AWS Features to look forward to in 2021

A few new exciting features for Compute, Storage, Database etc. on AWS

This article elucidates some of the new features made available recently / coming soon to AWS. Each feature is categorized under the product type — so let’s get started!


You can now use your existing container development workflows to build lambda applications by packaging and deploying Lambda functions as container images of up to 10 GB in size. This features allows you to use the same set of tools for containers and lambda based applications. Just like functions packaged as ZIP archives, functions deployed as container images benefit from the same operational simplicity, automatic scaling, high availability and native integrations with many services.

Image for post
Image for post

AWS is providing base images for all the supported Lambda runtimes (Python, Node.js, Java, .NET, Go, Ruby) so that you can easily add your code and dependencies. You can however also deploy lambda functions built on top of third-party base container images.

On top of all this, AWS is offering 1ms billing instead of 100ms billing which can give you upto 70% cost-savings based on your workload.

Image for post
Image for post
C6gn and T4g joining the Graviton Family

AWS Graviton processors add more choice to optimize performance and cost for the workloads. They are are custom built by AWS using 64-bit ARM Neoverse cores and are supported by popular Linux operating systems including Amazon Linux 2, Red Hat, SUSE, and Ubuntu. The A1 instances using first-generation AWS Graviton processors delivered significant cost savings over other general-purpose instances for scale-out applications such as web servers, containerized microservices, data/log processing etc.

The latest generation of Graviton processors (Graviton2) deliver a major leap in performance and capabilities over first-generation AWS Graviton processors. The instances using Graviton2 provide up to 40% better price performance over comparable current generation x86-based instances for a wide variety of workloads, including application servers, microservices, high-performance computing, electronic design automation, gaming, open-source databases, and in-memory caches. They deliver 7x more performance, 4x more compute cores, 5x faster memory, and 2x larger caches.

R6g, M6g, C6g instances were announced earlier this earlier, with T4g available now on free trial while C6gn to be launced soon.

Free Trial: Until March 31st 2021, all new and existing AWS customers can try the t4g.micro instances free for up to 750 hours per month. During the free-trial period, customers who run a t4g.micro instance will automatically get 750 free hours per month deducted from their monthly bill through March 2021.

  • Largest local storage instance with D3n (336 TB)
  • On-demand MacOS Instances
  • Best price-performance for graphics-intensive workloads with G4ad
  • ECS and EKS in own data center!


The new version of Aurora Serverless helps you scale much faster than ever before — hundreds of thousands of transactions in a fraction of a second, delivering up to 90% cost savings compared to provisioning for peak capacity. The speed of scale has been a pain point with many customers. Thus instead of doubling capacity every time a workload needs to scale, Aurora Serverless v2 now adjusts capacity in fine-grained increments to provide just the right amount of database resources for an application’s needs.

Babelfish for Amazon Aurora helps you to easily run SQL Server applications on Aurora PostgreSQL. It is a new translation layer for Amazon Aurora that enables Aurora to understand queries from applications written for Microsoft SQL Server. With Babelfish, applications currently running on SQL Server can now run directly on Aurora PostgreSQL with little to no code changes. Babelfish understands the SQL Server wire-protocol and T-SQL, so you don’t have to switch database drivers or re-write all of your application queries.

All you have to do is migrate your data to Aurora PostgreSQL with DMS, then update you application configuration to point to Aurora and thus save yourself SQL Server Licensing costs!

Management and Governance

Image for post
Image for post

AWS Proton provides automated management for container and serverless deployments.

Advantages for the Infrastructure/Platform team — It enables the platform team to define standard templates centrally and make them available for developers in their organization. This allows infrastructure teams to manage and update infrastructure without impacting developer productivity. Platform team can coordinate all the different tools needed for infrastructure provisioning, code deployments, monitoring, and updates.

Advantages for the developer team — Devlopers can now deploy applications using approved stacks. Using approved stacks, authorized developers in your organization are able to use Proton to create and deploy a new production infrastructure service for their container and serverless applications.

AWS Proton also provides curated templates that follow AWS best practices for common use cases such as web services running on AWS Fargate or stream processing apps built on AWS Lambda.


io2 Block Express is similar to a SAN solution. It is designed for workloads which require very high level of IOPS (~200k+). It is built on the new EBS Block Express architecture that takes advantage of some advanced communication protocols implemented as part of the AWS Nitro System, the volumes will give you up to 256K IOPS & 4000 MBps of throughput and a maximum volume size of 64 TiB, all with sub-millisecond, low-variance I/O latency.

Throughput scales proportionally at 0.256 MB/second per provisioned IOPS, up to a maximum of 4000 MBps per volume. You can provision 1000 IOPS per GiB of storage, twice as many as before. The increased volume size & higher throughput means that you will no longer need to stripe multiple EBS volumes together, reducing complexity and management overhead.

More SAN features like multi-attach (attach to multiple EC2 instances), Elastic Volumes ( increase volume size, adjust performance, or change the volume type while the volume is in use), I/O Fencing (consistent access for multi-attach volume) and Fast snapshot restore are on the way.

Machine Learning

There are two new offerings to lower the deep learning model training cost.

AWS will be introducing special EC2 instances built specifically for training deep learning powered by the Habana Gaudi processors from Intel. While Habana Gaudi chips may not be as performant as Nvidia A100 chips, they offer a much better price-performance ratio.

They will provide up to 40 percent better price-performance than similar cloud instances running Nvidia GPUs for training deep learning models — leveraging up to 8 Gaudi accelerators. The accelerators are specifically designed for deep learning training workloads such as natural language processing, object detection and classification, recommendation and personalization.

They will have support for Tensorflow and PyTorch.

AWS Trainium is a Machine Learning Training chip custom designed by AWS to deliver the most cost effective training in the cloud. Trainium offers the highest performance with the most teraflops — one teraflop is one trillion (10¹²) floating-point operations per second — of compute power for ML in the cloud. It will also be optimized for deep learning training workloads — just like Habana Gaudi based instances, thus acting as a direct competitor.

AWS Trainium shares the same AWS Neuron SDK as AWS Inferentia making it easy for developers using Inferentia to get started with Trainium. Because the Neuron SDK is integrated with popular ML frameworks including TensorFlow, PyTorch, and MXNet developers can easily migrate to AWS Trainium from GPU-based instances with minimal code changes. AWS Trainium will be available via Amazon EC2 instances and AWS Deep Learning AMIs as well as managed services including Amazon SageMaker, Amazon ECS, EKS, and AWS Batch.

Other honorable mentions

  • More metro cities available for AWS Local Zones
  • More countries like Japan, South Korea, England to get support for AWS Wavelength
  • New Industrial Machine Learning services such as Amazon Monitron, Amazon Lookout for Equipment, the AWS Panorama Appliance, the AWS Panorama SDK, and Amazon Lookout for Vision.

A Certified Multi-Cloud Architect/Big Data/ML Specialist and Quantum Computing Enthusiast

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store