Private Inference

Inference without exposing the prompt.

OpenAI-compatible. Signed receipts. By construction, no logs.

your thoughts stay yours

Private LLM catalog

Frontier models with private runtime.

OpenAI-compatible models with hardware-backed privacy and verification. Keep your SDK flow, change the endpoint, and copy the real call when you need it.

encrypted

Qwen: Qwen3.5-122B-A10B

262K context

$0.46/M input

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Qwen: Qwen3 32B

41K context

$0.12/M input

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Google: Gemma 4 31B

262K context

$0.15/M input

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Qwen: Qwen3.6 35B A3B

262K context

$0.20/M input

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DeepSeek: DeepSeek V4 Pro

800K context

$1.50/M input

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Phala: Gemma-4 26B-A4B Uncensored (Heretic)

66K context

$0.15/M input

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Phala: Qwen3.6 35B-A3B Uncensored (Aggressive)

131K context

$0.30/M input

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MoonshotAI: Kimi K2.6

262K context

$1.09/M input

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Z.ai: GLM 5.1

203K context

$1.21/M input

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Qwen: Qwen3.5-27B

262K context

$0.30/M input

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Qwen: Qwen3.5 397B A17B

262K context

$0.55/M input

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MiniMax: MiniMax M2.5

197K context

$0.20/M input

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Model requests are routed through confidential AI providers with TEE support.
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Private inference, by construction.

What you say to the model stays between your client and an attested CVM. Three primitives — encryption, TEE, no-logs — make that a property of the build, not a promise.

End-to-End Encryption

  • AES-GCM ciphertext on the wire, both hops
  • RA-TLS terminates inside the CVM, not at a load balancer
  • No plaintext intermediary on the host
How it works

Step through a single request, end to end.

Toggle dstack off to see exactly which guarantee disappears.

Private Inference on dstack

Two-hop RA-TLS into a fleet of attested model CVMs — verifiable, no-log by construction

1
Step 1 / 5

Verify the Build Before Sending One Byte

Client SDK fetches each candidate CVM’s TDX quote and runs dcap-qvl locally — confirms the build matches a no-log entry in DstackApp.sol. The trust decision is client-side; Phala is not asked to vouch for itself.

With dstack: User holds the trust root, anchored in Intel’s TDX hardware signature.

Same SDK. Same endpoints. Confidential by default.

cURL · drop-in

Hit api.redpill.ai/v1/chat/completions with the OpenAI request shape. Receipt headers come back on every response — even from curl.

cURL
$ curl https://api.redpill.ai \/v1/chat/completions \-H "Authorization: …" \-d '{"model":…}'x-phala-receipt-sig: 0x9c..x-phala-compose-hash: 0xa1..
PYTHON
from openai import OpenAIc = OpenAI(base_url="…redpill.ai/v1",api_key=RP_KEY)r = c.chat.completions.create(…)

OpenAI Python SDK

`base_url="https://api.redpill.ai/v1"` and you’re done. Existing code keeps working; receipts attach to the response object.

One unified verifier

Whether the model runs on Intel TDX + H100 or AMD SEV + B300, the receipt format is identical. One verification path covers your whole TEE-LLM fleet — even when you mix providers.

UNIFIED PROOF
unified verifierall match
phalaLlama 3.1
near aiDeepSeek V3
tinfoilQwen2.5
chutesMistral
one format · any provider
OPENROUTER
openrouter · phala2026-05-30
3.5Btokens / day
Llama · open$0.40 / M
Llama · phala$0.40 / M
DeepSeek · open$0.27 / M
DeepSeek · phala$0.27 / M

No premium for privacy

Confidential routes through Phala on OpenRouter price the same as the open route. Privacy is no longer a procurement line item — just a header you opted into.

two-hop RA-TLS · X.509 with TDX-quote extension

tunneled · no plaintext intermediary

hop 01 · client → gateway

CN=phala-gatewayTDX-quote ext (1.3.6.1.4.1…)

hop 02 · gateway → model CVM

CN=vllm-llama-3.1-70bTDX+H100 quote ext
RA-TLSmTLSX.509tunneled

Two-hop RA-TLS, all the way to the model

The first TLS hop terminates inside the dstack-gateway CVM (whose certificate carries its TDX quote). The second terminates inside the model CVM. There is no plaintext intermediary — just two confidential VMs whose X.509 certificates ARE their attestations.

response · /v1/chat/completions

200
x-phala-receipt-sig0x9c1a…f7e2x-phala-compose-hash0xa1b2…d1f3x-phala-app-idvllm-llama-3.1-70bx-phala-no-logtrue · by build
verify offlinechains to DstackApp.sol

Signed receipt + on-chain compose-hash, every response

Every response carries x-phala-receipt-sig + x-phala-compose-hash. The signature chains to the TDX root and the on-chain DstackApp.sol entry — verify offline that the build that ran is the build that was registered.

in production today · 3 live partners

Confidential inference, in production.

OpenRouter routes its enterprise tier through Phala. NEAR AI ships verifiable agent inference. OODA AI runs decentralized GPU TEE.

01enterprise · live

OpenRouter

enterprise tier · drop-in

Drop-in OpenAI-compatible endpoint with verifiable, no-log routing. The receipt is the audit trail.

18B+ tokens

no-log · verified routing

02web3 · live

NEAR AI

verifiable agent inference

Verifiable agent inference for autonomous, on-chain workflows. Every model call lands on-chain with proof.

100% receipts

on-chain verified · zk inference

03public-co · live

OODA AI

NASDAQ-listed · decentralized GPUs

Decentralized GPUs with hardware attestation guarantees. No host root, no off-band access, no policy promises.

12M tokens / day

TDX + H100 · hardware-attested

OpenAI-compatible

drop-in /v1 surface

TDX + H100/H200/B300

CPU + GPU TEE

5–15% overhead

vs bare-metal

No host root

compose-hash IS the policy

AI solution paths

Use private models where AI touches secrets.

The private model endpoint is the first entry point. The same privacy primitive extends to agents, data workflows, and training.

Agents

Private AI agents

Run agents with keys, tools, memory, and actions inside a verified runtime instead of a visible automation cloud.

Open solution
Training

Private model training

Adapt models on proprietary data while keeping datasets, gradients, checkpoints, and evaluation traces inside the boundary.

Open solution

private training run

Observe without exposing weights.

H100 CC

01

dataset

sealed

02

fine-tune

running

03

eval

private

04

checkpoint

verified

loss curve

proof attached

attestation.json

Data

Private AI data

Move models to sensitive records and return approved outputs without exposing raw data to the model operator.

Open solution

source

EHR data

source

Customer records

source

Internal docs

TEE clean room

query without raw access

approved output

aggregate only
no row exportproof linked

Deploy private inference

Two-hop RA-TLS. Signed receipts. On-chain no-log.

Drop-in with the OpenAI SDK you already use. Point at api.redpill.ai. Get a signed receipt with every response.

View docsTalk to sales
  • 01OpenAI-compatible base URL
  • 02TDX + H100 / H200 / Blackwell
  • 03Signed receipt per response
  • 04On-chain compose-hash registry
  • 055–15% TEE overhead vs bare-metal