Although Amazon dominates the market share of cloud services, there’s been a trend among retailers to choose AWS alternatives. Big-name retailers, in verticals from clothing to electronics, are moving away from maintaining their own data centers in favor of the public cloud’s agility and better access to customers worldwide. To highlight a few:
Gap Inc. signed a five-year contract with Microsoft, choosing Azure as their primary cloud provider. Employees will also be using Microsoft 365 tools and the Enterprise Mobility and Security suite. They chose Azure to support their e-commerce operations, inventory, and workforce systems.
Gap chose Azure among AWS alternatives because they wanted “a partner that is not going to be a competitor […] in any other parts of their businesses,” as told by Shelley Bransten, corporate VP for global retail and consumer goods at Microsoft.
Furthermore, in a move directly targeting Amazon, Walmart has asked their tech vendors to choose AWS alternatives. Wal-Mart spokesman Dan Toporek told CNBC: “Our vendors have the choice of using any cloud provider that meets their needs and their customers’ needs. It shouldn’t be a big surprise that there are cases in which we’d prefer our most sensitive data isn’t sitting on a competitor’s platform.”
Supermarket and retail giant Kroger took a multi-cloud approach, first with Pivotal and Microsoft, and later adding on Google Cloud in 2017. In a CNBC interview, Chris Hjelm, Kroger’s chief information officer, explains why the retailer spends millions of dollars on Microsoft and Google in order to avoid AWS: “For obvious reasons competitively, it doesn’t make sense for us to do a ton to help grow that business for them.”
Target, another retail competitor of Amazon, decided to stop financing its rival in mid-2017 and began dropping down their use of AWS. Microsoft, Google, and Oracle all pushed for their business as discussions were kept quiet, with a Target spokesperson only admitting that they use multiple clouds. Earlier this year, Google CEO Sundar Pichai confirmed Target as a big cloud customer.
And the list goes on…
In addition to the rest, Spotify, eBay, Best Buy, and LL.Bean all turned to Google to meet their cloud needs. One by one, big retailers with recognized names are choosing Microsoft and Google in favor of Amazon.
Why Retailers Choose AWS Alternatives
Cloud migration requires a massive haul of data, costs, and time from a business. Not only is there a lot to consider in terms of pricing, services, and overall offerings, but there are also certain needs unique to a specific industry. Big retailers turning away from AWS and onto other cloud providers highlights an issue for Amazon as a competitor in the retail industry, providing opportunities for other providers like Microsoft and Google to secure enterprise deals.
Meanwhile, not everyone has chosen AWS alternatives. Amazon still holds the market lead and continues to retain a footprint in the retail industry with customers including Nordstrom, Nike, Under Armour, and Lululemon. So while sources suggest that more retailers are looking for other options outside of AWS, time will tell if Amazon can hold its spot among retailers.
Amazon Web Services (AWS) has been pumping out announcements in the lead up to their AWS re:Invent conference next week – which is predicted to exceed 50,000 attendees this year. (See you there?) We’re excited to see what big news the cloud giant has for us next week!
In the meantime, here are three AWS announcements from the last few days that will interest anyone who’s concerned with cloud costs.
Predictive Scaling for EC2
AWS’s new predictive scaling for EC2 is a new and improved way to use Auto Scaling to optimize costs. Typically when you set up an Auto Scaling Group, you need to set scaling policies, such as rules for launching instances based on changes in capacity. Given the complexity of these requirements, some users we’ve talked to forgo them altogether, instead using Auto Scaling simply for instance health checks and replacements.
With predictive scaling for EC2, there is very little the user needs to set up. You will simply set up the group, and machine learning models will analyze daily and weekly scaling patterns to predictively scale. You’ll have choices to optimize for availability, or optimize for cost – making it easy to use Auto Scaling to save money. Of course, sometimes you’ll know better than the machine – for example, development and test instances may require on/off or scale-up/scale-down schedules based on when users need them, which won’t always be consistent. For that, use ParkMyCloud to schedule auto scaling groups to turn off or change scaling when you know they will have little or no utilization.
AWS Cost Explorer Forecasting
AWS has announced an improved forecasting engine for the AWS Cost Explorer. It now breaks down historical data based on charge type – distinguishing between On Demand and Reserved Instance charges – and applies machine learning to predict future spend.
They have extended the prediction range from three months to twelve months, which will certainly be of use for budget forecasting. It’s also accessible via the API – we see this being used to show budget predictions on team dashboards in your office, among other applications.
CloudWatch Automatic Dashboards
The third announcement from this week that we’re looking forward to using ourselves here at ParkMyCloud is the new series of CloudWatch Automatic Dashboards. This will make it remarkably easier to navigate through your CloudWatch metrics and monitor costs and performance, and help potential issues break through the noise.
Now, play around with AWS’s new predictive scaling for EC2, then take some time to relax.
Happy Thanksgiving! (And to our non-U.S. readers, enjoy your Thursday!)
Cloud cost optimization leader adds new method to address cloud waste to improve cloud users’ efficiency
November 21, 2018 (Dulles, VA) – ParkMyCloud, provider of the leading enterprise platform for continuous cost control in public cloud, announced today that its cost optimization platform has now expanded to provide “rightsizing”. Rightsizing is a way to reduce wasted cloud spend by resizing cloud resources, and can provide significant savings: just by moving a virtual machine down one size tier, 50% or more — and resources are often so overprovisioned that multiple size transitions are possible, raising that savings to 75% or more.
This joins ParkMyCloud’s “parking” functionality, which automatically schedules non-production cloud resources, such as those used for development, testing, staging, and QA, to turn off when they’re not needed. With a typical schedule that parks a resource for 12 hours each night and on weekends, users can save 65% of the cost of their resources. Combined with rightsizing, this means that an average cloud user is poised to reduce overall costs in their cloud environment from 50-80% or more.
This release marks the first step to a fully automated instance “SmartSizing”, which ParkMyCloud will release in January 2019. SmartSizing will take rightsizing a step further by actually automating size corrections, requiring little management on the part of the user.
“With each release of the ParkMyCloud platform, we’re delivering customers another piece of the puzzle they need to fully automate multi-cloud cost optimization,” said Bill Supernor, ParkMyCloud’s Chief Technology Officer. “We developed rightsizing in close contact with customers based on their needs, and initial feedback has been positive.”
ParkMyCloud will demo the new functionality at AWS re:Invent, November 26th through November 29th, and invites attendees to visit the company in the expo hall at booth #1709.
ParkMyCloud provides an easy-to-use platform that helps enterprises automatically identify and eliminate wasted cloud spend. More than 800 enterprises around the world – including Unilever, Sysco, Hitachi ID Systems, Sage Software, and National Geographic – trust ParkMyCloud to cut their cloud spend by millions of dollars annually. ParkMyCloud’s SaaS offering allows enterprises to easily manage, govern, and optimize their spend across multiple public clouds. For more information, visit www.parkmycloud.com.
We chatted with Steve Scott, Cloud Infrastructure Manager at Dealer-FX about how they use ParkMyCloud’s automated AWS management to save significant amounts of time and sanity.
Tell us about what Dealer-FX does, and what your team does within the company.
Dealer-FX provides software solutions to dealerships. Our software is used at the service advisor level – the people that you see when you take your car in. They’re usually behind a monitor that you never get to see and they’re typing away all things associated with your car information, VIN, scheduling information, recall information, etc. Our software controls all of that across many different OEMs, which are the manufacturers, and thousands of dealerships across Canada and the US.
I am the manager of cloud operations here and my team is strictly at the cloud management level, fully invested in AWS. We started using AWS through one of the OEMs we work with and that’s how we got into the cloud a few years ago.
Can you describe more about how you’re using AWS?
We use AWS for all of our testing, development, staging, and production environments. We use it all, from the API level to the functional level with virtual servers and virtual environments – everything we have that’s customer facing resides with AWS today.
Before you started using ParkMyCloud, what challenges did you face in your use of AWS?
One of the biggest things is that we use a lot of servers. When we had somewhere around 400 servers, we started to look into scheduling, both for server maintenance and for things that were only required to be online during certain periods of time. There was no inherent AWS service that was easily configurable for the same function that ParkMyCloud offered.
We’ve been using ParkMyCloud for a few years for automated AWS management to schedule resources on and off. Our code is in a period of transition from legacy to more cloud native, so we don’t have the resources to use some of the more cost-effective offerings from AWS like reserved instances, but we’re getting there. ParkMyCloud is certainly helping us, as we rely on it for scheduling server maintenance, staging, testing, and development environments.
How did you find ParkMyCloud?
I was bugging our AWS rep for some type of scheduling functionality. They could do it, but it would have taken a lot of work, and it was kind of iffy whether or not it would work for us. He directed me to ParkMyCloud.
Do you see yourselves using more cost efficient resources like Reserved Instances in the future?
I wouldn’t say that exactly. One thing we will look into is more autoscaling functionality. We do all of that manually, except ParkMyCloud sets up the scheduling and does that beautifully. We currently use ParkMyCloud scheduling because we have a predictable workload. For example, we might have 8 servers online between a certain number of hours, and after a period of time bring it down to 7, then 6, and so on depending on the environment, and then bring them back up again the next day.
In the future, as we build new apps, we’ll still be utilizing ParkMyCloud as we always have. We have RDS functionality on the horizon, which we know we can also schedule with ParkMyCloud’s automated AWS management.
We also use ParkMyCloud for planning on/off times for our staging environments which are on-demand. We haven’t taken advantage of all the features yet, but we use ParkMyCloud for very strategic reasons, in very strategic places, and it works phenomenally.
How would you describe the benefits that Dealer-FX has gotten from ParkMyCloud?
From the sysadmin perspective, the main reason we wanted ParkMyCloud was the sheer ease of turning servers on and off. Before, we needed to wake up at certain times and do it ourselves, manually turning off and on hundreds of servers. Having to do those things is no one’s cup of tea!
Who was responsible for doing that previously?
It was 2-3 people on my team.
It sounds like that took a lot of time.
It was a significant amount of time, and due to the high volume of deployments and growth over time, it became more and more terrible to administrate. ParkMyCloud is saving us time and sanity all over the place, and it just works. We’ve never had an issue with it. The design is ultimately “set it and forget it.”
Any other feedback?
I know there’s lots of things on the horizon that we’ll be using as needed, and I’d be happy to receive updates of new features. Any new tools, extensions, or anything you add I would love to hear about.
We’ll be sharing rightsizing shortly, so look forward to that next! We appreciate your time and feedback.
Today we’re going to look at an interesting trend we are seeing toward the use of custom machine types in Google Cloud Platform. One of the interesting byproducts of managing the ParkMyCloud platform is that we get to see changes and trends in cloud usage in real time. Since we’re directly at the customer level, we can often see these changes before they are spotted by official cloud industry commentators. They start off as small signals in the noise, but practice has allowed us to see when something is shifting and a trend is emerging – as is the case with these custom machine types.
Over the last year, the shift to greater use of Custom Machine Types (launched in 2016 on Google Compute Engine) and to a lesser extent Optimized EC2 instances (launched in 2018 on AWS) are just such a signal that we have observed growing in strength. Interestingly, Microsoft has yet to offer their equivalent version on Azure.
What do GCE custom machine types let you do?
Custom machine types let you build a bespoke instance to match the specific needs of your workload. Many workloads can be matched to off-the-shelf instance types, but there are many workloads for which it is now possible to build a better matching machine which delivers a more cost effective price. The growth in adoption of this particular instance type supports the case and likely benefits of their availability.
So what are the benefits of these new customized machines? First, they provide a granular level of control to match the needs of your specific application workloads. In practice, this leads to compromise as you select the closest instance type to your optimal configuration. Such compromises typically lead to over-provisioning, a situation we see across the board among our customer base. We analyzed usage of the instances in our platform this summer, and found that across all the instances in our platform, the average vCPU utilization was less than 10%!
Secondly, they allow you to finely tune your machine to maximize the cost effectiveness of your infrastructure. Google claims savings of up to 50% when utilizing their customized options compared to traditional predefined instances, which we believe to be a reasonable assessment as we see the standard instance types are often massively overprovisioned.
On GCE, the variables that you can configure include:
Quantity and type of vCPU’s;
Quantity and type of GPU;
Memory size (albeit there are some limits on the maximum per vCPU).
On AWS, customized options are currently more limited and include only the number and type of vCPU’s, and the options are focused on per-core licensed software problems, rather than cost optimization. It will be interesting if they follow Google and open up cost-based customization options in the coming months, and allow the effective unbundling of fixed off-the-shelf instance types.
Should you use custom machine types?
So just because customization is an option, is this something you should actually pursue? In fact, you will pay a small premium compared to the size of standard instances/VMs, albeit you can optimize for specific workloads, which oftentimes will mean an overall lower cost. To make such an assessment will require that you examine your applications resource use and performance requirements. Such determinations require that you carefully analyze your infrastructure utilization data. This quickly gets complex, although there are a number of platforms which can support thorough analytics and data visualization. Ideally, such analytics would be combined with the ability to recommend specific cost-effective customized instance configurations as well as automate their provisioning.
Watch this space for more news on custom machine types!