Amazon EC2 provides a wide range of instance types giving you an opportunity to choose the right one for your workload. Knowing them gives you the flexibility to choose the appropriate one for your applications.
When you launch an AWS EC2 instance, the instance type that you specify determines the hardware of the host computer used for your instance. Each instance type offers different compute, memory, and storage capabilities and are grouped in instance families based on these capabilities. Select an instance type based on the requirements of the application that you plan to run on your instance.
Amazon EC2 Instance Types:
General purpose instances provide a balance of compute, memory and networking resources and are ideal for applications that use these resources in equal proportions.
Use Cases:
Scale-out workloads such as web servers, containerized microservices, caching fleets, and distributed data stores, as well as development environments.
Compute Optimized instances are ideal for compute bound applications that benefit from high performance processors.
Use Cases:
High performance computing (HPC), batch processing, ad serving, video encoding, gaming, scientific modelling, distributed analytics, and CPU-based machine learning inference.
Memory optimized instances are designed to deliver fast performance for workloads that process large data sets in memory.
Use Cases: Memory-intensive applications such as open-source databases, in-memory caches, and real time big data analytics
Accelerated computing instances use hardware accelerators, or co-processors, to perform functions, such as floating-point number calculations, graphics processing, or data pattern matching, more efficiently than is possible in software running on CPUs.
Use Cases:
Machine/Deep learning, high performance computing, computational fluid dynamics, computational finance, seismic analysis, speech recognition, autonomous vehicles, drug discovery.
Storage optimized instances are designed for workloads that require high, sequential read and write access to very large data sets on local storage. They are optimized to deliver tens of thousands of low-latency, random I/O operations per second (IOPS) to applications.
Use Cases:
NoSQL databases (e.g. Cassandra, MongoDB, Redis), in-memory databases (e.g. Aerospike), scale-out transactional databases, data warehousing, Elasticsearch, analytics workloads.
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