title: 'Azure Deployment Options' description: 'Guide to deploying Spice.ai applications on Microsoft Azure' sidebar_label: 'Azure' sidebar_position: 2 pagination_next: null keywords: [ spice.ai, azure, aks, azure container apps, azure container instances, azure virtual machines, azure kubernetes service, arm, bicep, terraform, ]

Spice.ai provides multiple deployment options on Microsoft Azure, enabling data and AI applications to run on Azure's global infrastructure. Whether using virtual machines, container orchestration, or serverless containers, Spice deploys to meet requirements for performance, scalability, and cost efficiency.

For a complete list of Azure-compatible data connectors, AI models, and integrations, see Azure Integrations (opens in a new tab).

Benefits of Deploying on Azure

• Scalability: Scale Spice.ai workloads with virtual machine scale sets (opens in a new tab), AKS (opens in a new tab), and Container Apps (opens in a new tab).
• Global Reach: Deploy across Azure's worldwide regions (opens in a new tab) for low-latency access.
• Integration: Connect to other Azure services such as Azure Blob Storage (opens in a new tab), Azure SQL Database (opens in a new tab), Azure Database for PostgreSQL (opens in a new tab), and Azure Key Vault (opens in a new tab).
• Cost Control: Choose from VM sizes (opens in a new tab), reserved instances (opens in a new tab), and spot pricing (opens in a new tab) to match workload requirements.
• Security and Compliance: Run Spice.ai inside an Azure security perimeter using Virtual Networks (opens in a new tab), private endpoints (opens in a new tab), Microsoft Entra ID (opens in a new tab), and managed identities (opens in a new tab).

Deployment Options

Azure Kubernetes Service (AKS)

Run Spice.ai on Azure Kubernetes Service (opens in a new tab) when the workload benefits from Kubernetes orchestration, multi-replica scale, declarative configuration, or shared cluster tenancy. AKS pairs well with the Spice Helm chart (opens in a new tab) and the Argo CD (opens in a new tab) or Flux (opens in a new tab) GitOps workflows.

1. Provision the cluster

Provision an AKS cluster with workload identity (opens in a new tab) and the OIDC issuer (opens in a new tab) enabled — both are required for federated credentials to Azure services.

RG=spiceai-rg
CLUSTER=spiceai-prod
LOCATION=eastus

az group create --name $RG --location $LOCATION

az aks create \
  --resource-group $RG \
  --name $CLUSTER \
  --location $LOCATION \
  --kubernetes-version 1.31 \
  --node-count 3 \
  --node-vm-size Standard_D4s_v5 \
  --enable-cluster-autoscaler --min-count 2 --max-count 6 \
  --enable-oidc-issuer \
  --enable-workload-identity \
  --network-plugin azure \
  --generate-ssh-keys

az aks get-credentials --resource-group $RG --name $CLUSTER

RG=spiceai-rg
CLUSTER=spiceai-prod
LOCATION=eastus

az group create --name $RG --location $LOCATION

az aks create \
  --resource-group $RG \
  --name $CLUSTER \
  --location $LOCATION \
  --kubernetes-version 1.31 \
  --node-count 3 \
  --node-vm-size Standard_D4s_v5 \
  --enable-cluster-autoscaler --min-count 2 --max-count 6 \
  --enable-oidc-issuer \
  --enable-workload-identity \
  --network-plugin azure \
  --generate-ssh-keys

az aks get-credentials --resource-group $RG --name $CLUSTER

For burst or low-utilization workloads, attach virtual nodes (opens in a new tab) backed by Azure Container Instances. For production, prefer Bicep or Terraform for repeatable provisioning — the Azure Verified Modules (opens in a new tab) library publishes a maintained AKS module.

2. Configure workload identity for Azure access

Most Spice connectors (ABFS, Azure SQL, Key Vault, Azure OpenAI) accept Azure credentials from the environment. Use workload identity (opens in a new tab) so pods receive scoped, federated tokens without static secrets:

# 1. Create a user-assigned managed identity
az identity create --resource-group $RG --name spiceai-identity
CLIENT_ID=$(az identity show -g $RG -n spiceai-identity --query clientId -o tsv)
PRINCIPAL_ID=$(az identity show -g $RG -n spiceai-identity --query principalId -o tsv)

# 2. Grant the identity access to Azure resources the Spicepod needs
az role assignment create \
  --assignee-object-id $PRINCIPAL_ID --assignee-principal-type ServicePrincipal \
  --role "Storage Blob Data Reader" \
  --scope "/subscriptions/<sub>/resourceGroups/$RG/providers/Microsoft.Storage/storageAccounts/<acct>"

# 3. Federate the identity with the Kubernetes ServiceAccount
ISSUER=$(az aks show -g $RG -n $CLUSTER --query oidcIssuerProfile.issuerUrl -o tsv)
az identity federated-credential create \
  --name spiceai-fed \
  --identity-name spiceai-identity \
  --resource-group $RG \
  --issuer "$ISSUER" \
  --subject system:serviceaccount:spiceai:spiceai \
  --audiences api://AzureADTokenExchange

# 1. Create a user-assigned managed identity
az identity create --resource-group $RG --name spiceai-identity
CLIENT_ID=$(az identity show -g $RG -n spiceai-identity --query clientId -o tsv)
PRINCIPAL_ID=$(az identity show -g $RG -n spiceai-identity --query principalId -o tsv)

# 2. Grant the identity access to Azure resources the Spicepod needs
az role assignment create \
  --assignee-object-id $PRINCIPAL_ID --assignee-principal-type ServicePrincipal \
  --role "Storage Blob Data Reader" \
  --scope "/subscriptions/<sub>/resourceGroups/$RG/providers/Microsoft.Storage/storageAccounts/<acct>"

# 3. Federate the identity with the Kubernetes ServiceAccount
ISSUER=$(az aks show -g $RG -n $CLUSTER --query oidcIssuerProfile.issuerUrl -o tsv)
az identity federated-credential create \
  --name spiceai-fed \
  --identity-name spiceai-identity \
  --resource-group $RG \
  --issuer "$ISSUER" \
  --subject system:serviceaccount:spiceai:spiceai \
  --audiences api://AzureADTokenExchange

Reference the identity from the Helm release so Spice pods inherit federated tokens via the DefaultAzureCredential (opens in a new tab) chain:

# values.yaml
serviceAccount:
  create: true
  name: spiceai
  annotations:
    azure.workload.identity/client-id: "<CLIENT_ID>"
podLabels:
  azure.workload.identity/use: "true"

# values.yaml
serviceAccount:
  create: true
  name: spiceai
  annotations:
    azure.workload.identity/client-id: "<CLIENT_ID>"
podLabels:
  azure.workload.identity/use: "true"

3. Install Spice.ai

helm repo add spiceai https://helm.spiceai.org
helm repo update

helm upgrade --install spiceai spiceai/spiceai \
  --namespace spiceai --create-namespace \
  --version 1.11.5 \
  -f values.yaml

helm repo add spiceai https://helm.spiceai.org
helm repo update

helm upgrade --install spiceai spiceai/spiceai \
  --namespace spiceai --create-namespace \
  --version 1.11.5 \
  -f values.yaml

For declarative GitOps, swap this command for an Argo CD Application or a Flux HelmRelease pointing at the same chart. See the Argo CD (opens in a new tab) or Flux (opens in a new tab) guides for full manifests.

4. Storage and ingress

For stateful acceleration (DuckDB, SQLite, Cayenne):

• Local NVMe (recommended) — Spice acceleration is latency- and IOPS-sensitive, so the lowest-latency option is a node-local NVMe SSD on an instance family with attached NVMe (Lsv3 / Lasv3 (opens in a new tab), Ddsv5 / Ddsv6 (opens in a new tab), Edsv5 / Edsv6 (opens in a new tab)). Expose the local NVMe through the Local Volume Static Provisioner (opens in a new tab) as a local-storage StorageClass. Local NVMe does not survive node replacement, so pair with a refresh strategy or a re-hydration source.
• Premium SSD v2 — when shared / replica-attachable persistence is required, Premium SSD v2 (opens in a new tab) delivers up to 80,000 IOPS and sub-millisecond latency with independently configurable IOPS and throughput. Use the Azure Disks CSI driver (opens in a new tab) with a custom StorageClass (skuName: PremiumV2_LRS).
• Premium SSD (managed-csi-premium) — use the built-in managed-csi-premium storage class only when Premium SSD v2 is unavailable in a region.
• Azure Files (azurefile-csi) — not recommended for acceleration — use only for stateless shared artefacts that need ReadWriteMany. SMB/NFS latency negates the benefit of using a local accelerator.
• Set stateful.enabled: true and stateful.storageClass: <chosen-class> in values.yaml.

:::tip[Spice.ai Enterprise] For production stateful workloads, the Spice.ai Enterprise (opens in a new tab) Operator's SpicepodSet (opens in a new tab) provides per-replica StatefulSets with automatic PVC resizing, workload-identity-aware ServiceAccount annotations, and configurable update strategies. For distributed query execution across scheduler/executor tiers backed by Azure Blob Storage, see SpicepodCluster (opens in a new tab). :::

To expose Spice externally, install the Application Gateway Ingress Controller (AGIC) (opens in a new tab) or use a Standard public Load Balancer (opens in a new tab):

# values.yaml
service:
  type: LoadBalancer
  additionalAnnotations:
    service.beta.kubernetes.io/azure-load-balancer-internal: "true" # internal only

# values.yaml
service:
  type: LoadBalancer
  additionalAnnotations:
    service.beta.kubernetes.io/azure-load-balancer-internal: "true" # internal only

For internal-only deployments, set azure-load-balancer-internal: "true" to bind to the cluster's VNet rather than a public IP.

5. Observability

The Spice Helm chart ships a PodMonitor resource for the Prometheus Operator (opens in a new tab). On AKS, the Azure Monitor managed service for Prometheus (opens in a new tab) and Container insights (opens in a new tab) are the common targets. Set monitoring.podMonitor.enabled: true and import the Spice Grafana dashboard (opens in a new tab) into Azure Managed Grafana (opens in a new tab).

For comprehensive guidance, refer to the Azure Kubernetes Service documentation (opens in a new tab), the AKS baseline architecture (opens in a new tab), and the Spice.ai Kubernetes Deployment Guide (opens in a new tab).

Azure Container Apps

Azure Container Apps (opens in a new tab) is a serverless container platform suitable for HTTP-driven Spice.ai workloads that benefit from scale-to-zero and request-based autoscaling. Use it when a single managed container is sufficient and operating Kubernetes is not desired.

1. Create the environment

The environment is the security and networking boundary that hosts one or more container apps:

RG=spiceai-rg
ENV=spiceai-env
LOCATION=eastus

az group create --name $RG --location $LOCATION

az containerapp env create \
  --name $ENV \
  --resource-group $RG \
  --location $LOCATION \
  --logs-destination log-analytics

RG=spiceai-rg
ENV=spiceai-env
LOCATION=eastus

az group create --name $RG --location $LOCATION

az containerapp env create \
  --name $ENV \
  --resource-group $RG \
  --location $LOCATION \
  --logs-destination log-analytics

To reach Azure SQL, Storage, or Key Vault behind private endpoints, attach the environment to a VNet-injected subnet (opens in a new tab) by adding --infrastructure-subnet-resource-id and --internal-only true.

2. Configure managed identity

Container Apps support both system-assigned and user-assigned managed identities (opens in a new tab). A user-assigned identity is preferred so role assignments survive app recreation:

az identity create --resource-group $RG --name spiceai-identity
IDENTITY_ID=$(az identity show -g $RG -n spiceai-identity --query id -o tsv)
PRINCIPAL_ID=$(az identity show -g $RG -n spiceai-identity --query principalId -o tsv)

# Grant access to the resources the Spicepod connects to
az role assignment create \
  --assignee-object-id $PRINCIPAL_ID --assignee-principal-type ServicePrincipal \
  --role "Storage Blob Data Reader" \
  --scope "/subscriptions/<sub>/resourceGroups/$RG/providers/Microsoft.Storage/storageAccounts/<acct>"

az identity create --resource-group $RG --name spiceai-identity
IDENTITY_ID=$(az identity show -g $RG -n spiceai-identity --query id -o tsv)
PRINCIPAL_ID=$(az identity show -g $RG -n spiceai-identity --query principalId -o tsv)

# Grant access to the resources the Spicepod connects to
az role assignment create \
  --assignee-object-id $PRINCIPAL_ID --assignee-principal-type ServicePrincipal \
  --role "Storage Blob Data Reader" \
  --scope "/subscriptions/<sub>/resourceGroups/$RG/providers/Microsoft.Storage/storageAccounts/<acct>"

3. Deploy Spice.ai

Mount Azure Files (opens in a new tab) for stateful acceleration, inject secrets from Key Vault (opens in a new tab), and configure HTTP ingress on port 8090:

az containerapp create \
  --name spiceai \
  --resource-group $RG \
  --environment $ENV \
  --image spiceai/spiceai:1.11.5-models \
  --target-port 8090 \
  --ingress external \
  --transport http \
  --user-assigned $IDENTITY_ID \
  --min-replicas 1 --max-replicas 5 \
  --cpu 1.0 --memory 2.0Gi \
  --env-vars \
      SPICED_LOG=INFO \
      AZURE_CLIENT_ID=$(az identity show -g $RG -n spiceai-identity --query clientId -o tsv) \
  --secrets spiceai-key=keyvaultref:https://my-vault.vault.azure.net/secrets/spiceai-key,identityref:$IDENTITY_ID \
  --secret-volume-mount /mnt/secrets

az containerapp create \
  --name spiceai \
  --resource-group $RG \
  --environment $ENV \
  --image spiceai/spiceai:1.11.5-models \
  --target-port 8090 \
  --ingress external \
  --transport http \
  --user-assigned $IDENTITY_ID \
  --min-replicas 1 --max-replicas 5 \
  --cpu 1.0 --memory 2.0Gi \
  --env-vars \
      SPICED_LOG=INFO \
      AZURE_CLIENT_ID=$(az identity show -g $RG -n spiceai-identity --query clientId -o tsv) \
  --secrets spiceai-key=keyvaultref:https://my-vault.vault.azure.net/secrets/spiceai-key,identityref:$IDENTITY_ID \
  --secret-volume-mount /mnt/secrets

To run multiple replicas with shared file-based acceleration, define an Azure Files storage mount (opens in a new tab) and reference it from the app's revision template, then point file accelerators at the mount path (for example, duckdb_file: /data/taxi_trips.db).

4. Scaling rules

Spice.ai is HTTP-driven, so the default HTTP scale rule (opens in a new tab) (concurrent requests per replica) is usually sufficient. For background workloads (refresh schedules, ingestion) that should not scale to zero, set --min-replicas 1 and add a custom scale rule (opens in a new tab) backed by a CPU or queue metric:

az containerapp update \
  --name spiceai --resource-group $RG \
  --scale-rule-name http-rule \
  --scale-rule-type http \
  --scale-rule-http-concurrency 50

az containerapp update \
  --name spiceai --resource-group $RG \
  --scale-rule-name http-rule \
  --scale-rule-type http \
  --scale-rule-http-concurrency 50

5. Health probes and revisions

Configure the liveness and readiness probes (opens in a new tab) to use /health and /v1/ready. Container Apps creates a new revision (opens in a new tab) on each update, supporting traffic splitting between revisions for canary upgrades:

az containerapp revision set-mode --name spiceai --resource-group $RG --mode multiple
az containerapp ingress traffic set --name spiceai --resource-group $RG \
  --revision-weight latest=90 spiceai--prev=10

az containerapp revision set-mode --name spiceai --resource-group $RG --mode multiple
az containerapp ingress traffic set --name spiceai --resource-group $RG \
  --revision-weight latest=90 spiceai--prev=10

For more details, see the Azure Container Apps documentation (opens in a new tab) and the Spice.ai Docker Deployment Guide (opens in a new tab).

Azure Container Instances (ACI)

Run Spice.ai as a single container without provisioning a cluster using Azure Container Instances (opens in a new tab). Suitable for development environments, scheduled jobs, and low-traffic deployments.

az container create \
  --resource-group my-rg \
  --name spiceai \
  --image spiceai/spiceai:latest \
  --cpu 2 --memory 4 \
  --ports 8090 50051 \
  --ip-address public \
  --environment-variables SPICED_LOG=INFO \
  --azure-file-volume-share-name spice-data \
  --azure-file-volume-account-name mystorageacct \
  --azure-file-volume-account-key "<key>" \
  --azure-file-volume-mount-path /data

az container create \
  --resource-group my-rg \
  --name spiceai \
  --image spiceai/spiceai:latest \
  --cpu 2 --memory 4 \
  --ports 8090 50051 \
  --ip-address public \
  --environment-variables SPICED_LOG=INFO \
  --azure-file-volume-share-name spice-data \
  --azure-file-volume-account-name mystorageacct \
  --azure-file-volume-account-key "<key>" \
  --azure-file-volume-mount-path /data

Refer to the Azure Container Instances documentation (opens in a new tab) for advanced networking, virtual network integration (opens in a new tab), and managed identity (opens in a new tab) configuration.

Azure Virtual Machines

Deploy Spice.ai directly on Azure Virtual Machines (opens in a new tab) for maximum control over the environment, GPU access, or large-memory instance types.

1. Manual VM deployment:

   • Provision a Linux VM (Ubuntu, Debian, or Azure Linux) with an appropriate VM size (opens in a new tab).
   • Install Docker Engine (opens in a new tab) and run Spice.ai as a Docker container (opens in a new tab), or install the spice binary directly. See the installation guide (opens in a new tab).
   • Attach a managed identity (opens in a new tab) so Spice can read from Blob Storage, Azure SQL, and Key Vault without static credentials.

2. Automated deployment with Bicep or Terraform:

   • Define infrastructure in a Bicep template (opens in a new tab) or Terraform configuration (opens in a new tab), including the VM, NIC, NSG, virtual network, and managed identity.
   • Use cloud-init (opens in a new tab) or a custom script extension (opens in a new tab) to install Docker, pull the Spice.ai image (opens in a new tab), retrieve secrets from Key Vault (opens in a new tab), and start the runtime.
   • Use VM scale sets (opens in a new tab) for horizontally scaled deployments fronted by an Azure Load Balancer (opens in a new tab) or Application Gateway (opens in a new tab).

For detailed guidance, refer to the Linux on Azure documentation (opens in a new tab), Bicep documentation (opens in a new tab), and Azure provider for Terraform (opens in a new tab).

Authentication

Most Azure services that Spice connects to accept explicit credentials through component parameters (for example, an azure_storage_account_key on the ABFS connector (opens in a new tab)). When explicit credentials are not provided, Spice follows the standard Azure Identity DefaultAzureCredential (opens in a new tab) chain, attempting credentials in this order:

1. Environment variables:

   • AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET (service principal with secret)
   • AZURE_CLIENT_CERTIFICATE_PATH (service principal with certificate)
   • AZURE_USERNAME, AZURE_PASSWORD (resource owner password — not recommended)

2. Workload Identity (AKS): federated tokens injected via AZURE_FEDERATED_TOKEN_FILE, AZURE_AUTHORITY_HOST, AZURE_CLIENT_ID, and AZURE_TENANT_ID. See Workload Identity for AKS (opens in a new tab).

3. Managed identity: System-assigned or user-assigned identities on Azure VMs, AKS, Container Apps, and ACI. See What are managed identities? (opens in a new tab).

4. Azure CLI: Cached credentials from a local az login session. Common during development.

5. Azure Developer CLI (azd) and Azure PowerShell: Used when the corresponding CLI is signed in.

For services with explicit parameters (Blob Storage, Azure SQL, Cosmos DB, OpenAI), prefer named credentials or managed identity over environment variables in production.

:::note[Role assignments] Regardless of the credential source, the principal must have the appropriate Azure role assignments (for example, Storage Blob Data Reader on a storage account, or SQL DB Contributor on Azure SQL). When a Spicepod connects to multiple Azure services, the principal must have permissions across all of them. :::

Resources

Documentation

• Azure Integrations (opens in a new tab) — complete list of Azure data connectors, AI models, and supported services
• Spice.ai Kubernetes Deployment Guide (opens in a new tab) — Helm, Argo CD, and Flux options for AKS

Azure Marketplace

Spice.ai is not yet published to the Microsoft Azure Marketplace (opens in a new tab) (coming soon). In the meantime, deploy using the spiceai/spiceai (opens in a new tab) container image or the Spice Helm chart (opens in a new tab).