This is a reference architecture I use for reasoning about LLM operations. It is not presented as an employer deployment. The idea is straightforward: applications call one internal API using stable model aliases, while the gateway owns provider credentials and common controls.

Keep the contract smaller than the providers.

The gateway should not expose every provider option. A narrow request contract makes behavior easier to secure, test, and migrate:

{
  "task": "support-summary",
  "input": {
    "ticket": "Customer cannot complete checkout."
  },
  "request_id": "req_01J...",
  "max_output_tokens": 300
}

The task name maps to a server-side policy rather than a provider model supplied by the client:

tasks:
  support-summary:
    model_alias: balanced-text
    timeout_seconds: 12
    max_input_tokens: 6000
    max_output_tokens: 400
    temperature: 0.1
    tools: []
    data_classification: internal
    fallback: compact-text

This lets a platform team change a model version, provider, timeout, or budget without updating every application. The caller still owns the business workflow; the gateway owns cross-cutting policy.

Apply controls before sending a request.

The request path authenticates the workload, authorizes the task, checks tenant and user rate limits, estimates token usage, and enforces a budget. It rejects unknown parameters, oversized payloads, unsupported content types, and model names supplied outside policy.

Depending on data classification, it can detect secrets or obvious personal data and either redact them or reject the request. Redaction must be observable and testable; silently changing important input can produce a misleading answer.

Prompt templates live on the server and clearly delimit untrusted content. If tools are enabled, each task receives an explicit allowlist, JSON schema validation, scoped credentials, and a separate authorization check. High-impact actions need confirmation or human approval. A prompt saying “ignore dangerous instructions” is helpful context, not a complete guardrail.

Use reliability patterns that fit generated output.

The gateway sets an end-to-end timeout and leaves enough budget for the caller to handle failure. It retries only transient errors such as selected rate limits or provider failures, with exponential backoff and jitter. Non-idempotent tool actions are not blindly retried.

Streaming changes retry behavior: after response bytes reach the client, restarting generation can duplicate or contradict output. At that point I return a clear interrupted status instead of silently starting a new answer.

A fallback model is useful only when it satisfies the same data, tool, context-window, and output-contract requirements. Switching to a cheaper model may be acceptable for summarization and unacceptable for structured extraction. Fallbacks are tested per task, not enabled globally.

Validate outputs at the boundary.

For structured tasks, the gateway requires a JSON schema and rejects or repairs invalid output within a strict attempt limit. For RAG tasks, it verifies that citation identifiers came from supplied context. Content classifiers and blocklists can reduce known risks, but they do not guarantee factuality or safety.

The application must still decide how much trust to place in the result. A generated draft can be shown to a human; a production change or financial decision needs stronger domain controls.

Observe behavior without collecting every prompt.

I record request ID, hashed tenant identity, task, model alias, provider, policy outcome, input and output token counts, time to first token, total latency, retry count, cache status, estimated cost, and error class. OpenTelemetry spans connect the gateway call to the application trace.

Raw prompts and outputs are not logged by default. If temporary content capture is needed for debugging, it should have explicit authorization, redaction, a short retention period, and an audit trail.

Roll policy changes out like code.

Model aliases, prompts, and guardrails are versioned. A small evaluation set tests expected output, refusal behavior, schema validity, latency, and cost before rollout. New versions start with shadow traffic or a canary, and every task has a kill switch.

A gateway does not make LLMs deterministic or safe by itself. It creates one enforceable place for credentials, routing, budgets, retries, telemetry, and shared guardrails. That smaller operational surface is the real benefit: applications can experiment without each one rebuilding the same controls differently.