Azure Virtual Machines offer flexible deployment options, including customizable virtual machine sizes and multiple pricing tiers.
As a consumer, you have multple classes to choose your VM workload from. Lets take a closer look at the different Azure VM classes available and how they can be used to meet specific needs.
- General-purpose VMs
General-purpose VMs are designed for a wide range of workloads and applications. These VMs offer a balance of CPU, memory, and temporary storage, making them ideal for applications that require moderate-to-high performance, such as web servers, small databases, and development and testing environments.
- Compute-optimized VMs
Compute-optimized VMs are designed for compute-intensive workloads such as high-performance computing, gaming applications and batch processing. These VMs offer a higher ratio of CPU to memory, making them ideal for applications that require high computational performance.
- Memory-optimized VMs
Memory-optimized VMs are designed for memory-intensive workloads that require a large amount of RAM, such as in-memory databases, real-time analytics, and data modeling. These VMs offer a higher ratio of memory to CPU.
- Storage-optimized VMs
Storage-optimized VMs are designed for workloads that require a large amount of disk throughput and input/output operations per second (IOPS), such as big data and data warehousing applications. These VMs offer high disk throughput and IOPS, making them ideal for applications that require a lot of data storage and processing power.
- GPU-optimized VMs
GPU-optimized VMs are designed for workloads that require graphical processing power, such as gaming, rendering, and machine learning applications. These VMs offer high-performance graphics processing units (GPUs), making them ideal for applications that require advanced graphics processing capabilities.
These are further divide into different labels based on the type of hardware. Choosing the right VM class for your application, you can ensure optimal performance, scalability, and cost-effectiveness in the cloud.
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