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
Private Inferenz
Inference, ohne den Prompt offenzulegen.
OpenAI-kompatibel. Signierte Belege. Von Grund auf keine Logs.
deine Gedanken bleiben deine
Private LLM catalog
Frontier models with private runtime.
OpenAI-compatible models with hardware-backed privacy and ACI verification. Keep your SDK flow, change the endpoint, and copy the real call when you need it.
DeepSeek: DeepSeek V4.1 Flash 1017
Beta
1.0M Kontext
$0.75/M input
$1.60/M output
$0.15/M cache read
Phala: Qwen3.8 27B Uncensored (Aggressive)
262K Kontext
$0.30/M input
$1.50/M output
$0.05/M cache read
Z.ai: GLM 5.3 Flash
Beta
1.0M Kontext
$0.15/M input
$0.50/M output
$0.03/M cache read
Qwen: Qwen3.8 27B
Beta
1M Kontext
$0.20/M input
$2.50/M output
$0.05/M cache read
DeepSeek: DeepSeek V4 Flash 0731
1.0M Kontext
$0.44/M input
$1.32/M output
$0.028/M cache read
MoonshotAI: Kimi K3
1.0M Kontext
$3.00/M input
$15.00/M output
$0.30/M cache read
Qwen: Qwen3.6 27B
262K Kontext
$0.32/M input
$3.25/M output
$0.03/M cache read
Google: Gemma 4 31B
262K Kontext
$0.15/M input
$0.46/M output
$0.075/M cache read
Model requests are routed through confidential AI providers with TEE support.
Check allPrivate 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.
Encrypted into the TEE
- Encrypted on both hops, each channel key bound to attestation
- RA-TLS terminates inside the CVM, not at a load balancer
- No plaintext intermediary on the host
So funktioniert es
Gehen Sie eine einzelne Anfrage Ende zu Ende durch.
Schalten Sie dstack aus, um zu sehen, welche Garantie genau entfällt.
Private Inference auf dstack
ACI gateway attestation, signed receipts, and verified upstream model sessions
aktiver Edgeinaktivneu in Schritt
Schritt 1 of 5 · scroll-aware1
Schritt 1 / 5Verifiziere den Build, bevor du ein Byte sendest
Client SDK fetches the gateway’s TDX quote with a fresh nonce and runs dcap-qvl locally — when the caller pins accepted compose hashes, it confirms the measured release is one of them. The trust decision is client-side; Phala is not asked to vouch for itself.
With dstack: Der Nutzer hält den Trust Root, verankert in der TDX-Hardware-Signatur von Intel.
Gleiches SDK. Gleiche Endpoints. Standardmäßig vertraulich.
cURL · sofort einsetzbar
Hit inference.phala.com/v1/chat/completions with the OpenAI request shape. Receipt headers come back on every response — even from curl.
cURL
$ curl https://inference.phala.com \/v1/chat/completions \-H "Authorization: …" \-d '{"model":…}'x-receipt-id: rcpt-e0ee..x-aci-keyset-digest: sha256:3eff..
PYTHON
from openai import OpenAIc = OpenAI(base_url="…phala.com/v1",api_key=os.environ["PHALA_AI_API_KEY"])r = c.chat.completions.create(…)
OpenAI Python SDK
`base_url="https://inference.phala.com/v1"` and you’re done. Existing code keeps working; capture x-receipt-id from the raw response when you need proof.
Ein einheitlicher Verifier
Every route returns the same ACI artifacts: a gateway attestation report, a signed receipt, and an attested-session reference for the serving upstream.
UNIFIED PROOF
unified verifierall match
TinfoilDeepSeek V4.1 Flash 1017PhalaKev 4B
0G Private ComputerGLM 5.3
Near AIGLM 5.3 Flash
ChutesQwen3.8 27Bone format · any provider
OPENROUTER
openrouter · phala2026-10-26
41.2Btokens / day
models with traffic10
sourceopenrouter.ai/provider/phala
Live on OpenRouter
Phala serves confidential models as a provider on OpenRouter. The daily token count comes from OpenRouter’s public Phala provider page.
two-hop RA-TLS · TLS key bound to TDX quote
tunneled · no plaintext intermediary
hop 01 · client → gateway
inference.phala.comSPKI in gateway report
hop 02 · gateway → model CVM
Phala model CVMSPKI in model quote
RA-TLSSPKI pinX.509tunneled
RA-TLS über zwei Hops, bis zum Modell
The first TLS hop terminates inside the gateway CVM, whose TLS key is bound to its TDX quote. For Phala-hosted models, the second terminates inside the model CVM, whose TLS key is bound to its own quote. Other providers bind the second hop to keys from their own evidence. There is no plaintext intermediary on the host.
response · /v1/chat/completions
200x-receipt-idrcpt-e0eefe…x-aci-identitysha256:3def…x-aci-keyset-digestsha256:3eff…sessionupstream.verified
verify receiptmatches attestation
Signed receipt + attested session, every response
Every response carries x-receipt-id. Fetch the ACI receipt, verify its signature and response hash against a fresh gateway attestation, then follow upstream.verified.session_id when you need deeper audit evidence.
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.
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
KI-Lösungswege
Verwenden Sie private Modelle, wenn KI mit Geheimnissen interagiert.
Der private Modell-Endpunkt ist der erste Einstiegspunkt. Dieselbe Datenschutz-Primitive lässt sich auf Agents, Daten-Workflows und Training ausweiten.
Agents
Private KI-Agenten
Agenten mit Schlüsseln, Tools, Speicher und Aktionen in einer verifizierten Laufzeit ausführen statt in einer sichtbaren Automatisierungs-Cloud.
Training
Private Modelltrainings
Passe Modelle an proprietäre Daten an, während Datensätze, Gradients, Checkpoints und Evaluations-Traces innerhalb der Grenze bleiben.
private training run
Observe without exposing weights.
01
dataset
sealed
02
fine-tune
running
03
eval
private
04
checkpoint
verified
loss curve
proof attached
attestation.json
Data
Private KI-Daten
Modelle zu sensiblen Datensätzen verschieben und freigegebene Ausgaben zurückgeben, ohne Rohdaten dem Modellbetreiber offenzulegen.
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
OpenAI-compatible. Attested. Receipt-backed.
Drop in with the OpenAI SDK you already use. Point at inference.phala.com and capture x-receipt-id for the signed ACI receipt.
- 01OpenAI-compatible base URL
- 02TDX + H100 / H200 / Blackwell
- 03Signed ACI receipt per response
- 04Gateway attestation + upstream sessions
- 055–15% TEE overhead vs bare-metal
Private Ausführung. Verifizierbare Ergebnisse.
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