Ensure that OpenAI deployment SKUs meet your organization’s specific requirements. These can be based on your organization’s data processing location compliance or usage (e.g., Standard for variable workloads, ProvisionedManaged for high volume).
When deploying OpenAI services in Azure, selecting the appropriate SKU (Stock Keeping Unit) is a critical decision that impacts cost efficiency, performance, and compliance. Different SKUs offer varying levels of computational resources, pricing models, and geographical availability. Making informed choices about these deployments can lead to significant cost savings while maintaining the performance levels your applications require.
Azure OpenAI Service offers multiple deployment options, each designed for specific use cases:
Standard SKUs: Pay-per-token pricing model ideal for variable workloads
ProvisionedManaged SKUs: Fixed capacity with predictable pricing for high-volume scenarios
Regional SKUs: Variations based on geographic data processing requirements
Organizations that don’t standardize their OpenAI SKU selection often experience unnecessary cost overruns, performance issues, and potential compliance violations.
Cost Impact Assessment
Selecting non-optimal SKUs can lead to substantial unnecessary expenditures. Here’s how the wrong choices impact your cloud budget:
Overprovisioning: Using ProvisionedManaged SKUs for variable or low-volume workloads results in paying for unused capacity
Regional price variations: Costs can vary up to 15-20% between regions
Newer model versions: Often more cost-effective than older generations for the same capabilities
Potential Savings
Consider these real-world examples of cost optimization through proper SKU selection:
Example 1: Workload-Appropriate SKU Selection
Organization using ProvisionedManaged SKU ($10/hour) for sporadic workloads
Monthly cost: $7,200 (24×7 availability)
After switching to Standard SKU (pay-per-token): $1,800/month
Monthly savings: $5,400 (75% reduction)
Example 2: Regional Optimization
10 million tokens processed daily in higher-cost region: $8,000/month
Same workload in optimized region: $6,800/month
Monthly savings: $1,200 (15% reduction)
Example 3: Multiple Small Deployments Consolidation
Five separate small ProvisionedManaged deployments: $3,600/month each ($18,000 total)
Consolidated to two optimized deployments: $7,200/month
Monthly savings: $10,800 (60% reduction)
Implementation Guide
Infrastructure-as-Code Implementation (Terraform Example)
When defining OpenAI deployments in Terraform, ensure you’re selecting the appropriate SKU based on your usage patterns and compliance requirements.
Non-Compliant Example:
resource "azurerm_openai_account" "example" {
name = "example-openai"
resource_group_name = azurerm_resource_group.example.name
location = "West US"
sku_name = "S0"
}
resource "azurerm_openai_deployment" "example" {
name = "example-deployment"
openai_account_id = azurerm_openai_account.example.id
model {
format = "OpenAI"
name = "gpt-4"
version = "0613"
}
scale {
type = "Standard"
capacity = 120
}
}
Compliant Example:
resource "azurerm_openai_account" "example" {
name = "example-openai"
resource_group_name = azurerm_resource_group.example.name
location = "East US" # Choose region based on compliance and cost
sku_name = "S0"
}
resource "azurerm_openai_deployment" "example" {
name = "example-deployment"
openai_account_id = azurerm_openai_account.example.id
model {
format = "OpenAI"
name = "gpt-4"
version = "1106-preview" # Use newer versions when appropriate
}
scale {
type = "ProvisionedManaged" # Only use for consistent high-volume workloads
capacity = 60 # Right-sized based on actual usage patterns
}
}
Step-by-Step Implementation
Audit existing deployments: Use Infracost to scan your infrastructure code and identify non-compliant OpenAI SKUs. Infracost includes this policy check, enabling you to quickly identify optimization opportunities.
Analyze usage patterns:
Review token consumption and API call patterns over 30-60 days
Identify peak usage and baseline requirements
Determine if usage is predictable or variable
Define SKU selection criteria:
For variable or unpredictable workloads: Use Standard SKUs
For high-volume, consistent workloads: Consider ProvisionedManaged SKUs
For regulated workloads: Ensure regional selection meets compliance requirements
Implement SKU standards in IaC:
Update Terraform/ARM/Bicep templates with standardized SKU configurations
Implement automated validation using Infracost to prevent deployment of non-compliant SKUs
Document exceptions with appropriate justification
Monitor and optimize:
Regularly review usage metrics to ensure SKU selections remain appropriate
Adjust capacity or SKU type as usage patterns evolve
Best Practices
Create a SKU selection framework based on:
Monthly token volume
Request pattern predictability
Budget constraints
Compliance requirements
Performance needs
Implement guardrails:
Use Infracost policies to prevent deployment of non-preferred SKUs
Create approval workflows for exceptions
Document justifications for non-standard selections
Establish regular review cycles:
Quarterly assessment of SKU appropriateness
Alignment with model version updates from OpenAI
Cost vs. performance optimization
Centralize model deployment management:
Use shared services approach where possible
Consolidate deployments to reduce overhead
Standardize deployment patterns
Example Scenarios
Example 1: Enterprise AI Development Platform
Before Policy Implementation:
Multiple teams deploying individual OpenAI instances
Mix of SKUs across regions with no standardization
Inconsistent versioning and unnecessary duplications
Monthly spend: $42,000
After Policy Implementation:
Standardized deployments based on workload type
Consolidated to three regional deployments
Optimized SKU selection based on usage patterns
Monthly spend: $23,000 (45% reduction)
Example 2: AI-Powered Customer Service System
Before Policy Implementation:
ProvisionedManaged SKU deployed for 24/7 availability
Actual usage concentrated in business hours
70% of capacity unused during nights and weekends
Monthly spend: $21,600
After Policy Implementation:
Switched to Standard SKU with pay-per-token model
Maintained smaller ProvisionedManaged instance for baseline operations
Implemented auto-scaling for peak periods
Monthly spend: $8,900 (59% reduction)
Example 3: Regulatory Compliance Scenario
Before Policy Implementation:
All AI workloads deployed in US regions by default
EU data processing requirements not consistently met
Risk of non-compliance with GDPR
Unnecessary data transfer costs
After Policy Implementation:
Region-specific deployment strategy
EU data processed in EU regions
Reduced latency for regional users
Eliminated compliance risks
Reduced data transfer costs by 22%
Considerations and Caveats
When This Policy May Not Apply
Prototype or POC environments: During initial testing phases, standard deployments may be acceptable for short durations
Specialized model requirements: Some specific models may only be available in certain regions or SKUs
Integration constraints: Some legacy systems may have dependencies requiring specific deployment configurations
Implementation Challenges
Usage forecasting complexity: Accurately predicting token consumption patterns can be difficult, especially for new applications
Model version transitions: Changing model versions may require recalibration of capacity requirements
Regional availability limitations: Not all models are available in all regions, potentially forcing trade-offs between locality and model capability
Performance Considerations
Cold start impacts: Standard SKUs may experience latency during periods of inactivity
Quota limitations: Be aware of subscription and regional quota constraints when planning deployments
Burst capacity requirements: Some workloads may have extreme peak demands that justify oversizing
Monitoring and Maintenance
To ensure ongoing optimization:
Implement usage dashboards tracking:
Token consumption by deployment
Request patterns and peak usage
Cost per model version and deployment
Set up alerting for:
Sustained high utilization (>80%)
Extended periods of low utilization (<20%)
Cost anomalies or sudden changes in usage patterns
Regular optimization reviews:
Quarterly assessment of SKU appropriateness
Adjustment based on changing usage patterns
Evaluation of new SKU options as they become available
Infracost’s policy scanning capabilities can help you continuously monitor your infrastructure code for compliance with this policy, identifying opportunities for optimization even as your deployment grows and evolves. The free trial allows you to scan your existing codebase and identify potential savings opportunities.
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