1. Introduction
Hello! We are a writer team from Definer Inc.
In this issue, you are wondering about the difference and usage between serverless and containers.
Let's take a look at the actual screens and resources to explain in detail.
In this issue, you are wondering about the difference and usage between serverless and containers.
Let's take a look at the actual screens and resources to explain in detail.
2. Purpose/Use Cases
This article provides a collection of information and practices that you may find helpful when you want to understand the differences and use cases between serverless and containers.
3. Difference between serverless and containers
First, we will discuss serverless and containers, respectively.
・Serverless
To run a program, a server (computer) is needed in addition to the code.
The server must be configured with various settings, including the operating system, its version, and middleware for executing the program.
In the past, building such a server was essential as a preliminary step to building an application.
The cloud service provider takes care of the server construction part, and the user only needs to prepare the code to execute the program. This concept is called serverless.
It is called serverless because the user does not need to prepare a server.
In many cases, the service is metered, so it is cost-effective when server resources are not used except for specific periods of time, such as for batch processing.
Typical services include AWS Lambda and Azure Functions.
・Containers
Containers are a technology that isolates the resources used from other processes and runs applications within them.
As a result, applications on containers appear to be running on independent computers.
This makes it easy to reproduce the same environment on different servers, even if they have different OS types and versions.
Therefore, moving applications from one host to another is also easier than with applications running on conventional servers.
Typical services include AWS ECS and Google Kubernetes Engine (GKE).
Both are more efficient than regular IaaS (Virtual Machine), and the applications are isolated from the host.
・Serverless
To run a program, a server (computer) is needed in addition to the code.
The server must be configured with various settings, including the operating system, its version, and middleware for executing the program.
In the past, building such a server was essential as a preliminary step to building an application.
The cloud service provider takes care of the server construction part, and the user only needs to prepare the code to execute the program. This concept is called serverless.
It is called serverless because the user does not need to prepare a server.
In many cases, the service is metered, so it is cost-effective when server resources are not used except for specific periods of time, such as for batch processing.
Typical services include AWS Lambda and Azure Functions.
・Containers
Containers are a technology that isolates the resources used from other processes and runs applications within them.
As a result, applications on containers appear to be running on independent computers.
This makes it easy to reproduce the same environment on different servers, even if they have different OS types and versions.
Therefore, moving applications from one host to another is also easier than with applications running on conventional servers.
Typical services include AWS ECS and Google Kubernetes Engine (GKE).
Both are more efficient than regular IaaS (Virtual Machine), and the applications are isolated from the host.
4. Use of serverless and containers
The following perspectives can be used to determine the difference between serverless and container use.
(1) Execution time/resource size
If the execution time is long or the required size of resources (CPU/Memory) is large, containers are suitable.
For example, AWS Lambda, a well-known serverless service, has a limit of 15 minutes for execution and 10G of memory.
(2) System management unit
If the management unit of the system is each application (each code), a serverless service is suitable, and if the system is a whole platform, containers are suitable.
(3) Host
Containers can run on-premise or in the cloud, and have a wide range of supported operating systems.
Serverless runs on a specific platform in the cloud.
(4) Language
For containers, applications can be created in basically any language.
In the case of serverless, it must be written in the language specified by the cloud provider.
(5) Is it stateless?
Serverless is stateless, so containers should be considered for stateful requirements.
(1) Execution time/resource size
If the execution time is long or the required size of resources (CPU/Memory) is large, containers are suitable.
For example, AWS Lambda, a well-known serverless service, has a limit of 15 minutes for execution and 10G of memory.
(2) System management unit
If the management unit of the system is each application (each code), a serverless service is suitable, and if the system is a whole platform, containers are suitable.
(3) Host
Containers can run on-premise or in the cloud, and have a wide range of supported operating systems.
Serverless runs on a specific platform in the cloud.
(4) Language
For containers, applications can be created in basically any language.
In the case of serverless, it must be written in the language specified by the cloud provider.
(5) Is it stateless?
Serverless is stateless, so containers should be considered for stateful requirements.
5. Cited/Referenced Articles
6. About the proprietary solution "PrismScaler"
・PrismScaler is a web service that enables the construction of multi-cloud infrastructures such as AWS, Azure, and GCP in just three steps, without requiring development and operation.
・PrismScaler is a web service that enables multi-cloud infrastructure construction such as AWS, Azure, GCP, etc. in just 3 steps without development and operation.
・The solution is designed for a wide range of usage scenarios such as cloud infrastructure construction/cloud migration, cloud maintenance and operation, and cost optimization, and can easily realize more than several hundred high-quality general-purpose cloud infrastructures by appropriately combining IaaS and PaaS.
・PrismScaler is a web service that enables multi-cloud infrastructure construction such as AWS, Azure, GCP, etc. in just 3 steps without development and operation.
・The solution is designed for a wide range of usage scenarios such as cloud infrastructure construction/cloud migration, cloud maintenance and operation, and cost optimization, and can easily realize more than several hundred high-quality general-purpose cloud infrastructures by appropriately combining IaaS and PaaS.
7. Contact us
This article provides useful introductory information free of charge. For consultation and inquiries, please contact "Definer Inc".
8. Regarding Definer
・Definer Inc. provides one-stop solutions from upstream to downstream of IT.
・We are committed to providing integrated support for advanced IT technologies such as AI and cloud IT infrastructure, from consulting to requirement definition/design development/implementation, and maintenance and operation.
・We are committed to providing integrated support for advanced IT technologies such as AI and cloud IT infrastructure, from consulting to requirement definition, design development, implementation, maintenance, and operation.
・PrismScaler is a high-quality, rapid, "auto-configuration," "auto-monitoring," "problem detection," and "configuration visualization" for multi-cloud/IT infrastructure such as AWS, Azure, and GCP.
・We are committed to providing integrated support for advanced IT technologies such as AI and cloud IT infrastructure, from consulting to requirement definition/design development/implementation, and maintenance and operation.
・We are committed to providing integrated support for advanced IT technologies such as AI and cloud IT infrastructure, from consulting to requirement definition, design development, implementation, maintenance, and operation.
・PrismScaler is a high-quality, rapid, "auto-configuration," "auto-monitoring," "problem detection," and "configuration visualization" for multi-cloud/IT infrastructure such as AWS, Azure, and GCP.