How do I put a model in front of an internal app with auth and monitoring?

A good pattern is to wrap the model behind a small internal API gateway/service rather than exposing the model directly to the app.

Typical setup

Internal app → Auth layer / API gateway → Model service → Logging/metrics/monitoring

1) Put the model behind an internal service

Run the model as a dedicated service with:

• a stable HTTP/gRPC endpoint
• request/response schemas
• versioning
• rate limits / timeouts
• input/output validation

This keeps the model isolated from the app and makes it easier to secure and monitor.

2) Add authentication and authorization

Before requests reach the model service, enforce:

• SSO / IdP integration: Okta, Azure AD, Google Workspace, etc.
• JWT/OAuth2 for service-to-service or user-authenticated calls
• mTLS for internal service identity if needed
• RBAC/ABAC to control who can use which model or features

A common approach:

• The app authenticates the user
• The app calls the model service with a short-lived token
• The gateway verifies the token and policy

3) Put an API gateway or reverse proxy in front

Use something like:

• Kong
• Envoy
• NGINX
• Traefik
• Cloud-native API gateway

This layer can handle:

• auth enforcement
• request quotas / rate limiting
• IP allowlists
• TLS termination
• routing to model versions
• request size limits

4) Add monitoring and observability

Track both infrastructure and model behavior.

Infrastructure metrics

• latency p50/p95/p99
• error rate
• throughput / QPS
• CPU/GPU/memory
• queue depth
• timeouts / retries

Model metrics

• token counts / cost
• prompt length / response length
• refusal rates
• schema validation failures
• drift / quality signals
• user feedback / thumbs up-down

Logging

• log request IDs and trace IDs
• redact secrets / PII
• sample full prompts carefully
• store only what you’re allowed to retain

Use:

• OpenTelemetry for traces
• Prometheus/Grafana for metrics
• ELK/Datadog/Splunk for logs

5) Secure the data path

• TLS everywhere
• secrets in a vault or managed secret store
• network segmentation / private subnets
• least privilege for the model service
• PII redaction before logging
• prompt-injection and output-filtering checks if the model can access tools or internal data

6) If the model is used for RAG or tools

Put extra controls around:

• document access permissions
• retrieval filters based on user identity
• tool execution authorization
• output sanitization

Simple reference architecture

Browser/Internal App
   ↓
Auth Provider (SSO/OAuth)
   ↓
API Gateway / Reverse Proxy
   ↓
Model Orchestrator Service
   ↓
Model Server (LLM)
   ↓
Vector DB / Tools / Internal APIs

Practical implementation options

Option A: Fastest internal deployment

• App auth via SSO
• NGINX/Kong in front
• Model service in Docker/Kubernetes
• Prometheus + Grafana + centralized logs

Option B: More enterprise-grade

• IdP + OAuth2/OIDC
• API gateway with policy engine
• Kubernetes deployment with mTLS/service mesh
• OpenTelemetry traces
• Audit logs and DLP/redaction

Recommended minimum controls

If you want the shortest safe baseline:

1. Put the model behind an internal API
2. Require auth with OIDC/JWT
3. Enforce TLS and network restrictions
4. Add request logging with redaction
5. Export latency/error/usage metrics
6. Set rate limits and timeouts

If you want, I can sketch a concrete setup for your stack, e.g.:

• Kubernetes + NGINX + OIDC
• AWS API Gateway + Lambda/ECS/SageMaker
• Azure + Entra ID
• FastAPI + Prometheus + Grafana