Azure Batch Pricing for scaling up and down within an hour - azure

What is the pricing model when autoscaling an Azure Batch cluster up and down within the same hour?
For instance if a vm costs $x per hour and I scale my cluster up to 10 vms, then down to 0, then up to 12 within 1 hour, how many vms do I get charged for?

To my understanding of Azure VM pricing, pricing is listed by the hour, but billing occurs by the minute - rounded down, so 52:43 of usage would be billed as 52 minutes, so your cost for that machine would be:
VM hourly cost * number of VMs for a given period * number of minutes used in that period / 60
In your case, you scale up to 10 VMs, you need to establish how long you're at that level. Then, how long are you at 12 VMs? Apply the formula above each time and add.

Related

How do I calculate the cost of Azure Synapse Analytics?

I need to calculate the cost of the Azure Synapse Analytics. I have used the Azure Pricing Calculator but I could not figure it out. It shows close to USD 2,100.
I have the following components as a part of the Azure Synapse Analytics
Synapse workspace
Self Hosted agent - Standard_B2s
Synapse SQL pool
How do I calculate the cost of Azure Synapse Analytics?
This is a very difficult question to answer, because most of the costs are consumption/runtime oriented.
The pricing calculator defaults are not great, so you'll really want to fine tune it. For instance, you cannot remove Dedicated Pools, but you can set the Hours to 0. It also includes Data Explorer, which cannot be removed. To not include these prices in the calculator, deselect the "Auto select engine instances", and under both Engine V-Cores and Data Management V-Cores, set the hours to 0.
The calculator will NOT include any time for Spark pools (Notebooks) or Data Flows. These are both heavily consumption oriented which will vary greatly based on your runtime choices like pool size. Their costs are based on minutes of consumption, so good luck predicting that.
Here is a sample pricing calculator filled out to describe your situation. The assumptions are below.
you are using a Dedicated SQL pool not a Serverless SQL pool
you have scaled the dedicated SQL pool to DWU100c and left it running 24 hours a day (if you programmatically pause it then that would reduce the cost)
you do not want to commit to running it 24 hours a day for 1 or 3 years and get reserved pricing discounts
in the dedicated SQL pool you have under 1TB of data (compressed) and you have geo-redundant backups enabled
you are running under 1,000 pipeline activities per month on the self-hosted integration runtime, copy activities run less than an hour per month, and other activity hours are less than 7 hours per month.
you are not using other parts of Synapse like Spark pools, data flows, Data Explorer pools, Synapse Serverless SQL, etc.
you are in the East US Azure region
you have a B2s virtual machine with a 128GB premium SSD OS disk and no other attached disks where the self-hosted IR is installed. It is running 24 hours a day. (The VM cost but not storage cost could be lowered if you pause and resume it programmatically)
on the B2s virtual machine you do not want to commit to running it 24 hours a day for 1 or 3 years to get a reserved pricing discount and you are renting the Windows license with the VM rather than bringing your license with Azure Hybrid Benefit
this is retail pricing

Effect of Horizontal scaling / scale out on Azure App Services Pricing

I am following best practices document and trying to implement Auto Scaling and would like to know about pricing perspective.
Robust-Apps-for-the-cloud
I would like to utilize custom auto scale to use multiple instances. I have configured the rules as shown here:
With this, I would like more information on how this will affect the pricing for my app service plan.
Note: My App Service Plan is S2.
App Service Plans are priced based on the size and number of instances you run, and they are billed on a per second basis. For your case on the S2 plan, a single instance will cost $0.20/hour.
I see on your autoscale configuration that the minimum and default number of instances you will be running on this plan is two instances. With this, if the autoscale triggers are not hit, your App Service Plan would cost $0.40/hour.
With the configuration you shared this could run up to $0.80/hour, if maximum four instances are run after the autoscale triggers are met.
As App Service Plans are billed on a per second basis, the cost will be prorated on a per second basis for the number of instances you run.
For example:
if you were running two instances for 40 minutes, three instances for 10 minutes, and four instances for the last 10 minutes of an hour. The total cost of the App Service Plan for that hour would be roughly: $0.50 for that hour.
If you were to scale up or down your App Service Plan tier you can see more information about how this could affect pricing using this tool here:
App Service Pricing

Azure auto-scale VM by time of day

I'm new to Azure and auto-scaling and have some questions I have not find answers to.
Today I use a VM and a Service Bus queue. The queue revives jobs only at beginning of each hour during office hours. Because it is important to execute this jobs quickly I want to scale out VM instances before the jobs arrives. And then scale in after the work is done(around 15 minutes later).
In my Service Bus Queue I have a MLQ(multilevel queue). I want to scale in to 1 VM as fast the queue with highest priority is empty.
So, my questions is:
How can I scale out VM instances 10 minutes before each our during office hours? How can I Scale in when the highest prioritized queue is empty? Is using Azure Scale Set a good approach? What techniques can be used to achieve this scaling rules?
Thanks in advance

Shared Websites in Azure

If there are only requests to a shared Azure website during day hours (12 hours per day) does this mean the monthly bill would be for ~375 hours? All of the calculator prices are based on 744 hours which equates to one month.
Currently the calculator shows the pricing for one shared website is the same as one small VM so why even have the shared level at all?
Edit: I just found out there was actually a bug in the Azure Calculator.
You will have to pay for every hour (minute from June) your Web site, VM, Mobile service and cloud service has been online/available. This means if you keep your site running for a month, you will have to pay for ~744 hours (average hours in a month).
Microsoft changed the Azure pricing model to a charge per minute your VM is running last month (June). Previous to last month you would also have to pay for each hour even if you VM was stopped. When you stop your VM now, you will no longer be billed for each hour the VM is stopped.
In your case, this means you would have to stop and start your website to pay less per month.
As for the pricing. As far as I know the cost for one shared website is less than the other options (extra small VM, extra small CS, etc).
For example (using the calculator for 1 instance):
Shared Website: €7,21
Extra Small VM: €11,09

cloud services payments

All cloud payments described in hours.
Let's look at situation when server spend 0.75s to generate page and only one time this month (because nobody requested website).
Here is text from AWS and Azure website
"Pay only for what you use. There is no minimum fee. Estimate your
monthly bill using the AWS Simple Monthly Calculator."
"Windows Azure Pricing No upfront costs. Pay only for what you use"
Does it mean that I will pay just for 0.75s or for entire month?
You'll have to pay for one hour:
AWS:
Pricing is per instance-hour consumed for each instance, from the time
an instance is launched until it is terminated. Each partial
instance-hour consumed will be billed as a full hour.
Azure:
Compute hours are billed based on the number of clock hours your
service was deployed multiplied by the number of equivalent small
compute instances included in your deployment. Partial compute
instance hours (prior to conversion) are billed as full compute hours
for each clock hour an instance is deployed.

Resources