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

  1. 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.
  2. 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
  3. 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
  4. 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
  5. 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:

  1. Implement usage dashboards tracking:
    • Token consumption by deployment
    • Request patterns and peak usage
    • Cost per model version and deployment
  2. Set up alerting for:
    • Sustained high utilization (>80%)
    • Extended periods of low utilization (<20%)
    • Cost anomalies or sudden changes in usage patterns
  3. 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.

Frequently Asked Questions (FAQs)

Standard SKUs use a pay-per-token model where you’re charged based on actual usage, making them ideal for variable workloads. ProvisionedManaged SKUs provide dedicated capacity at a fixed hourly rate, which is cost-effective for high-volume, consistent workloads. The break-even point typically occurs when you’re processing tokens at 70-80% of the provisioned capacity consistently.

Consider three key factors: compliance requirements (data residency), cost differences between regions (which can vary by 10-20%), and proximity to your application users or services to minimize latency. Some models may only be available in specific regions, which could constrain your options.

Yes, changing between Standard and ProvisionedManaged SKUs typically requires redeployment. This may involve downtime and reconfiguration of application connections. Plan these transitions during maintenance windows and ensure you have proper connection string management in place.

For established workloads with stable patterns, quarterly reviews are recommended. For newer applications or those with evolving usage patterns, monthly reviews are advisable. Additionally, review whenever there are significant changes to your application functionality that might impact AI usage patterns.

Yes, hybrid approaches can be effective. For example, you might use a smaller ProvisionedManaged deployment for baseline traffic and supplement with Standard SKU deployments for handling peak loads. This approach can optimize costs while maintaining performance for variable workloads.

Newer model versions often offer better performance and cost efficiency than older versions. They may process tokens more effectively, requiring less computational resources for equivalent outputs. Always evaluate the latest model versions as part of your regular SKU optimization process.

Azure Monitor, Azure Cost Management, and Application Insights can help track usage patterns. Infracost can identify potential savings in your infrastructure code. Custom logging within your application can provide more detailed insights into token consumption patterns by feature or user segment.

Azure enforces quota limits per subscription, region, and deployment type. Ensure your planned usage stays within these limits or request increases in advance. ProvisionedManaged SKUs typically offer higher quota ceilings than Standard SKUs, which may influence your selection for high-volume applications.