.

Next Steps

Use the API reference and feature guides for the next step:

Chat Completions documents the core request and response shape.
List Models shows how to discover models programmatically.
Get Receipt documents the canonical per-response proof.
Embeddings covers embedding model calls.
Tool Calling helps you call tools from your AI models.
Images and Vision helps you use image-capable models.
Structured Output helps you get JSON responses.
Streaming helps you consume streaming responses.
Zero Data Retention helps you route requests only to serving routes that do not retain prompt or completion content.
Playground helps you test models in a private environment.

​Overview

​Prerequisites

​Make Your First Request

​Confirm the Response Was Attested

​Choose a Model

​Verify Your AI is Running Securely

​Next Steps

Overview

Prerequisites

Make Your First Request

Confirm the Response Was Attested

Choose a Model

Verify Your AI is Running Securely

Next Steps