DTU vs vCore pricing models in Azure - Performance Comparison
We want to understand how the cost and performance scale when using DTU or vCore models in Azure SQL Database.
How does the number of resources or operations grow as workload increases?
Analyze the scaling of resource allocation in DTU and vCore models.
// DTU model: fixed bundles of CPU, memory, and IO
// vCore model: separate CPU cores and memory units
// Example:
// DTU: 100 DTUs for a database
// vCore: 4 vCores with 16 GB RAM
// Workload increases from 10 to 1000 requests per second
// Observe how resource units scale
This shows how resource units grow with workload in each pricing model.
Look at what resources or operations increase as workload grows.
- Primary operation: Allocation of DTUs or vCores to handle workload.
- How many times: Resource units increase as workload increases.
As workload (requests per second) grows, DTU bundles increase in fixed steps, while vCore resources scale more linearly.
| Input Size (requests/sec) | DTU Units | vCore Units |
|---|---|---|
| 10 | 10 DTUs (minimum bundle) | 1 vCore |
| 100 | 50 DTUs | 2 vCores |
| 1000 | 400 DTUs | 8 vCores |
DTU grows in fixed bundles, vCore grows more smoothly with workload.
Time Complexity: O(n)
This means resource needs grow roughly in direct proportion to workload size.
[X] Wrong: "DTU and vCore models scale the same way with workload."
[OK] Correct: DTU bundles scale in fixed steps combining CPU, memory, and IO, while vCore lets you scale CPU and memory independently, leading to different growth patterns.
Understanding how resource allocation scales with workload helps you design cost-effective and performant cloud databases.
"What if we changed from a DTU model to a vCore model with serverless compute? How would the time complexity of resource scaling change?"