Best practice 14.1 – Evaluate the infrastructure usage patterns and choose your payment options accordingly - Data Analytics Lens

Best practice 14.1 – Evaluate the infrastructure usage patterns and choose your payment options accordingly

On-demand resources provide immense flexibility with pay-as-you-go payment models across multiple scenarios and scales. Alternately, Reserved Instances provide significant cost saving for workloads that have steady resource utilization and serverless options for unpredictable demand. Perform regular workload resource usage analysis. Choose the best pricing model to ensure that you don’t miss cost optimization opportunities and maximize your discounts.

Suggestion 14.1.1 – Evaluate available payment options of the infrastructure resources of your choice

Review the pricing page for specific AWS services. Each service will list the billing metrics, such as runtime or gigabytes processed, as well as any discount options for dedicated usage. In addition, many AWS analytics services offer discounted payment terms, Reserved Instances, or Savings Plans, in exchange for a specific usage commitment. Almost all AWS services offer the payment for usage on demand, meaning you only pay for what you use.

Suggestion 14.1.2 – For steady, permanent workloads, obtain Reserved Instances or Savings Plans price discounts instead of paying On-Demand Instance pricing

Reserved Instances give you the option to reserve some AWS resources for a one- or a three-year term. In turn, you will receive a significant discount compared with the On-Demand Instance pricing. Workloads that have consistent long-term usage are good candidates for the Reserved Instance payment option.

Suggestion 14.1.3 – Use either on-demand, spot or serverless resources during development and in pre-production environments

Development and pre-production environments frequently change and often do not require 100% availability. Use on-demand instances with start and stop resources, or serverless resources in cases where workload utilization is unpredictable, frequently changes, or is only used for portions of the day. You can use spot instances for fault-tolerant and flexible big data analytics applications. Spot instances are available at up to a 90% discount compared to on-demand prices. Spot instances are not suitable for workloads that are inflexible, stateful, fault-intolerant, or tightly coupled between instance nodes.

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