TrustedRouter — Private, reliable LLM routing for production AI apps

Privacy-first AI gateway<br>Private, reliable LLM routing for production AI apps.

Route the open-weight leaders — Qwen, GLM, DeepSeek, Gemma, Kimi, MiniMax — plus every frontier model, through one OpenAI-compatible API. TrustedRouter keeps prompt traffic on an attested gateway, avoids prompt/output logs, and gives teams a verifiable trust path instead of another black-box router.

Get API key<br>🛡 Verify trust

Migrate from OpenRouter →

✓ OpenAI-compatible API<br>✓ No prompt/output logs<br>✓ Attested gateway<br>✓ Provider failover<br>✓ BYOK + prepaid credits<br>✓ Open source

Control plane (metadata only)

◈ User app / AI product

Making inference calls

ATTESTED GATEWAY

🛡 TrustedRouter

🔒 No prompt logs

▤ Metadata-only control plane

◈ Failover logic

Qwen

GLM

DeepSeek

Gemma

Kimi

DeepInfra

Production AI now needs an inference control layer.

Most AI products start by calling one model provider directly. That works until you need fallback, model choice, cost control, regional routing, privacy controls, provider-specific policies, customer trust, or proof that sensitive prompts are not being logged by another intermediary.

Providers fail or rate-limit

Model quality changes

Costs move by workload

Sensitive prompts create trust risk

Closed routers create another vendor to trust

Customers increasingly ask where their data goes

TrustedRouter gives you one API, multiple providers, private routing, and a trust record your customers can inspect.

One gateway. Three jobs.

🔎<br>Route

Send requests to many models through one OpenAI-compatible API. Use explicit models or trustedrouter/auto for provider fallback.

🛡<br>Protect

Keep prompt traffic on an attested gateway. The control plane stores metadata for billing and operations, not prompt or output bodies.

📄<br>Prove

Publish the source commit, image reference, image digest, and attestation path so technical customers can verify what is running.

Move over with one base_url change.

✓ Use trustedrouter/auto for fallback

✓ Use provider.data_collection = "deny" for zero-retention provider preference

✓ Use BYOK where needed

✓ Keep existing OpenAI-shaped calls working

View migration guide →

Drop-in, OpenAI-compatibleTypeScript

const client = new OpenAI({<br>apiKey: process.env.TRUSTEDROUTER_API_KEY,<br>baseURL: "https://api.trustedrouter.com/v1"<br>})

const response = await client.chat.completions.create({<br>model: "trustedrouter/auto",<br>messages: [{ role: "user", content: "Hello" }],<br>provider: { data_collection: "deny" }<br>})

Your prompt path should be inspectable.

TrustedRouter separates production inference from the normal dashboard/control-plane surface. Prompt and output bodies should not pass through the control plane. Metadata is used for billing, routing, latency, status, and cost tracking.

Architecture

Production prompt path

App → Attested Gateway → Provider

Control plane path

Dashboard → Metadata / Billing / API Keys

🛡 No prompt/output logs

🛡 Metadata-only billing records

🛡 Attested gateway

🛡 Fail-closed if attestation fails

🛡 Open-source backend & config

🛡 Public trust page with commit & digest

Open trust page

Fallback when providers fail.

Production AI should not break because one provider returns 429s, 5xx errors, degraded latency, or temporary capacity issues. TrustedRouter routes across healthy providers and exposes route health so teams can build more resilient AI products.

View status

Router availability99.99%

Live regions3

Provider health Healthy

Median routing overhead8ms

Privacy is not a setting buried in a dashboard.

For sensitive AI products, the question is not only which model you use. It is who can see the prompts, what gets logged, which providers retain data, and whether the router itself can be inspected.

FeatureDirect Provider APIClosed RouterTrustedRouter

One API across providersNoYesYes

Provider fallbackNoYesYes

BYOK supportSometimesSometimesYes

Zero-retention preferenceVariesUnknownYes

Prompt/output logsYes (API dependent)Yes (usually)Never

Open-source routerN/ANoYes

Public attestationN/ANoYes

Customer-facing trust recordN/ANoYes

Compare with OpenRouter

Built for teams where prompts matter.

Legal AI

Pain: Cannot risk exposing client privilege to intermediaries.

Solution: Route sensitive client work with a verifiable prompt path to zero-retention providers.

Read security details →

Finance & PE

Pain: Evaluating private data with models creates non-disclosure risks.

Solution: Evaluate companies, documents, and private data without another opaque intermediary.

Read security details →

Production AI apps

Pain: Provider downtime breaks core product reliability.

Solution: Keep production inference reliable across providers using fallback routing.

Read security details →

AI agencies

Pain: Need an easy API, but clients demand data privacy.

Solution: Route customer workloads with clearer trust boundaries and verifiable answers.

Read...