AWS Cost Explorer? Expect More From Your Cost Management Tools

AWS provides a free cost tool that can visualize the AWS cost and usage data (CUR), that is Cost Explorer. The CUR file has details on every AWS resource and is exposed to any third party tool as an API. So given that AWS already comes with a tool for cost visibility, why do you need to spend more on another tool for cost management.

AWS Trusted Advisor is available as part of a paid support plan (3-10% of your AWS spend) and provides recommendations on cost optimization. Trusted Advisor does a lot more that Cost Explorer also, it also checks your environment against best practices for performance, security and fault tolerance.

With these two tools available to you from AWS, why should you use another third-party cost management tool ?

Read our blog to get a quick refresher on the basics of AWS billing. AWS has levelled the playing field in the Cost and Billing arena by giving all third parties access to the same data that it uses for analysis via the Cost and Usage Report (CUR). So another question that follows the previous question is

Why should I consider HyperCloud Analytics for cost management and optimization ?

As I go through the key functionality that native-AWS tools provide, I will outline why you should opt for HyperCloud.

EC2 On-Demand Instance Costs

Both AWS Cost Explorer and HyperCloud Analytics allow you to visualize your bill by service.

Cost Explorer (on the left) shows you the spend, but to drill any deeper, you need to use Trusted Advisor. Our users gave us feedback that since EC2 is (still) the lion’s share of their AWS bill, EC2-drilldown very important to them and we have elevated this to the front page (on the right).

So let me give you two reasons why HyperCloud should be your choice –

Reason 1: Detailed Cost and Usage By Instance. We go beyond just a snapshot of your usage, and allow you, in one click, to view detailed usage and cost of all instances. Filtering capabilities are available to be able to drill down to a specific deployment, creator or region.

Reason 2: Continuous Instance Optimization. It is important to stay ahead of the pricing curve by continuously re-analyzing your pricing and looking for opportunities for optimization. By regularly monitoring both your cloud spend and your performance data, HyperCloud Analytics can find opportunities for optimization. Continuous change also creates interesting arbitrage opportunities that can result in unexpected savings – different instance types, different payment terms even different clouds can all be utilized to optimize costs.

In HyperCloud Analytics, we regularly benchmark over 400 million pricing combinations of instances and services across different clouds. HyperCloud looks at resource utilization and recommends optimization options, including:

  • Deploying a different instance type (e.g. t3.large instead of t2.large)
  • Changing Payment Terms (e.g. PAYG to All Up Front)
  • Deploying in a different cloud altogether (e.g. Azure centralindia instead of AWS ap-south-1)

EC2 – Reserved Instance Optimization

Reservation allows you to get pricing breaks on AWS instances without making any changes to your deployment. Intelligently purchasing Reserved Instances (RI) will have a big impact on your EC2 spend; but, managing Reserved Instances and ensuring that they are being applied towards utilization is equally important.

Both AWS Trusted Advisor and HyperCloud Analytics provide analysis for RI optimization: finding the best candidates to convert to RIs.  The screenshots below show the RI recommendations view in Trusted Advisor as well as HyperCloud Analytics.

Here are the two reasons why HyperCloud should be your choice –

Reason 1: Reserved Instance Wastage

An unused RI is a double whammy. You spend money on a reservation that isn’t used. Then you probably spend money to get an instance to take its place. An unused RI is sunk cost that needs to be recovered ASAP.

By giving you a quick view of RI wastage, HyperCloud Analytics enables you to either:

  1. Sell the unused portion of a Standard RI on the AWS RI Marketplace
  2. Trade-in convertible RIs for one that matches your needs

Reason 2: Current Reserved Instance Savings

A huge part of the cloud operations is reporting. Report to your CXOs or VPs about your cloud spend, ROI and so on. HyperCloud automatically calculates savings from RIs so that you can easily calculate your ROI from RIs. This data can easily be imported into a legacy ITFM tools.

Lambda Optimization

Over time, we are starting to see a trend of compute spend moving from EC2 to Lambda; to help organizations prepare for a production-grade Lambda rollout, Lambda Cost Optimization is an integral part of HyperCloud Analytics.

This is one of the biggest gaps as of now in the AWS-native tools. Here are three reasons why HyperCloud is ahead of all the others in the market –

Reason 1: Lambda Spend Visualization

HyperCloud Analytics provides a consolidated dashboard of all Lambda functions and their costs in near-real time, broken down by their various components – memory, runtime, invocations etc You can visually understand and map your Lambda usage (stay tuned to our future blogs for more on this capability)

Reason 2: Lambda Cost Optimization

Since Lambda is paid for in terms of resource utilization over runtime (GB-seconds), any resource overallocation is a sunk cost, the function doesn’t use it, but you are getting charged for it.

By giving the Ops team the ability to identify functions that have overallocated resources, HyperCloud creates opportunities for cost optimization in real-time.  When coupled with the spend visualization dashboard, it is possible to see the effects of cost-optimization in near real-time and make incremental adjustments till the right tradeoff is reached.

Reason 3: Lambda Waste Reduction

Lambda is charged by the invocation, so each failed invocation is also money wasted.  By keeping an eye on wasted invocations i.e. functions with a high error rate, it is possible to adjust the resource allocation (or the function code) and reduce the number of wasted invocations.

By focusing on the high error rate functions, and providing the capability to drill down and understand resource constraints in these functions, HyperCloud Analytics allows the DevOps team to fully own the serverless environment and understand the cost implications of their decisions.

To Summarize..

Here is what is a 1:1 mapping in terms of functionality that does not come with the native-AWS tools

Use Case AWS Support HyperCloud Analytics Support
Per-Service Breakdown of Bill Cost Explorer Yes
Detailed Cost and Usage by Instance No Yes
Recommend alternate instances No Yes
Recommend alternate clouds No Yes
Recommend Reserved Instances Trusted Advisor Yes
Show wasted RIs No Yes
Show savings from RIs No Yes
Lambda – Cost Optimization No Yes
Lambda  – Wasted Invocations No Yes

We believe our platform is beneficially different from any other in the market as it simplifies cloud adoption using automation, orchestration and predictive analytics. Experience this for yourself with a 30-day risk-free trial of the HyperCloud by visiting us on the AWS Marketplace.

Badri Venkatachari leads product management and product marketing at HyperGrid. He joined HyperGrid from Microsoft where he managed the StorSimple business, which was a highly successful acquisition that Microsoft made in 2012. His team was responsible for rapidly growing the hybrid cloud storage business and expanding its market reach to more than 60 countries. His responsibilities included product management & marketing, business strategy, GTM, ISV/resale partnerships, and supply-chain planning and fulfillment. Prior to Microsoft, he led product and partner marketing at StorSimple through its acquisition and led post-acquisition integration efforts coordinating across multiple teams. He has also held senior roles in corporate strategic & financial advisory, product management and R&D for distributed systems in Turin Networks, ADC and Novell. Badri has an MBA from Kellogg School of Management. He also holds Masters degrees in Computer Science from Worcester Polytechnic Institute and in Physics from BITS, Pilani, India.

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