Capacity Reservation

[marius1106]

Dear Microsoft,

I want to reserve a capacity and have 2 Querstions:

1) What ist the Cancellation period?

2) What happens if i exceed the hourly limit of my capacity => For example with my F16-Capa: What happens if I exceed the daily limit? Do I have to pay extra for the ressources on top or is it still the fix-price?

Thanks

[v-karpurapud]

Hi @marius1106 
Hope you are doing well!

I wanted to check if you had the opportunity to review the information provided. Please feel free to contact us if you have any further questions. If my response has addressed your query, please accept it as a solution and give a 'Kudos' so other members can easily find it.

Thank you.

[marius1106]

Hello v-karpurapud,

I still had this one final Question 😉 :

What if I reserved an Instance for one year and after 3 months I realize that I have to scale up/down my capacity: Is it possible? And what about the remaining costs - are they just double the price or is it a new year of contract?

Thank you for your time!

[v-karpurapud · Accepted solution]

Hi @marius1106 

Regarding your question, the solution provided by @lbendlin  addresses the issue. Once a reservation is made for the selected duration (one or three years), you will continue to pay the reserved price throughout the term, regardless of usage. However, if your usage exceeds the reserved capacity, any additional consumption will be charged at Pay-As-You-Go (PAYG) rates.

Additionally, while scaling up is possible by purchasing additional reserved capacity (which starts a new reservation contract), scaling down is not permitted mid-term. The original reservation commitment remains unchanged until its expiration.

If this post helps , kindly mark as Accepted Solution and appreciate your Kudos.

Thank You.

[v-karpurapud]

Hello @marius1106 

May I ask if you have resolved this issue? If so, please mark the helpful reply and accept it as the solution. This will be helpful for other community members who have similar problems to solve it faster.

Thank you.

[v-karpurapud]

Hello @marius1106 

Thank you @lbendlin  for sharing the helpful information.

Thank you for reaching out to Microsoft Fabric Capacity. We understand that you are seeking insights regarding capacity reservation. Let’s clarify this in detail:

Cancellation Policy: Microsoft Fabric reserved capacities follow Azure reservation policies. Pay-as-you-go subscriptions can be canceled anytime, while reserved instance (RI) purchases are subject to Microsoft's refund rules, typically allowing a prorated refund with a cancellation fee.

Exceeding Capacity Limits: Microsoft Fabric capacities are provisioned based on SKU limits (e.g., F2, F8, F16, etc.). If your workload exceeds the allocated capacity, the system will queue or throttle tasks instead of automatically incurring additional charges. There are no extra costs beyond your reserved capacity.

For detailed information, please refer to the link below:

Save costs with Microsoft Fabric Capacity reservations - Microsoft Cost Management | Microsoft Learn

If my response has resolved your query, please mark it as the Accepted Solution to assist others. Additionally, a 'Kudos' would be appreciated if you found my response helpful.

Thank you!

[marius1106]

Hello @v-karpurapud,

thank you for your answer. So is a reserved capacity different from the pay-as-you-go concerning the extra costs when exceeding capacity limits? Because I tested a lower capacity as pay-as-you-go and got extra costs as I exceeded the limit.

So did I understand that correct, that with the reserved Instance there are no extra costs any time?

Thanks,

Marius

[v-karpurapud]

Hello @marius1106 

Yes, you understood correctly! With Reserved Capacity, there are no additional costs beyond your purchased limit. However, workloads may be throttled or queued instead of scaling up automatically. In contrast, Pay-as-you-go scales dynamically, which can result in extra charges when capacity is exceeded.

If you find this post helpful, please mark it as an "Accept as Solution" and give a KUDOS.

Thank you!

[marius1106]

Hello @v-karpurapud,

now I understood my 2) better, thank you!

But for my first Question, I didnt understand the answer and Azure Doks. Which rules are defined for canceling a reserved capacity? 

- For example after one year I dont want to extend the contract

- For example I want to cancel the contract 3 months before end

Thanks!

[v-karpurapud]

Hello @marius1106 

Here is detailed information regarding your first Question:

1. If you do not want to extend the contract after one year, no action is required as the reservation will expire automatically, and you will not be charged further.
2. If you cancel your reservation early (e.g., 3 months before it ends), Microsoft provides a prorated refund with a 12% cancellation fee, and refunds are limited to $50,000 per year per customer, subject to approval.

For more details, you can review Microsoft’s official document:

What are Azure Reservations? - Microsoft Cost Management | Microsoft Learn

Thank You.

[marius1106]

Hello, 

thanks for your replay. Another final Question:

What if I reserved an Instance for one year and after 3 months I realize that I have to scale up/down my capacity: Is it possible? And what about the remaining costs - are they just double the price or is it a new year of contract?

[lbendlin]

You made the reservation, for a year or three.

Within the limits of that reservation you can modify your capacities as you like. You will pay the reservation price, no matter what.

If you exceed the reservation you will have to PAYG for that part.

You cannot reduce a reservation mid term. You can add more reservation.

[lbendlin]

Part 3

(20) Fabric Billing Part 1- What is a capacity model? | LinkedIn

(21) Fabric Billing Part 2- Bursting, Smoothing and the evolution of the Fabric Capacity Model | Lin...

(21) Fabric Billing Part 3- Reservations (RI) v Pay As You Go (PAYG) | LinkedIn

(21) Fabric Billing Part 4- Implications of pause and restart | LinkedIn

(21) Fabric Billing Part 5- Implications of scale up and scale down | LinkedIn