Check data privacy before renting an LLM inference endpoint

Trace the request to the provider's Mac

The published PROXIES.SX flow sends a customer's request through the compute API, delivers inference jobs to a provider agent and runs the model on that provider's Apple Silicon Mac. The agent posts generated results back to the platform. This describes the application flow visible in public materials; it is not a complete audit of backend storage or infrastructure. Provider documentation, agent source

Your application
  -> PROXIES.SX compute API and job delivery
    -> provider Mac and local MLX model server
      -> result returned through the platform
        -> your application

A document excerpt included in a prompt becomes part of that request. Keeping the full document database on your own server does not keep the selected excerpts there. The same applies to conversation history, tool results and identifiers added by your application.

Separate dedicated capacity from data protection

Single-customer rental describes allocation. The supplier owns and administers the Mac, including its operating system and memory. This service does not provide confidential computing or independently attested execution. The current customer documentation explicitly describes temporary relay retention.

HTTPS protects the network connection to the endpoint. Binding the local model server to loopback limits its network exposure. Neither establishes who can inspect data inside the machines processing it.

These questions concern the coverage and enforcement of the published policy. A stated relay cleanup window does not answer every question about application logs, backups or access inside the supplier machine.

Read the published relay retention windows

The agent does not intentionally persist prompts, but runtime and diagnostic errors can contain sensitive text. The supplier still administers the Mac, so this behavior does not establish everything the host retains. The customer documentation describes relay storage used to deliver prompts and responses, with completed-job content normally scrubbed after one hour and job records expiring after 24 hours, subject to database cleanup timing. Provider privacy description, customer retention description

The one-hour window describes normal cleanup of completed jobs, not a guarantee that every copy disappears exactly sixty minutes after a request. Database cleanup, failed jobs, application logs and supplier-host copies need separate consideration. This is not a zero-retention service.

Trace your own application copies too. A saved conversation, an error report or a tracing service can retain text after the inference relay has removed it. Decide what each system needs and set its retention accordingly. A confidential workflow needs evidence covering the whole request path.

Keep credentials and unnecessary data out of prompts

Call the rental API from your application backend and keep the account key there. A browser-delivered bundle can expose embedded credentials to its users. The API guide explains the documented header and rental-specific endpoint.

Prepare a sample request and inspect everything it contains before allowing real application traffic. Remove account secrets, unrelated conversation history and document sections that the task does not need. If a task only needs an order status, consider supplying the status and an internal reference instead of the customer's full record.

Record operational metrics such as duration and status separately from prompt text. Debug logging is an application choice: check your SDK, proxy, error tracker and tracing configuration as well as the inference service.

Check retrieval and fallback paths

For a RAG application, enforce document access before selecting excerpts for the model. A user must not receive another user's document through retrieval, even if the final answer is well written. Keeping access checks in application code makes that boundary independent of the model's response.

A fallback can introduce another processor. If an offline node causes your code to send the same prompt to a second model service, review that destination under the same data-handling requirements. Do not silently broaden where confidential requests are sent.

External documents can also contain instructions aimed at the model. OWASP describes this as indirect prompt injection and notes that RAG does not remove the vulnerability. Treat retrieved content as input data, and enforce tool permissions in application code. This reduces potential impact; a prompt instruction alone does not guarantee containment. OWASP prompt injection guidance

Choose inputs that match the evidence

Public product descriptions, synthetic test cases and approved non-sensitive documents can help evaluate a model while data-handling questions are resolved. If your workload depends on a specific privacy guarantee, obtain supporting terms and technical evidence before sending it.

The rental guide covers availability and workload fit. For the service's current setup references, use the agent documentation hub and compute provider documentation.