Which solution will meet these requirements MOST cost-effectively?

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A company uses an on-premises data analytics platform.The system is highly available in a fully redundant configuration across 12 servers in the company’s data center.The system runs scheduled jobs, both hourly and daily, in addition to one-time requests from users.Scheduled jobs can take between 20 minutes and 2 hours to finish running and have tight SLAs.The scheduled jobs account for 65% of the system usage.User jobs typically finish running in less than 5 minutes and have no SLA.The user jobs account for 35% of system usage.During system failures, scheduled jobs must continue to meet SLAs.However, user jobs can be delayed.A solutions architect needs to move the system to Amazon EC2 instances and adopt a consumption-based model to reduce costs with no long-term commitments.The solution must maintain high availability and must not affect the SLAs.

Which solution will meet these requirements MOST cost-effectively?

Split the 12 instances across two Availability Zones in the chosen AWS Region. Run two instances in each Availability Zone as On-Demand Instances with Capacity Reservations. Run four instances in each Availability Zone as Spot Instances.

Split the 12 instances across three Availability Zones in the chosen AWS Region. In one of the Availability Zones, run all four instances as On-Demand Instances with Capacity Reservations. Run the remaining instances as Spot Instances.

Split the 12 instances across three Availability Zones in the chosen AWS Region. Run two instances in each Availability Zone as On-Demand Instances with a Savings Plan. Run two instances in each Availability Zone as Spot Instances.

Split the 12 instances across three Availability Zones in the chosen AWS Region. Run three instances in each Availability Zone as On-Demand Instances with Capacity Reservations. Run one instance in each Availability Zone as a Spot Instance.

Explanations:

Running two instances in each Availability Zone as On-Demand Instances with Capacity Reservations and four instances as Spot Instances does not guarantee the required availability during failures, especially for scheduled jobs. Capacity Reservations ensure high availability for On-Demand Instances but the Spot Instances can be interrupted, which is not acceptable for the scheduled jobs that must meet tight SLAs.

Running four On-Demand Instances with Capacity Reservations in one Availability Zone and the rest as Spot Instances in the other Availability Zones is not ideal. It introduces risk to availability, as Spot Instances can be terminated, impacting the scheduled jobs that need to meet SLAs. Additionally, having a single Availability Zone with the majority of On-Demand Instances creates a potential single point of failure.

Running On-Demand Instances with a Savings Plan and Spot Instances is not a cost-effective solution. Savings Plans are typically for long-term commitment, which is not aligned with the requirement of a consumption-based model with no long-term commitments. The Spot Instances, while cost-effective, cannot guarantee the required availability for scheduled jobs that have tight SLAs.

Running three On-Demand Instances with Capacity Reservations in each of three Availability Zones ensures high availability and fault tolerance for scheduled jobs. The Capacity Reservations provide guaranteed capacity for critical instances, ensuring that scheduled jobs meet SLAs. Spot Instances in each Availability Zone can handle user jobs, which are less time-sensitive and can tolerate delays. This solution meets the cost and high availability requirements effectively.

2025-10-12

1 Comment

  1. Kathryn
    Author

    I surmise that the answer is:
    Split the 12 instances across three Availability Zones in the chosen AWS Region. Run three instances in each Availability Zone as On-Demand Instances with Capacity Reservations. Run one instance in each Availability Zone as a Spot Instance.

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