Inference & Costs
Understand the journey from request to output, performance trade-offs and budgets.
Open any lesson to see its learning objective. The lesson activities are being prepared.
How Inference Works
From Request to Model Response
Trace the major stages of an AI request.
Input and Output Tokens
Estimate tokens used for prompts and generated content.
Latency, Time to First Token and Total Time
Distinguish startup delay from overall completion time.
Throughput, Concurrency and Queueing
Understand multiple users competing for service capacity.
Performance and Efficiency
Compute and Memory Constraints
Understand why model size and available resources affect speed.
AI Request Cost Models
Calculate illustrative usage-based model costs.
Context Budgets and Truncation
Avoid unnecessarily long inputs while preserving needed information.
Caching and Reusing Results
Explore when saved outputs improve speed and cost.
Choosing and Planning
Model Selection: Quality, Speed and Cost
Choose between models using the requirements of a task.
Batch Processing Versus Real-Time Use
Compare deferred and immediate processing workflows.
Quotas, Limits and Capacity Planning
Recognize rate and volume limits under different usage patterns.
Budget Forecasting and Cost Incidents
Analyze scenarios where usage increases unexpectedly.