A new EC2 instance powered by Graviton2 processors, a fintech offering and a service making it easier to run containers on the AWS cloud are among the top Amazon Web Services releases standing out this year.

The Amazon EC2 X2gd Instances are next-generation, memory-optimized instances. Amazon FinSpace is the fintech play, and AWS App runner is the fully managed container application service.

Other AWS products and services that have become generally available in the last approximately six months include a cost anomaly detection tool and a new machine learning-powered service that makes it easier to improve applications’ operational performance and availability.

Here’s a closer look at those offerings and several others that CRN has pegged as 10 of the coolest new AWS tools of 2021 so far.

Amazon EC2 X2gd Instances

Amazon Elastic Cloud Compute (Amazon EC2) X2gd instances are AWS’ next generation of memory-optimized instances powered by AWS-designed, Arm-based Graviton2 processors and built on the AWS Nitro System.

Announced in March, the EC2 X2gd instances deliver up to 55 percent better price performance for memory-intensive workloads than current-generation x86-based X1 instances, according to the cloud provider, and offer the lowest cost per GiB of memory in Amazon EC2. They also offer increased memory per vCPU compared to AWS’ other Graviton2-based instances, including twice as much memory per vCPU as the memory-optimized R6g instances.

The EC2 X2gd instances support memory-intensive workloads including in-memory databases such as Redis and Memcached, relational databases such as MySQL and PostGreSQL, data warehousing applications such as Amazon Redshift and electronic design automation, according to AWS. Customers also can bundle more memory-intensive containerized applications on a single instance to lower their total cost of ownership.

The new EC2 instances are available in eight sizes and in bare metal form

AWS Cost Anomaly Detection

AWS Cost Anomaly Detection provides automated cost anomaly detection and root cause analysis to help customers monitor their AWS cloud spending and reduce the risk of billing surprises. It uses a multi-layered machine learning model that learns customers’ unique, historic AWS spending patterns to detect one-time cost spikes and/or continuous cost increases. Customers don’t have to define anomaly thresholds, as AWS’ learning models automatically will determine them. They can view every anomaly detected in a detection history tab, and AWS sends anomaly detection reports with root-cause analysis that includes the account ID, the service that’s responsible for the anomaly, the severity, duration, etc.

Customers can create their own contextualized cost monitors to define the spend segments they want to evaluate and receive alerts with just three simple steps, according to AWS.The four types of cost monitors are individual AWS services, linked/member accounts, cost allocation tags and cost categories. Customers can customize the alert threshold and frequency, along with alert recipients.

As customers evaluate the anomalies detected, they can submit assessments that will further train the machine learning models so they are tailored to their specific spend patterns.