If your company uses AWS, chances are you’ve considered developing a cloud optimization strategy. Since AWS resources can infinitely scale, many organizations find themselves in a conundrum where they’re not using these resources efficiently. However, a good cloud optimization strategy could potentially save an organization thousands, if not millions of dollars per year.
Rather than be surprised by one more AWS bill, now is the time to think about your strategy. Here’s where to start.
Use cloud cost as a primary engineering metric.
Many DevOps teams are accustomed to constantly monitoring the status of their applications and infrastructure using application performance monitoring (APM) tools, but they may be missing an opportunity to treat cost monitoring in a similar way. Instead of proactively detecting when there’s a cost anomaly in an application, for example, many teams wait until the AWS bill has already arrived to discover there’s a cost spike.
Rather than retroactively fixing a problem when it’s too late, teams should consider cloud cost as a first-class operational metric for their applications and infrastructure. Beyond correcting accidental overspend alone, proactive cost monitoring can catch and remediate potential scaling issues to future-proof your infrastructure...
Get proactive between finance and engineering.
All too often, finance and engineering teams operate in silos, and don’t communicate until after an issue is already discovered with the cloud bill. Instead, a proactive cloud optimization strategy should involve both finance and engineering. Some decisions, such as negotiating advanced purchases of RIs or discounts, should fall into the hands of finance, while others should be based on real-time cloud cost data.
Finance and engineering should share a single source of truth on cloud spending to understand unexpected cost spikes, infrastructure provisioning, and other engineering decisions that impact cost. What’s more, a single source of truth will create more proactive, ongoing and strategic conversations between finance and engineering, instead of a reactive rush to correct mistakes.
Ensure that your cloud optimization strategy is flexible to theintegrated with the SLDC.
Unfortunately, unlike the server provisioning days of old, it’s nearly impossible to set a fixed budget for cloud costs. That’s why your cloud optimization strategy should be flexible, and mapped back to each stage of the software development lifecycle. Cost data should inform the entire product roadmap and technical debt-related decisions. For example, teams should be able to:
Planning stage: Justify the budget they need using cost data. Reduce unexpected spend and quickly adjust the budget when necessary.
Deployment and operation stage: Quickly identify unpredicted spend and adjust.
Design and build stage: Have the data they need at-hand to make cost-effective architecture decisions. Report on planned spend and understand their cost of goods sold (COGS).
Monitoring stage: Reassess cost by team, product, or feature and report on operational expenditures and ROI, segmented by business initiative.
Correlating real-time cloud cost data with engineering data could mean the difference between veering off budget course entirely, or making in-the-moment adjustments that are best for the organization as a whole. Ideally, a proactive cloud optimization strategy will help reign in AWS costs and architect more efficient systems. To make the job simpler, tools like CloudZero provide teams with real-time cost data and a single source of truth to inform their strategic decisions.
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