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azure-ki-infrastruktur/beispiel-setup/2025-12-08-Azure_KI_Beispiel_Setup_ChatGPT_5.1_Thinking.md
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Alles klar, ich mache das jetzt konkret – so, als würdet ihr morgen mit diesem Setup auf Azure starten. Ich zeige dir:

  1. Annahmen & Namenskonvention
  2. Netzwerk- & Security-Setup
  3. Compute- & KI-Setup (AKS, OpenAI, Agents)
  4. Data-Layer-Setup
  5. Observability / Security-Setup
  6. DevOps & Terraform-Setup (inkl. Beispiel-Snippets)

Ich halte es „konkret, aber noch lesbar“, nicht jede Kleinigkeit auf Ressourcenniveau – eher ein Blueprint, den man in Terraform übersetzen kann.

1. Annahmen & Namenskonvention

Umgebung:

  • Subscription: sub-ai-prod-001
  • Region: westeurope
  • Tenant: euer Produktiv-Tenant

Namenskonvention (Beispiele):

  • Resource Group: rg-<scope>-<env>-<region>

    • z. B. rg-network-prod-weu, rg-aks-prod-weu

  • AKS: aks-ai-prod-weu

  • VNet: vnet-hub-prod-weu, vnet-aks-prod-weu, vnet-data-prod-weu

  • Key Vault: kv-ai-prod-weu

  • Log Analytics: law-ai-prod-weu

Umgebungen:

  • dev, test, prod – identische Struktur, andere Größen und ggf. weniger Zonen.

2. Netzwerk- & Security-Setup

2.1 Resource Groups

  • rg-network-prod-weu – VNet, Firewall, Bastion, DDoS, Peerings
  • rg-aks-prod-weu – AKS, ACR, Managed Identities
  • rg-app-prod-weu – APIM, Front Door, Application Gateway, Event Hubs, Service Bus
  • rg-data-prod-weu – SQL, Cosmos, Storage, Data Explorer/Synapse
  • rg-secops-prod-weu – Log Analytics, Sentinel, Key Vault, Managed Grafana
  • rg-ops-prod-weu – devOps Hilfsressourcen (Automation, Runbooks, ggf. Jump-VM, Splunk Forwarder etc.)

2.2 Netzwerk-Topologie (Beispiel)

Hub-Spoke-Modell

  • Hub-VNet: vnet-hub-prod-weu – 10.0.0.0/16

    • Subnet snet-azurefw (Firewall): 10.0.0.0/24
    • Subnet snet-bastion: 10.0.1.0/24
    • Subnet snet-shared-services: 10.0.2.0/24

  • Spoke-VNet AKS: vnet-aks-prod-weu – 10.1.0.0/16

    • Subnet snet-aks-nodes: 10.1.0.0/20
    • Subnet snet-aks-system: 10.1.16.0/24
    • Subnet snet-aks-user: 10.1.17.0/24

  • Spoke-VNet Data: vnet-data-prod-weu – 10.2.0.0/16

    • Subnet snet-data-sql: 10.2.0.0/24
    • Subnet snet-data-analytics: 10.2.1.0/24

Komponenten:

  • Azure Firewall in Hub, alle Spokes via VNet-Peering angebunden
  • DDoS Protection Standard auf Hub-VNet
  • Azure Bastion im Hub für SSH/RDP auf Admin-VMs oder Knoten (wenn nötig)
  • PaaS-Services (SQL, Storage, Event Hubs, Service Bus, OpenAI, Key Vault) via Private Endpoints in vnet-data-prod-weu und vnet-aks-prod-weu.

3. Compute- & KI-Setup

3.1 AKS-Cluster

  • Ressource: aks-ai-prod-weu in rg-aks-prod-weu

  • Konfiguration (Beispiel):

    • Kubernetes-Version: 1.29.x (aktuelle LTS zum Rolloutzeitpunkt)

    • Nodepools:

      • systempool (B4ms, 3 Nodes, autoscale 3–10)
      • gpubatch (Standard_NC4as_T4_v3, autoscale 0–10) – für Model Serving / featurized Inference
      • appworkload (D4as_v5, autoscale 3–20) – AI Agents, LLM-Router, APIs, Frontend

    • Network: Azure CNI, IPAM über oben beschriebenes AKS-VNet

    • AKS-managed AAD Integration (Azure Entra ID) für Kubernetes-RBAC

    • OIDC Issuer für Workload-Identitäten (Federation mit Entra ID, kein Secret-basiertes Zugriffskonzept mehr)

Namespaces (Beispiele):

  • ns-frontend – UI-Container
  • ns-api – API & LLM-Router
  • ns-ai-agents – Agent-Orchestration
  • ns-ml-models – self-hosted Modelle
  • ns-observability – Prometheus/Grafana-Agent, OpenTelemetry, etc.

3.2 Container Registry

  • Ressource: acr-ai-prod-weu in rg-aks-prod-weu
  • Private Endpoint zu vnet-aks-prod-weu
  • Defender for Cloud / Defender for Containers Scan auf Images aktiv.

3.3 LLM-Router & Agentic AI

LLM-Router (Beispiel):

  • Deployment llm-router in ns-api (z. B. Python/FastAPI)

  • Patterns:

    • Endpunkte: /v1/chat, /v1/completions, /v1/tools/<toolname>
    • Routing-Tabelle in ConfigMap/Secret (z. B. gpt4o → Azure OpenAI, internal-llm → AKS-Model)

Agentic AI Services:

  • Deployments agent-orchestrator, retriever-service, task-planner in ns-ai-agents

  • Kommunikation mit:

    • Azure AI Search (VECTOR)
    • SQL/Cosmos für Domänendaten
    • Azure OpenAI / self-hosted Models über interne Services

3.4 Azure OpenAI & Self-hosted Models

  • Ressource: aoai-prod-weu (Azure OpenAI / Azure AI Foundry Project)

    • Region: westeurope oder andere EU-Region, Data Residency „EU only“
    • Bereitgestellte Modelle: gpt-4.x, gpt-4o, embedding-3-large etc.

  • Self-hosted Modelle (z. B. Llama, Mistral):

    • Als Deployment in ns-ml-models
    • Exponiert via ClusterIP Services, nur intern erreich­bar
    • GPU-Nodepool gpubatch zugeordnet.

4. Data-Layer-Setup

4.1 SQL, NoSQL, Graph

  • Azure SQL Managed Instance: sqlmi-ai-prod-weu in rg-data-prod-weu

    • Nutzung: Referenzdaten, Konfigurationen, Audit-Tabellen, fachliche Daten
    • Always Encrypted / TDE mit CMK aus Key Vault

  • Azure Cosmos DB (Core + Gremlin):

    • Account: cosmos-ai-prod-weu

    • DBs:

      • aiapp (Core API) – Session-Daten, Konfigurationsobjekte, Agent-States
      • graph (Gremlin API) – Graph der Entitäten / Policies / Relationen

4.2 Object Storage / Data Lake / Vektorindex

  • Storage Account: staiaprodweu (Data Lake Gen2 aktiviert)

    • Container:

      • logs-ai – Langfrist-Logs (immutable)
      • model-artifacts – Model-Checkpoints, Eval-Reports
      • training-data – z. B. anonymisierte Historik

  • Azure AI Search: aisearch-ai-prod-weu

    • Index idx-documents (klassisch) und idx-documents-vector (Vektor)
    • Nutzt Blob Storage als Quelle (Indexers)
    • Dient RAG-Orchestrator als Retrieval-Ziel.

4.3 Messaging & Streaming

  • Event Hubs Namespace: evh-ai-prod-weu

    • Event Hub eh-ai-telemetry (Kafka-kompatibler Endpoint) – Streams von Request-/Systemevents

  • Service Bus Namespace: sb-ai-prod-weu

    • Queues: q-agent-tasks, q-agent-results
    • Topics: t-notifications, t-compliance-events

4.4 Analytics

  • Azure Data Explorer / Synapse Data Explorer: adx-ai-prod-weu

    • Tabellen: ai_requests, ai_decisions, security_events

  • Optional: Synapse Workspace für Reporting und DWH-Workloads.

5. Observability & Security-Setup

5.1 Logging & Monitoring

  • Log Analytics Workspace: law-ai-prod-weu in rg-secops-prod-weu

  • Datenquellen:

    • AKS (Container Logs/Insights)
    • APIM, Application Gateway, Front Door
    • Event Hubs, Service Bus, SQL, Storage
    • Azure OpenAI (über Diagnostic Settings, soweit verfügbar)

  • Azure Monitor + Application Insights:

    • Application Insights Ressource: appi-ai-prod-weu
    • Instrumentation in Frontend, API, Agents, Router
    • Distributed Tracing mit Correlation IDs

  • Azure Managed Grafana:

    • Workspace: graf-ai-prod-weu

    • Data Source: Log Analytics, Azure Monitor

    • Dashboards:

      • „Platform Health“ (CPU, Memory, Node Status, Error Rates)
      • „LLM Performance“ (Latenzen, Tokenverbrauch, Rate-Limits)
      • „Compliance & Oversight“ (Anzahl manueller Overrides, abgebrochene Anfragen, Policy-Verstöße)

5.2 SIEM / SOC

  • Microsoft Sentinel: sentinel-ai-prod-weu (auf law-ai-prod-weu)

    • Daten-Connectors: Azure Activity, Sign-In Logs, Defender, AKS, APIM, WAF-Logs, Custom Logs von Agents

    • Regeln:

      • Anomalie bei LLM-Nutzung (z. B. plötzlicher Spike an bestimmten Endpunkten)
      • Ungewöhnliche Admin-Logins (PIM, Key Vault, AKS)

  • Splunk:

    • Entweder via Add-on für Azure Monitor oder Event Hub Export
    • Forwarder in rg-ops-prod-weu (VM in snet-shared-services)

5.3 Identity, Key Management & Bastion

  • Azure Entra ID:

    • Security-Gruppen: sg-aks-admins, sg-ai-dev, sg-ai-ops, sg-soc
    • App Registrations für Frontend, API, Agents (client credentials / auth code with PKCE)

  • PIM:

    • Rollen wie Owner, Contributor, User Access Administrator, AKS Cluster Admin, Key Vault Admin nur via PIM.

  • Key Vault: kv-ai-prod-weu

    • Secrets:

      • DB Connection Strings (wenn nicht komplett MI-basiert)
      • API Keys (z. B. Drittsysteme), Webhook-Secrets

    • Keys:

      • CMK für SQL, Storage, eventuell OpenAI (falls unterstützt)

    • Certificates:

      • TLS-Zertifikate für App Gateway, APIM (öffentlich + privat)

  • Azure Bastion:

    • Zugriff auf Jump VM (z. B. für Splunk-Forwarder, Spezial-Tools) ohne Public IP.

6. DevOps & Terraform-Setup

6.1 Git & Pipelines

  • GitHub Enterprise Org: mycompany-ai (oder Azure DevOps Project ai-platform)

  • Repositories:

    • infra-landingzone – VNet, RGs, Policies, Key Vault, Log Analytics, Firewall
    • infra-platform – AKS, ACR, APIM, OpenAI, Data Layer
    • app-llm-router, app-ai-agents, app-frontend – Applikationscode

  • Pipelines:

    • infra-landingzone-ci / cd → Terraform Plan/Apply gegen dev/test/prod
    • app-*-ci → Build, Tests, Container-Build, Push nach ACR
    • app-*-cd → Deploy nach AKS (Helm)

6.2 Terraform – Beispiel-Snippets (stark vereinfacht)

Backend & Provider (infra-landingzone)

terraform {
  required_version = ">= 1.8.0"

  backend "azurerm" {
    resource_group_name  = "rg-tfstate-prod-weu"
    storage_account_name = "sttfstateprodweu"
    container_name       = "tfstate"
    key                  = "landingzone-prod.tfstate"
  }

  required_providers {
    azurerm = {
      source  = "hashicorp/azurerm"
      version = "~> 4.0"
    }
  }
}

provider "azurerm" {
  features {}
}

Beispiel: VNet + Subnet für AKS

resource "azurerm_virtual_network" "vnet_aks" {
  name                = "vnet-aks-prod-weu"
  location            = "westeurope"
  resource_group_name = "rg-network-prod-weu"
  address_space       = ["10.1.0.0/16"]
}

resource "azurerm_subnet" "snet_aks_nodes" {
  name                 = "snet-aks-nodes"
  resource_group_name  = azurerm_virtual_network.vnet_aks.resource_group_name
  virtual_network_name = azurerm_virtual_network.vnet_aks.name
  address_prefixes     = ["10.1.0.0/20"]
}

Beispiel: AKS-Cluster (gekürzt)

resource "azurerm_kubernetes_cluster" "aks_ai" {
  name                = "aks-ai-prod-weu"
  location            = "westeurope"
  resource_group_name = "rg-aks-prod-weu"
  dns_prefix          = "aks-ai-prod"

  sku_tier = "Standard"

  default_node_pool {
    name                = "systempool"
    vm_size             = "Standard_D4as_v5"
    node_count          = 3
    enable_auto_scaling = true
    min_count           = 3
    max_count           = 10
    vnet_subnet_id      = azurerm_subnet.snet_aks_nodes.id
  }

  identity {
    type = "SystemAssigned"
  }

  azure_active_directory_role_based_access_control {
    managed                = true
    admin_group_object_ids = ["<GUID-AKS-Admins-Group>"]
  }

  oidc_issuer_enabled       = true
  workload_identity_enabled = true

  network_profile {
    network_plugin = "azure"
  }
}

Beispiel: Log Analytics + Diagnostics

resource "azurerm_log_analytics_workspace" "law_ai" {
  name                = "law-ai-prod-weu"
  location            = "westeurope"
  resource_group_name = "rg-secops-prod-weu"
  sku                 = "PerGB2018"
}

resource "azurerm_monitor_diagnostic_setting" "aks_diag" {
  name                       = "diag-aks-ai-prod"
  target_resource_id         = azurerm_kubernetes_cluster.aks_ai.id
  log_analytics_workspace_id = azurerm_log_analytics_workspace.law_ai.id

  enabled_log {
    category = "kube-apiserver"
  }

  enabled_log {
    category = "kube-audit"
  }

  metric {
    category = "AllMetrics"
  }
}

Beispiel: Azure OpenAI (Symbolisch) (Hinweis: die Terraform-Resource ist je nach Provider-Version ggf. unterschiedlich; pseudomäßig:)

resource "azurerm_cognitive_account" "aoai" {
  name                = "aoai-prod-weu"
  location            = "westeurope"
  resource_group_name = "rg-app-prod-weu"
  kind                = "OpenAI"
  sku_name            = "S0"

  custom_subdomain_name = "aoai-prod"

  identity {
    type = "SystemAssigned"
  }
}

Wenn du magst, kann ich als nächsten Schritt:

  • das Beispiel auf „nur KI-Kernplattform“ zurechtschneiden (Minimal-Setup),
  • oder dir eine konkrete AKS-Namespace- und Helm-Chart-Struktur für LLM-Router & Agents vorschlagen.
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