Cloud Optimization Tools = Cloud Cost Control

Cloud Optimization Tools = Cloud Cost Control

Over the past couple of years we have had a lot of conversations with large and small enterprises regarding cloud management and cloud optimization tools, all of whom were looking for cost control. They wanted to reduce their bills, just like any utility you might run at home — why spend more than you need to? Amazon Web Services (AWS) actively promotes optimizing cloud infrastructure, and where they lead, others follow. AWS even goes so far as to suggest the following simple steps to control AWS costs:

  1. Right-size your services to meet capacity needs at the lowest cost;
  2. Save money when you reserve;
  3. Use the spot market;
  4. Monitor and track service usage;
  5. Use Cost Explorer to optimize savings; and
  6. Turn off idle instances (we added this one).

Its interesting to note use of the word ‘control’ even though the section is labeled Cost Optimization.

So where is all of this headed? It’s great that AWS offers their own solutions but what if you want automation into your DevOps processes, multi-cloud support (or plan to be multi cloud), real-time reporting on these savings, and to turn stuff off when you are not using it? Well then you likely need to use a third-party tool to help with these tasks.

Let’s take a quick look at a description of each AWS recommendation above, and get a better understanding of each offering. Following this we will then explore if these cost optimization options can be automated as part of a continuous cost control process:

  1. Right-sizing – Both the EC2 Right Sizing solution and AWS Trusted Advisor analyze utilization of EC2 instances running during the prior two weeks. The EC2 Right Sizing solution analyzes all instances with a max CPU utilization less than 50% and determines a more cost-effective instance type for that workload, if available.
  2. Reserved Instances (RI) – For certain services like Amazon EC2 and Amazon RDS, you can invest in reserved capacity. With RI’s, you can save up to 75% over equivalent ‘on-demand’ capacity. RI’s are available in three options – (1) All up-front, (2) Partial up-front or (3) No upfront payments.
  3. Spot – Amazon EC2 Spot instances allow you to bid on spare Amazon EC2 computing capacity. Since Spot instances are often available at a discount compared to On-Demand pricing, you can significantly reduce the cost of running your applications, grow your application’s compute capacity and throughput for the same budget, and enable new types of cloud computing applications.
  4. Monitor and Track Usage – You can use Amazon CloudWatch to collect and track metrics, monitor log files, set alarms, and automatically react to changes in your AWS resources. You can also use Amazon CloudWatch to gain system-wide visibility into resource utilization, application performance, and operational health.
  5. Cost Explorer – AWS Cost Explorer gives you the ability to analyze your costs and usage. Using a set of default reports, you can quickly get started with identifying your underlying cost drivers and usage trends. From there, you can slice and dice your data along numerous dimensions to dive deeper into your costs.
  6. Turn off Idle Instances – To “park” your cloud resources by assigning them schedules of operating hours they will run or be temporarily stopped – i.e. parked. Most non-production resources (dev, test, staging, and QA) can be parked at nights and on weekends, when they are not being used. On the flip side of this, some batch processing or load testing type applications can only run during non-business hours, so they can be shut down during the day.

Many of these AWS solutions offer recommendations, but do require manual efforts to gain the benefits. This is why third party solutions have have seen widespread adoption and include cloud management, cloud governance and visibility, and cloud optimization tools. In part two of this this blog we will have a look at some of those tools, the benefits of each, approach and the level of automation to be gained.

Cloud Cost Management Tool Comparison

Cloud Cost Management Tool Comparison

Not only has it become apparent that public cloud is here to stay, it’s also growing faster as time goes on (by 2020, it is estimated that more than 40% of enterprise workloads will be in the cloud). IT infrastructure has changed permanently, and enterprise organizations are coming to terms with some of the side effects of this shift.  One of those side effects is the need for tools and processes (and even teams in larger organizations) dedicated to cloud cost management and cost control.  Executives from all teams within an organization want to see costs, projections, usage, savings, and quantifiable efforts to save the company money while maximizing IT throughput as enterprises shift to resources to the cloud.  

There’s a variety of tools to solve some of these problems, so let’s take a look at a few of the major ones.  All of the tools mentioned below support Amazon AWS, Microsoft Azure, and Google Cloud Platform.

CloudHealth

CloudHealth provides detailed analytics and reporting on your overall cloud spend, with the ability to slice-and-dice that data in a variety of ways.  Recommendations about your instances are made based on a score driven by instance utilization and cloud provider best practices. This data is collected from agents that are installed on the instances, along with cloud-level information.  Analysis and business intelligence tools for cloud spend and infrastructure utilization are featured prominently in the dashboard, with governance provided through policies driven by teams for alerts and thresholds.  Some actions can be scripted, such as deleting elastic IPs/snapshots and managing EC2 instances, but reporting and dashboards are the main focus.

Overall, the platform seems to be a popular choice for large enterprises wanting cost and governance visibility across their cloud infrastructure.  Pricing is based on a percentage of your monthly cloud spend.

CloudCheckr

Cloudcheckr provides visibility into governance, security, compliance, and cost problems based on doing analytics and checks against logic built into their platform. It relies on non-native tools and integrations to take action on the recommendations, such as Spotinst, Ansible, or Chef.  CloudCheckr’s reports cover a wide range of topics, including inventory, utilization, security, costs, and overall best-practices. The UI is simple and is likely equally well regarded by technical and non-technical users.

The platform seems to be a popular choice with small and medium sized enterprises looking for greater overall visibility and recommendations to help optimize their use of cloud.  Given their SMB focus customers are often provided this service through MSPs. Pricing is based on your cloud spend, but a free tier is also available.

Cloudyn

Cloudyn (recently acquired by Microsoft) is focused on providing advice and recommendations along with chargeback and showback capabilities for enterprise organizations. Cloud resources and costs can be managed through their hierarchical team structure.  Visibility, alerting, and recommendations are made in real time to assist in right-sizing instances and identifying outlying resources.  Like CloudCheckr, it relies on external tools or people to act upon recommendations and lacks automation

Their platform options include supporting MSPs in the management of their end customer’s cloud environments as well as an interesting cloud benchmarking service called Cloudyndex.  Pricing for Cloudyn is also based on your monthly cloud spend.  Much of the focus seems to be on current Microsoft Azure customers and users.

ParkMyCloud

Unlike the other tools mentioned, ParkMyCloud focuses on actions and automated scheduling of resources to provide optimization and immediate ROI.  Reports and dashboards are available to show the cost savings provided by these schedules and recommendations on which instances to park.  The schedules can be manually attached to instances, or automatically assigned based on tags or naming schemes through its Policy Engine.  It pairs well with the other previously mentioned recommendation-based tools in this space to provide total cost control through both actions and reporting.

ParkMyCloud is widely used by DevOps and IT Ops in organizations from small startups to global multinationals, all who are keen to automate cost control by leveraging ParkMyCloud’s native API and pre-built integration with tools like Slack, Atlassian, and Jenkins.  Pricing is based on a cost per-instance, with a free tier available.

Conclusion

Cloud cost management isn’t just a “should think about” item, it’s a “must have in place” item, regardless of the size of a company’s cloud bill.  Specialized tools can help you view, manage, and project your cloud costs no matter which provider you choose.  The right toolkit can supercharge your IT infrastructure, so consider a combination of some of the tools above to really get the most out of your AWS, Azure, or Google environment.

New: Park AWS RDS Instances with ParkMyCloud

New: Park AWS RDS Instances with ParkMyCloud

Now You Can Park AWS RDS Instances with ParkMyCloud

We’re happy to share that you can now park AWS RDS instances with ParkMyCloud!

AWS just recently released the ability to start and stop RDS instances. Now with ParkMyCloud, you can automate RDS start/stop on a schedule, so your databases used for development, testing, and other non-production purposes are only running when you actually need them – and you only pay for the hours you use. This is the first parking feature on the market that’s fully integrated with AWS’s new RDS start/stop capability.

You can also use ParkMyCloud’s policy engine to create rules that automatically assign your RDS instances to parking schedules and to teams, so they’re only accessible to the users who need them.

Why it Matters

Our customers who use AWS have long asked for the ability to park RDS instances. In fact,

RDS is the area of biggest of cloud spend after compute, accounting for about 15-20% of an average user’s bill. The savings users can enjoy from parking RDS will be significant. On average, ParkMyCloud users save $140 per parked instance per month on compute – and as RDS instances cost significantly more per hour, the savings will be proportionally higher.

“We’ve used ParkMyCloud for over a year to reduce our EC2 spend, enjoying a 13X return on our yearly license fee – it’s literally saved us thousands of dollars on our AWS bill. We look forward to saving even more now that ParkMyCloud has added support for RDS start/stop!” – Anthony Suda, Release Manager/Senior Network Manager, Sundog.

How to Get Started

It’s easy to get started and park AWS RDS instances with ParkMyCloud.

If you don’t yet use ParkMyCloud, you can try it now for free. We offer a 14-day free trial of all ParkMyCloud features, after which you can choose to subscribe to a premium  plan or continue parking your instances using ParkMyCloud’s free tier.

If you already use ParkMyCloud, you’ll need to check your AWS permissions and ParkMyCloud policies out, and then turn on the RDS feature via your settings page. Please see more information about this on our support page.

As always, we welcome your feedback about this new addition to ParkMyCloud, and anything else you’d like to see in the future.

Happy parking!

Start and Stop RDS Instances on AWS – and Schedule with ParkMyCloud

Start and Stop RDS Instances on AWS – and Schedule with ParkMyCloud

Amazon Web Services shared today that users can now start and stop RDS instances – check out the full announcement on their blog.

This is good news for cost-conscious engineering teams. Until now, databases were generally left running 24×7, even if they were only used during working hours for testing and staging purposes. Now, they can be turned off, so you’re not charged for time you’re not using. Nice!

Keep in mind that stopping the RDS instances will not bring the cost to zero – you will still be charged for provisioned storage, manual snapshots and automated backup storage.

Now, what if you want to start and stop RDS instances on an automated schedule to ensure they’re not left running when they’re not needed? Coming soon, you’ll be able to with ParkMyCloud!

Start and Stop RDS Instances on a Schedule with ParkMyCloud

Since ParkMyCloud was first released, customers have been asking us for the ability to park their RDS instances in the same way that they can park EC2 instances and auto scaling groups.

The logic to start/stop RDS instances using schedules is already in the production code for ParkMyCloud. We have been patiently waiting for AWS to officially announce this capability, so that we could turn the feature ON and release it to the public. That day is finally here!

Our development team has some final end-to-end testing to complete, just to make sure everything works as expected. Expect RDS parking to be released within a couple of weeks! Let us know if you’d like to be notified when this is released, or if you’re interested in beta testing the new functionality.

 

We’re excited about this opportunity to give ParkMyCloud users what they’re asking for. What else would you like to see for optimal cost control? Comment below to let us know.

Cutting through the AWS and Azure Cloud Pricing Confusion (Caveat Emptor)

Cutting through the AWS and Azure Cloud Pricing Confusion (Caveat Emptor)

Before I try to break down the AWS and Azure cloud pricing jargon, let me give you some context. I am a crusty, old CTO who has been working in advanced technology since the 1980’s. (That’s more than 18 Moore’s Law cycles for processor and chipset fans, and I have lost count of how many technology hype cycles that has been.)

I have grown accustomed to the “deal of a lifetime” on the “technology of the decade” coming around about once every week. So, you can believe me, when I tell you have a very low BS threshold for dishonest sales folks and bogus technology claims. Yes, I am jaded.

My latest venture is a platform, ParkMyCloud, that brings together  multiple public cloud providers. And I can tell you first hand that it is not for the faint-of-heart. It’s like being dropped off in the middle of the jungle in Papua, New Guinea. Each cloud provider has its own culture, its own philosophy, its own language and customers, its own maturity level and, worst of all — its own pricing strategy — which makes it tough for buyers to manage costs. I am convinced that the lowest circles of hell are reserved for people who develop cloud service pricing models. AWS and Azure cloud pricing gurus, beware. And reader, to you: caveat emptor.

AWS and Azure Terminology Differences

Case in point: You have probably read the comparisons of various services across the top cloud providers, as people try to wrap their minds around all the varying jargon used to describe pretty much the same thing. For example, let’s just look at one service: Cloud Computing.

In AWS, servers are called Elastic Compute Cloud (EC2) “Instances”. In Azure they are called “Virtual Machines” or “VMs”. Flocks of these spun up from a snapshot according scaling rules are called “auto scaling groups” in AWS. The same things are called “scale sets” in Azure.

Of course cloud providers had to start somewhere, then they learned from their mistakes and improved. When AWS started with EC2, they had not yet released virtual private clouds (VPCs), so their instances ran outside of VPCs. Now all the latest stuff runs inside of VPCs. The older ones are called, “classic” and have a number of limitations.
The same thing is true of Azure. When they first released, their VMs were not set up to use what is now their Resource Manager or be managed in Resource Groups (the moral equivalent of CloudFormation Stacks in AWS). Now, all of their latest VMs are compatible with Resource Manager. The older ones are called, you guessed it … “classic”.

(What genius came up with the idea to call the older versions of these, the ones you’re probably stranded with and no longer want, “classic”?)

Both AWS and Azure have a dizzying array of instances/VMs to choose from, and doing an apples-to-apples comparison between them can be quite daunting. They have different categories: General purpose, compute optimized, storage optimized, disk optimized, etc.

Then within each one of those, there are types or sizes. For example, in AWS the tiny, cheap ones are currently the “t2” family. In Azure, they are the “A” series. On top of that there are different generations of processors. In AWS, they use an integer after the family type, like t2, m3, m4 and there are sizes, t2.small, m3.medium, m4.large, r16.ginormus (OK, I made that one up).  

In Azure, they use a number after the family letter to connote size, like A0, A1, A2, D1, etc. and “v1”, “v2” after that to tell what generation it is, like D1v1, D2v2.

The bottom line: this is very confusing for folks moving their workloads to public cloud from on-premise data centers (yet another Wonderland of jargon and confusion in its own right). How does one decide which cloud provider to use? How does one even begin to compare prices with all of this mess? Cheer up … it gets worse!

AWS and Azure Cloud Pricing – Examining Differences in Charging

To add to that confusion, they charge you differently for the compute time you use. What do I mean?  AWS prices their compute time by the hour. And by hour, they mean any fraction of an hour: If you start an instance and run it for 61 minutes then shut it down, you get charged for 2 hours of compute time.

Microsoft Azure cloud pricing is listed by the hour for each VM, but they charge you by the minute. So, if you run for 61 minutes, you get charged for 61 minutes. On the surface, this sounds very appealing (and makes me want to wag my finger at AWS and say, “shame on you, AWS”).

However, you really have to pay attention to the use case and the comparable instance prices. Let me give you a concrete example. I mentioned my latest venture, ParkMyCloud, earlier. We park (schedule on/off times) for cloud computing resources in non-production environments (without scripting by the way). So, here is a graph of 6 months worth of data from an m4.large instance somewhere in Asia Pac. The m4 processor family is based on the Xeon Broadwell or Haswell processor and it is one of the most commonly used instance types.

This instance is on a ParkMyCloud parking schedule, where it is RUNNING from 8:00 a.m. to 7:00 p.m. on weekdays and PARKED evenings and weekends. This instance, assuming Linux pricing, costs $0.125 per hour in AWS. From November 6, 2016 until May 9, 2017, this instance ran for 111,690 minutes. This is actually about 1,862 hours, but AWS charged for 1,922 hours and it cost $240.25 in compute time.

example of instance uptime in minutes per dayWhy the difference? ParkMyCloud has a very fast and accurate orchestration engine, but when you start and stop instances, the cloud provider and network response can vary from hour-to-hour and day-to-day, depending on their load, so occasionally things will run that extra minute. And, even though this instance is on a parking schedule, when you look at the graph, you can see that the user took manual control a few times. Stuff happens!

What would the cost have been if AWS charged the same way as Azure?  It would have only cost $232.69. Well, that’s not too bad over the course of six months, unless you have 1,000 of these. Then it becomes material.

However, I wouldn’t rush to judgment on AWS. If you look at the comparable Azure VM, the standard pricing DS2 V2, also running Linux, costs $0.152/hour. So, this same instance running in Azure would have cost $290.39. Yikes!

Therefore, in my particular use case, unless the Azure cloud pricing drops to make their CPU pricing more competitive, their per minute pricing really doesn’t save money.

Conclusion

The ironic thing about all of this, is that once you get past all the confusing jargon and the ridiculous approaches to pricing and charging for usage, the actual cloud services themselves are much easier to use than legacy on-premise services. The public cloud services do provide much better flexibility and faster time-to-value. The cloud providers simply need to get out of their own way. Pricing is but one example where AWS and Azure need to make things a lot simpler, so that newcomers can make informed decisions.

From a pricing standpoint, AWS on-demand pricing is still more competitive than Azure cloud pricing for comparable compute engine’s, despite Azure’s more enlightened approach to charging for CPU/Hr time. That said, AWS really needs to get in-line with both Azure and Google, who charge by the minute. Nobody likes being charged extra for something they don’t use.

In the meantime, ParkMyCloud will continue to help you turn off non-production cloud resources, when you don’t need them and help save you a lot of money on your monthly cloud bills. If we make anything sound more complex than it needs to, call us out. No hiding behind jargon here.

Azure vs. AWS 2018: Is Azure really surpassing AWS?

Azure vs. AWS 2018: Is Azure really surpassing AWS?

Azure vs. AWS 2017: what’s the deal? There’s been a lot of speculation lately that Microsoft Azure may be outpacing Amazon Web Services (AWS). We think that’s interesting and therefore worth taking a look at these claims. After all, AWS has been dominating the public cloud market for so long, maybe the media is just bored of that story, and ready for an underdog to jump ahead. So let’s take a look.

Is Azure catching up to AWS?

You may have seen some of the recent reports on both Microsoft and Amazon’s recent quarterly earnings. There have certainly been some provocative headlines:

With Amazon and Microsoft reporting their quarterly earnings at the same time, this is a good time to analyze the numbers and see where they stand in relation to one another. Upon closer inspection, here’s what the recent quarterly earnings reports showed:

  • AWS revenue grew 43% in the quarter, with quarterly earnings of $3.66 billion, annualized to $14.6 billion. Sales and earnings exceeded expectations given by analyst estimates. In the immediate wake of Amazon’s report, the stock went up.
  • Microsoft reported that its Intelligent Cloud division grew 11% to $6.8 billion, and that the Commercial Cloud division has a annualized run rate of $15.2 billion. These reported earnings only met analyst expectations, and therefore the stock fell by nearly 2 percent within hours.
  • We think it’s important to note when it comes to Microsoft’s reported earnings the Commercial Cloud business includes Office 365, not just Azure. We have never fully understood why the Office 365 business has been bundled in with Commercial Cloud, given that it’s a very different business than the IAAS services of Amazon and Google to which it is often compared.
  • Microsoft stated that Azure’s growth rate was 93%, without providing an actual revenue number. Once again, we find this lack of lack of earnings clarity somewhat problematic.

So is Azure bigger than AWS?

Well, currently no. There is little evidence of Azure surpassing AWS, aside from a small research study which pales in comparison to a clear majority of data stating otherwise.

But is Azure growing quickly?

Yes. In this regard, it’s important to consider what factors are at play in Azure’s growth, and whether they hold any weight as far as surpassing Azure outpacing AWS in the future.

Where is Azure actually gaining ground?

Now let’s take a look at what is driving Azure’s growth, and where Azure is gaining ground.

First of all, as companies grow beyond dipping their toes in the water of public cloud, they become more interested in secondary options for diversity and different business cases. Just from our own conversations, we’re finding that more and more AWS users are using Azure as a secondary option. While users might be interested to see what Azure can offer them in comparison, this doesn’t necessarily indicate that it will ultimately surpass AWS.

Take, for example, the results of a research survey released by data analytics provider Sumo Logic and conducted by UBM Research. According to the survey of 230 IT professionals from 500+ employees, Azure actually beat AWS as the preferred primary cloud provider, taking the lead by a 10 percent margin, with 66 percent of participants preferring Azure as opposed to the 55 percent who relied in AWS.

This research is significant because it’s the first time that survey data on customer preferences has reported Azure taking a lead over AWS. However, the data also revealed that a significant number of enterprises are using more than one cloud provider. While Azure and AWS both take the lead, there is certainly an overlap in participants who use both, in addition to other up-and-coming providers.

Second, enterprises have been committed to a variety of Microsoft products for years. According to UBM Research survey data, over 50 percent of participants who preferred Azure as their primary cloud provider were coming from large enterprises with 10,000+ employees. This makes sense considering that Microsoft has a foothold in terms of relationships and enterprise agreements with these larger organizations and are able to cross-sell Azure.

Third, Azure has a strong base in Europe, where more users report using Azure rather than AWS as their primary provider. In a 451 Research Survey with 700 participants considered to be “IT decision makers,” AWS topped the list among all participants as the preferred provider among 39 percent of participants. While Azure saw an increase in users, it still landed in second place overall at 35 percent. However, among the European participants only, Azure took the top spot, with 43.7 percent naming Azure as their provider, and 32 percent sticking with AWS.

Why does the Azure vs. AWS debate matter?

Why does the Azure vs. AWS 2017 debate matter to, when choosing a new or secondary cloud provider? Well… in terms of market performance, it probably doesn’t. As always, the specific needs of your business are going to be what’s important.

One thing is for certain: the public cloud is growing and it’s here to stay. Let’s not forget that both Google and IBM both have growing public cloud offerings too (and Google is looking to expand their enterprise market this year.) All of this competition drives innovation, and therefore IaaS and PaaS offerings – and perhaps, better pricing.

For the customer, the basic questions remain the same when evaluating public cloud providers:

  • How understandable are the public cloud offerings to new customers?
  • How much do the products cost?
  • Are there adequate customer support and growth options?
  • Are there useful surrounding management tools?
  • Will our DevOps processes translate to these offerings?
  • Can the PaaS offerings speed time-to-value and simplify things sufficiently, to drive stickiness?
  • What security measures does the cloud provider have in place?

Based upon the evidence we think it’s pretty clear that AWS is still the leader among public cloud providers.

We’ll continue to track the AWS vs. Azure comparison, and as the companies’ offerings and pricing options grow and change – we’ll be interested to see how this evaluation changes in 2018.