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Monitor and Analyze Usage Patterns

Learn how to track and analyze LLM consumption patterns and associated costs to optimize your AI investments.

Overview​

Effective monitoring is the foundation of LLM governance. Tetrate Agent Operations Director provides comprehensive analytics tools that help organizations understand their usage patterns, identify optimization opportunities, and maintain control over AI spending.

Dashboard Navigation​

The Agent Operations Director offers multiple specialized dashboards:

Main Dashboard​

The central dashboard provides a high-level overview of your LLM ecosystem:

  • Resource Status: Count of resources by state
  • Consumer Status: Count of consumers by state
  • Budget Status: Count of budgets by state

Usage Analysis Dashboard​

For detailed consumption patterns across your organization:

  • Provider/Resource Heatmap: Usage distribution across providers and resources
  • Consumer Usage: Detailed breakdown by consumer application
  • Time-Series Analysis: Usage trends over selected time periods
  • Token Metrics: Input and output token consumption
  • Cost Analysis: Financial implications of current usage

Consumer Analysis Dashboard​

Focused on application-specific consumption:

  • Consumer Status Chart: Visual breakdown of consumer states
  • Usage by Consumer: Detailed metrics for specific applications
  • Comparative Analysis: Benchmark consumers against each other
  • Prompt Analysis: Insights into prompt efficiency and optimization

Resource Analysis Dashboard​

Centered on model and provider utilization:

  • Resource Status Chart: Visual breakdown of resource states
  • Resource Consumption: Usage metrics for specific models
  • Provider Distribution: Usage across different LLM providers
  • Efficiency Metrics: Tokens per request and cost per token

Budget Analysis Dashboard​

Focused on financial and quota governance:

  • Budget Status Chart: Visual breakdown of budget states
  • Consumption vs. Quota: Current usage against defined limits
  • Forecast Analysis: Projected usage based on current trends
  • Alert History: Record of threshold crossings and actions

Analyzing Key Metrics​

When monitoring LLM usage, focus on these critical metrics:

Volume Metrics​

  • Total Tokens: Overall consumption volume
  • Requests Count: Number of API calls made
  • Tokens per Request: Efficiency of prompts and responses

Financial Metrics​

  • Total Cost: Actual spending for the period
  • Cost per Token: Efficiency of resource selection
  • Projected Spending: Forecast based on current trends

Operational Metrics​

  • Success Rate: Percentage of successful requests
  • Latency: Response time from providers
  • Error Distribution: Types and frequency of failures

Analysis Patterns to Look For​

Cost Optimization Opportunities​

  • High Token Count: Consumers using excessive tokens per request
  • Expensive Models: Usage of premium models for simple tasks
  • Inefficient Prompts: Patterns indicating poor prompt engineering

Governance Concerns​

  • Budget Approaching Limits: Consumers nearing their quota
  • Rapid Growth: Sudden increases in consumption
  • Unusual Patterns: Anomalous usage that might indicate issues

Business Intelligence​

  • Usage Concentration: Which teams rely most heavily on LLMs
  • Value Generation: Correlating usage with business outcomes
  • Adoption Trends: Growth patterns across the organization