This term is happend in Amazon EC2 service.
EC2 instance don’t just come in different types. Each type comes in different sizes, with different allotments of vCPUs and memory. Choosing the right size is critical to using your instances efficiently.
An instance’s full type consists of its family name, followed by the generation number, and then the size. So an
m5.xlarge is an m-type instance from the 5th generation of m-type instances, in the extra-large size deployment.
Use cases: websites, web app, built servers, code repos, microservices, test, staging, dev env, business apps
Use cases: Small and mid size db, data processing tasks that require additional memory, caching fleets, backend SAP servers, Microsoft Sharepoint, cluster computing, other enterprise apps
Use cases: High performance web servers, high performance computing, scientific modeling, batch processing, distributed analytics, maching / deep learning inference, ad serving, highly scalable multiplayer gaming, video encoding.
Use cases: In-memory databases (SAP HANA), big data processing engines (Apache Spark or Presto)
Use cases: High performance DB, data mining and analytics, in-memory db, distributed web scale in-memory caches, application performing real-time processing of unstructured big data, Hadoop/Spark clusters, and other enterprise data apps.
Use cases: machine learning, deep learning, high performance computing, computational fluid dyanmics, computational finance, seismic analytics, speech recognition, automomous vehicles, pharmaceutical discovery
Use cases: Genomics research, financial analytics, real-time video processing, big data search and analysis, and security analytics
Use cases: Amazon EMR-based workloads, distributed file system such as HDFS and MapR-FS, network file systems, log or data processing applications such as Apache Kafka, big data workload clusters.
Use cases: NoSQL DB, in-memory db (eg: Aerospke), scale out transactional db, data warehousing, Elasticsearch, and analytics workloads
Use cases: Massively parallel processing (MPP) data warehousing, MapReduce and Hadoop distributed computing, distributed file systems, network file systems, log or data processing apps
Use cases: 3D visualizations, graphics intensive remote workstations, 3D rendering, application streaming, video encoding, other server side graphics workloads