Quota Optimization Strategies

Comprehensive strategies for optimizing Microsoft 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:

Deployment Type	Capacity (TPM)	Quota Freed	Cost Impact (TPM)	Cost Impact (PTU)
Test deployment	10K TPM	10K TPM	$0 (pay-per-use)	N/A
Unused production	100K TPM	100K TPM	$0 (pay-per-use)	N/A
Abandoned PTU deployment	100 PTU	~40K TPM equivalent	$0 TPM	$3,650/month saved (100 PTU × 730h × $0.05/h)
High-capacity test	450K TPM	450K TPM	$0 (pay-per-use)	N/A

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.

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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)

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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

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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> \