Quota Optimization Strategies

Comprehensive strategies for optimizing Azure AI Foundry quota allocation and reducing costs.

Table of Contents: 1. Identify and Delete Unused Deployments · 2. Right-Size Over-Provisioned Deployments · 3. Consolidate Multiple Small Deployments · 4. Cost Optimization Strategies · 5. Regional Quota Rebalancing

1. Identify and Delete Unused Deployments

Step 1: Discovery with Quota Context

Get quota limits FIRST to understand how close you are to capacity:

# Check current quota usage vs limits (run this FIRST)
subId=$(az account show --query id -o tsv)
region="eastus"  # Change to your region
az rest --method get \
  --url "https://management.azure.com/subscriptions/$subId/providers/Microsoft.CognitiveServices/locations/$region/usages?api-version=2023-05-01" \
  --query "value[?contains(name.value,'OpenAI')].{Model:name.value, Used:currentValue, Limit:limit, Available:'(Limit - Used)'}" -o table

Step 2: Parallel Deployment Enumeration

List all deployments across resources efficiently:

# Get all Foundry resources
resources=$(az cognitiveservices account list --query "[?kind=='AIServices'].{name:name,rg:resourceGroup}" -o json)

# Parallel deployment enumeration (faster than sequential)
echo "$resources" | jq -r '.[] | "\(.name) \(.rg)"' | while read name rg; do
  echo "=== $name ($rg) ==="
  az cognitiveservices account deployment list --name "$name" --resource-group "$rg" \
    --query "[].{Deployment:name,Model:properties.model.name,Capacity:sku.capacity,Created:systemData.createdAt}" -o table &
done
wait  # Wait for all background jobs to complete

Step 3: Identify Stale Deployments

Criteria for deletion candidates:

• Test/temporary naming: Contains "test", "demo", "temp", "dev" in deployment name
• Old timestamps: Created >90 days ago with timestamp-based naming (e.g., "gpt4-20231015")
• High capacity consumers: Deployments with >100K TPM capacity that haven't been referenced in recent logs
• Duplicate models: Multiple deployments of same model/version in same region

Example pattern matching for stale deployments:

# Find deployments with test/temp naming
az cognitiveservices account deployment list --name <resource> --resource-group <rg> \
  --query "[?contains(name,'test') || contains(name,'demo') || contains(name,'temp')].{Name:name,Capacity:sku.capacity}" -o table

Step 4: Delete and Verify Quota Recovery

# Delete unused deployment (quota freed IMMEDIATELY)
az cognitiveservices account deployment delete --name <resource> --resource-group <rg> --deployment-name <deployment>

# Verify quota freed (re-run Step 1 quota check)
# You should see "Used" decrease by the deployment's capacity

Cost Impact Analysis:

Key Insight: For TPM (Standard) deployments, deletion frees quota but has no direct cost impact (you pay per token used). For PTU (Provisioned) deployments, deletion immediately stops hourly charges and can save thousands per month.

2. Right-Size Over-Provisioned Deployments

Identify over-provisioned deployments:

• Check Azure Monitor metrics for actual token usage
• Compare allocated TPM vs. peak usage
• Look for deployments with <50% utilization

Right-sizing example:

# Update deployment to lower capacity
az cognitiveservices account deployment update --name <resource> --resource-group <rg> \
  --deployment-name <deployment> --sku-capacity 30  # Reduce from 50K to 30K TPM

Cost Optimization:

• TPM (Standard): Reduces regional quota consumption (no direct cost savings, pay-per-token)
• PTU (Provisioned): Direct cost reduction (40% capacity reduction = 40% cost reduction)

3. Consolidate Multiple Small Deployments

Pattern: Multiple 10K TPM deployments → One 30-50K TPM deployment

Benefits:

• Fewer deployment slots consumed
• Simpler management
• Same total capacity, better utilization

Example:

• Before: 3 deployments @ 10K TPM each = 30K TPM total, 3 slots used
• After: 1 deployment @ 30K TPM = 30K TPM total, 1 slot used
• Savings: 2 deployment slots freed for other models

4. Cost Optimization Strategies

Official Documentation: Plan to manage costs for Azure OpenAI and Fine-tuning cost management

A. Use Fine-Tuned Smaller Models (from Microsoft Transparency Note):

You can reduce costs or latency by swapping a fine-tuned version of a smaller/faster model (e.g., fine-tuned GPT-3.5-Turbo) for a more general-purpose model (e.g., GPT-4).

# Deploy fine-tuned GPT-3.5 Turbo as cost-effective alternative to GPT-4
az cognitiveservices account deployment create --name <resource> --resource-group <rg> \
  --deployment-name gpt-35-tuned --model-name <your-fine-tuned-model> \
  --model-format OpenAI --sku-name Standard --sku-capacity 10

B. Remove Unused Fine-Tuned Deployments (from Fine-tuning cost management):

Fine-tuned model deployments incur hourly hosting costs even when not in use. Remove unused deployments promptly to control costs.

• Inactive deployments unused for 15 consecutive days are automatically deleted
• Proactively delete unused fine-tuned deployments to avoid hourly charges

# Delete unused fine-tuned deployment
az cognitiveservices account deployment delete --name <resource> --resource-group <rg> \
  --deployment-name <unused-fine-tuned-deployment>

C. Batch Multiple Requests (from Cost optimization Q&A):

Batch multiple requests together to reduce the total number of API calls and lower overall costs.

D. Use Commitment Tiers for Predictable Costs (from Managing costs guide):

• Pay-as-you-go: Bills according to usage (variable costs)
• Commitment tiers: Commit to using service features for a fixed fee (predictable costs, potential savings for consistent usage)

5. Regional Quota Rebalancing

If you have quota spread across multiple regions but only use some:

# Check quota across regions
for region in eastus westus uksouth; do
  echo "=== $region ==="
  subId=$(az account show --query id -o tsv)
  az rest --method get \
    --url "https://management.azure.com/subscriptions/$subId/providers/Microsoft.CognitiveServices/locations/$region/usages?api-version=2023-05-01" \
    --query "value[?contains(name.value,'OpenAI')].{Model:name.value, Used:currentValue, Limit:limit}" -o table
done

Optimization: Concentrate deployments in fewer regions to maximize quota utilization per region.