Unleash the power of innovation with AWS and Serverless technology, the future of scalable, cost-effective, and efficient computing.
Amazon, one of the world's largest companies, began as a humble online bookstore and has now evolved into a marketplace where you can purchase virtually anything. What many people don’t know is Amazon actually generates more revenue from their server hosting business, Amazon Web Services (AWS). Making it the world's largest web hosting company with over 125 data centers across 34 regions.
AWS provides a range of services from traditional virtual hosting (EC2) to modern serverless architectures. Nexoid utilizes AWS's serverless services, ensuring not just high performance but also high-security. AWS customers include major corporations, banks, governments, and even the military, which means they take security seriously. Even we don't have access to the servers.
Serverless technology, also known as microservices, is an innovative architecture. Traditional architecture usually means a web application server connected to a database server, with the webserver remaining idle most of the time (wasting money). Traditional architectures also fail under sudden influxes of activity, resulting in bottlenecks and timeouts.
In contrast, serverless technology breaks code into small pieces handled by a huge pool of machines, or 'lambdas' in AWS. These servers wait for requests, which could come from an external end user, an internal robot in an Amazon warehouse, or any number of other AWS services. When a request reaches a lambda, the lambda runs the small snippet of code to handle the request and then stops.
For example, if a million people logged into Nexoid simultaneously, a million lambdas would activate, process the request, and shut down within milliseconds. This is why Nexoid is always fast and ready. This is not true of most ERP providers.
Serverless technology addresses both the bottleneck issue and the wasted resource problem found in traditional architectures. We only pay for the actual processing time the lambdas use, making it hundreds of times more cost-effective.