This is largely targeted towards startups and high-growth areas. It breaks down into the following:
Again, please note that this is targeted towards startups and organizations that are in a growth trajectory. Once your organization moves out of that startup and growth phase it’s important to shift the perspective in several ways including being very proactive, increasing automation in every way possible, enabling the centralized finops team to make changes, and more. I’ve been thinking about the finops model as it relates to business growth phases and will probably be releasing a blog post on it pretty soon.
A step by step guide to setting up tags and cost allocation tags to report bucket usage in to AWS Cost Explorer. How to Create An Amazon EC2 AutoScaling Policy Based on Memory Utilization Metric in ## Windows AutoScaling groups are a great way of scaling compute resources to meet demand, and to scale them down when they’re not in use thus minimizing unnecessary costs. Both posts come with example CF stacks that will deploy compute and scales appropriately given memory inputs. The appropriate CloudWatch agents must also be installed to capture these memory metrics.
This platform reduces document processing costs up to 60%.
Valid points that we’ve covered before, but are always worth reviewing:
The last post in the unit metric series it looks at the different roles and facets that have an interest in unit metric, such as product owners, marketing, planning. It makes the case that the unit metric can be used as the central point in a data driven organization.
A very hands-on series that, while somewhat seemingly entry-level, will get you up to speed on building and deploying a serverless application.
New updates for April:
This guide walks you through establishing a performance baseline (using a tool called HammerDB) given your deployment and then considering different instance families that are most cost efficient from those finding.s
With EC2 dedicated hosts you can have full Windows Server compliance with SQL server BYOLs. This post goes into some pricing details and examples using this type of set up.
The AWS free tier is fundamentally broken - especially for students and new engineering practitioners wanting to learn the platform.
Great points in here, and things of which you should always be aware:
KOPS is the tool used to spin up Kubernetes on cloud providers (technically, only AWS is supported but DigitalOcean, GCE, Azure, and OpenStack are being developed). There are pricing differences between running k8s on EC2 and EKS and this post outlines those.
Unit metrics are all the rage these days, and this gives some good characteristics.
Pricing drops in many ml-type instance families as well as notebook instances, SageMaker Studio instances, training instances, batch transform instances, and more.
An example of how to integrate Infracost by creating a Jenkins stage to execute it and build the diff output. It’s so important to have infrastructure costs built out as part of your CICD pipeline. Automation is key.
Another data transfer horror story in someone’s personal AWS account. Alerts are so important.
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