How do I connect model serving to our existing auth and logging stack?

To connect model serving to your existing auth and logging stack, treat the model server like any other internal API:

1) Put an API gateway or reverse proxy in front

Use your existing gateway pattern if you have one:

• Ingress / API Gateway / Envoy / NGINX / Kong / Apigee / Istio
• Terminate TLS there
• Enforce auth before requests reach the model
• Centralize request/response logging, rate limiting, and tracing

2) Reuse your existing auth

Common approaches:

JWT/OIDC

If your stack already uses OIDC:

• Clients authenticate with your IdP
• Gateway validates JWTs
• Pass identity claims downstream as trusted headers or through mTLS-authenticated service identity
• Model service should still verify the request came from the gateway or trusted mesh

API keys

If you use API keys:

• Validate at gateway
• Map key to tenant/user/service identity
• Forward identity context to the model service

Service-to-service auth

For internal calls:

• Use mTLS or workload identity
• In Kubernetes, this is often via service mesh or SPIFFE/SPIRE
• Let only the gateway or approved services call the model endpoint

3) Propagate identity and context

Forward only the minimum required metadata:

• user_id
• tenant_id
• request_id
• roles/scopes
• model_name or model_version

Avoid forwarding raw tokens unless the backend needs them. Prefer signed, verified identity context from the gateway.

4) Add logging at the right layers

You usually want logs in 3 places:

Gateway logs

Capture:

• Timestamp
• Request ID / trace ID
• Auth result
• Caller identity
• Endpoint/model requested
• Latency
• HTTP status
• Rate-limit decisions

Model service logs

Capture:

• Request ID / trace ID
• Model version
• Inference latency
• Input size / token count
• Output size
• Errors/timeouts
• Safety/filter decisions

Audit logs

For security/compliance:

• Auth events
• Access to specific models or tenants
• Administrative changes
• Denials
• Data access events

5) Add distributed tracing

If you already use OpenTelemetry, instrument:

• Gateway
• Model server
• Feature store / retrieval / vector DB if used
• Any downstream services

Propagate:

• traceparent
• x-request-id or equivalent

This makes auth failures, latency spikes, and model-specific issues much easier to debug.

6) Be careful with sensitive data

Model inputs often contain PII or secrets. Before logging:

• Redact or tokenize sensitive fields
• Log metadata instead of full prompts where possible
• Separate debug logs from audit logs
• Restrict access to logs

A good pattern is:

• Keep full prompts out of standard logs
• Store only hashes, IDs, token counts, and classification tags
• If you need prompt storage, do it explicitly with retention controls

7) Enforce authorization at the model level too

Even if the gateway handles auth, the model service should enforce:

• Tenant isolation
• Model access policy
• Dataset or tool access policy
• Per-route permissions

This prevents bypass if something reaches the service directly.

8) Rate limiting and quotas

Since model serving can be expensive:

• Per-user/per-tenant quotas
• Burst limits
• Concurrent request limits
• Token-based billing/usage tracking

Often easiest at the gateway, with usage events sent to your logging/metrics stack.

9) A practical reference architecture

Client → API Gateway/Ingress → Auth check → Logging/Tracing → Model Service → (optional) Retrieval/Tools → Logs/Metrics/Audit

10) Implementation checklist

• Put model endpoints behind gateway
• Validate JWT/API keys at edge
• Use mTLS or mesh identity internally
• Propagate request/trace IDs
• Log auth outcome, latency, status, usage
• Redact sensitive payloads
• Add tenant/model authorization in service
• Export metrics and traces to existing observability stack
• Set retention/access controls for logs

If you want, I can give you:

1. a Kubernetes/Istio example,
2. an NGINX/Envoy example, or
3. a Python/FastAPI model server middleware example that plugs into JWT auth and structured logging.