Beware the sticker shock – cloud services pricing is nothing close to simple, especially as you come to terms with the dollar amount on your monthly cloud bill. While cloud service providers like AWS, Azure, and Google were meant to provide compute resources to save enterprises money on their infrastructure, cloud services pricing is complicated, messy, and difficult to understand. Here are 7 ways that cloud providers obscure pricing on your monthly bill:
1 – They use varying terminology
For the purpose of this post, we’ll focus on the three biggest cloud service providers: AWS, Azure, and Google. Between these three cloud providers alone, different analogies are used for just about every component of services offered.
For example, when you think of a virtual machine (VM), that’s what AWS calls an “instance,” Azure calls a “virtual machine,” and Google calls a “virtual machine instance.” If you have a group of these different machines, or instances, in Amazon and Google they’re called “auto-scaling” groups, whereas in Azure they’re called “scale sets.” There’s also different terminology for their pricing models. AWS offers on-demand instances, Azure calls it “pay as you go,” and Google refers to it as “sustained use.” You’ve also got “reserved instances” in AWS, “reserved VM instances” in Azure, and “committed use” in Google. And you have spot instances in AWS, which are the same as low-priority VMs in Azure, and preemptible instances in Google.
2 – There’s a multitude of variables
Operating systems, compute, network, memory, and disk space are all different factors that go into the pricing and sizing of these instances. Each of these virtual machine instances also have different categories: general purpose, compute optimized, memory optimized, disk optimized and other various types. Then, within each of these different instance types, there are different families. In AWS, the cheapest and smallest instances are in the “t2” family, in Azure they’re called the “A” family. On top of that, there are different generations within each of those families, so in AWS there’s t2, t3, m2, m3, m4, and within each of those processor families, different sizes (small, medium, large, and extra large). So there’s lots of different options available. Oh, and lots confusion, too.
3 – It’s hard to see what you’re spending
If you aren’t familiar with AWS, Azure, or Google Cloud’s consoles or dashboards, it can be hard to find what you’re looking for. To find specific features, you really need to dig in, but event just trying to figure out the basics of how much you’re currently spending, and predicting how much you will be spending – all can be very hard to understand. You can go with the option of building your own dashboard by pulling in from their APIs, but that takes a lot of upfront effort, or you can purchase an external tool to manage overall cost and spending.
4 – It’s based on what you provision…not what you use
Cloud services pricing can charge on a per-hour, per-minute, or per-second basis. If you’re used to the on-prem model where you just deploy things and leave them running 24/7, then you may not be used to this kind of pricing model. But when you move to the cloud’s on-demand pricing models, everything is based on the amount of time you use it.
When you’re charged per hour, it might seem like 6 cents per hour is not that much, but after running instances for 730 hours in a month, it turns out to be a lot of money. This leads to another sub-point: the bill you get at the end of the month doesn’t come until 5 days after the month ends, and it’s not until that point that you get to see what you’ve used. As you’re using instances (or VMs) during the time you need them, you don’t really think about turning them off or even losing servers. We’ve had customers who have servers in different regions, or on different accounts that don’t get checked regularly, and they didn’t even realize they’ve been running all this time, charging up bill after bill.
You might also be overprovisioning or oversizing resources — for example, provisioning multiple extra large instances thinking you might need them someday or use them down the line. If you’re used to that, and overprovisioning everything by twice as much as you need, it can really come back to bite you when you go look at the bill and you’ve been running resources without utilizing them, but are still getting charged for them – constantly.
5 – They change the pricing frequently
Cloud services pricing has changed quite often. So far, they have been trending downward, so things have been getting cheaper over time due to factors like competition and increased utilization of data centers in their space. However, don’t jump to conclude that price changes will never go up.
Frequent price changes make it hard to map out usage and costs over time. Amazon has already made changes to their price more than 60 times since they’ve been around, making it hard for users to plan a long-term approach. Also for some of these instances, if you have them deployed for a long time, the prices of instances don’t display in a way that is easy to track, so you may not even realize that there’s been a price change if you’ve been running the same instances on a consistent basis.
6 – They offer cost savings options… but they’re difficult to understand (or implement)
In AWS, there are some cost savings measures available for shutting things down on a schedule, but in order to run them you need to be familiar with Amazon’s internal tools like Lambda and RDS. If you’re not already familiar, it may be difficult to actually implement this just for the sake of getting things to turn off on a schedule.
One of the other things you can use in AWS is Reserved Instances, or with Azure you can pay upfront for a full year or two years. The problem: you need to plan ahead for the next 12 to 24 months and know exactly what you’re going to use over that time, which sort of goes against the nature of cloud as a dynamic environment where you can just use what you need. Not to mention, going back to point #2, the obscure terminology for spot instances, reserved instances, and what the different sizes are.
7 – Each service is billed in a different way
Cloud services pricing shifts between IaaS (infrastructure as a service), which uses VMs that are billed one way, and PaaS (platform as a service) gets billed another way. Different mechanisms for billing can be very confusing as you start expanding into different services that cloud providers offer.
As an example, the Lambda functions in AWS are charged based on the number of requests for your functions, the duration, and the time it takes for your code to execute. The Lambda free tier includes 1M free requests per month and 400,000 GB-seconds of compute time per month, or you can get 1M request free and $0.20 per 1M requests thereafter, OR use “duration” tier and get 400,000 GB-seconds per month free, $0.00001667 for every GB-second used thereafter – simple, right? Not so much.
Another example comes from the databases you can run in Azure. Databases can run as a single server or can be priced by elastic pools, each with different tables based on the type of database, then priced by storage, number of databases, etc.
With Google Kubernetes clusters, you’re getting charged per node in the cluster, and each node is charged based on size. Nodes are auto-scaled, so price will go up and down based on the amount that you need. Once again, there’s no easy way of knowing how much you use or how much you need, making it hard to plan ahead.
What can you do about it?
Ultimately, cloud service offerings are there to help enterprises save money on their infrastructures, and they’re great options IF you know how to use them. To optimize your cloud environment and save money on costs, we have a few suggestions:
- Get a single view of your billing. You can write your own scripts (but that’s not the best answer) or use an external tool.
- Understand how each of the services you use is billed. Download the bill, look through it, and work with your team to understand how you’re being billed.
- Make sure you’re not running anything you shouldn’t be. Shut things down when you don’t need them, like dev and test instance on nights and weekends.Try to plan out as much as you can in advance.
- Review regularly to plan out usage and schedules as much as you can in advance
- Put governance measures in place so that users can only access certain features, regions, and limits within the environment.