AWS Lambda and Amazon EC2 Auto Scaling compete in the cloud computing space, providing scalable solutions for running applications. AWS Lambda appears to have an edge with its serverless architecture and cost-efficiency for variable workloads, while Amazon EC2 Auto Scaling excels in scenarios requiring persistent transactions and high availability.
Features: AWS Lambda offers a serverless architecture with automatic scaling, event-driven execution, and seamless integration with AWS services. It supports various programming languages and utilizes a pay-as-you-go pricing model, making it cost-efficient and easy to maintain. Amazon EC2 Auto Scaling provides dynamic control over server instances with features like load balancing and auto-scaling based on demand, ensuring high availability and ideal performance for applications needing persistent transactions.
Room for Improvement: AWS Lambda could improve in cold-start delays, expanding programming language support, and better integration with external services. Enhancements in security and troubleshooting would also be beneficial. Amazon EC2 Auto Scaling could improve pricing transparency, documentation, and security features, as well as offering better support for instances that do not start as expected.
Ease of Deployment and Customer Service: AWS Lambda is noted for its ease of deployment on public clouds and minimal infrastructure needs, with efficient scaling and comprehensive documentation. Users sometimes find the initial setup complex without detailed guides. Amazon EC2 Auto Scaling is praised for its integration in hybrid cloud environments. Both products are supported by Amazon's customer service, with Lambda receiving slightly more positive feedback.
Pricing and ROI: AWS Lambda offers a cost-effective pay-as-you-go pricing model, beneficial for applications with variable activity levels, resulting in positive ROI due to low operational costs. While Amazon EC2 Auto Scaling also follows a pay-as-you-go model, it may incur higher costs if instances are not managed effectively, though it remains cost-efficient for workloads needing constant availability, with a positive ROI through efficient resource management.
I would rate how scalable AWS Lambda is a nine on a scale from 1 to 10, where 1 would be the lowest and 10 would be the highest level of scalability.
Amazon should provide more detailed training materials for people who are just starting to work with Amazon EC2 Auto Scaling.
It operates on a pay-as-you-go model, meaning if a machine is used for only an hour, the pricing will be calculated for that hour only, not the entire month.
Amazon EC2 Auto Scaling has the capability to multiply itself, which enables it to handle peak loads effectively.
Amazon EC2 Auto Scaling helps you maintain application availability and allows you to automatically add or remove EC2 instances according to conditions you define. ... Dynamic scaling responds to changing demand and predictive scaling automatically schedules the right number of EC2 instances based on predicted demand.
AWS Lambda is a compute service that lets you run code without provisioning or managing servers. AWS Lambda executes your code only when needed and scales automatically, from a few requests per day to thousands per second. You pay only for the compute time you consume - there is no charge when your code is not running. With AWS Lambda, you can run code for virtually any type of application or backend service - all with zero administration. AWS Lambda runs your code on a high-availability compute infrastructure and performs all of the administration of the compute resources, including server and operating system maintenance, capacity provisioning and automatic scaling, code monitoring and logging. All you need to do is supply your code in one of the languages that AWS Lambda supports (currently Node.js, Java, C# and Python).
You can use AWS Lambda to run your code in response to events, such as changes to data in an Amazon S3 bucket or an Amazon DynamoDB table; to run your code in response to HTTP requests using Amazon API Gateway; or invoke your code using API calls made using AWS SDKs. With these capabilities, you can use Lambda to easily build data processing triggers for AWS services like Amazon S3 and Amazon DynamoDB process streaming data stored in Amazon Kinesis, or create your own back end that operates at AWS scale, performance, and security.
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