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Answers to the 5 Most Common Cloud Cost-Optimization Questions

Roman Yegorov

Solutions Engineer, Intel Granulate

In the constantly changing world of cloud computing, businesses are on the hunt for ways to enhance efficiency and reduce expenses. At Intel Granulate, we frequently engage in discussions about cloud cost optimization, a term that’s gaining momentum but often leaves many wondering about its practical implications for their operations.

Cloud cost optimization isn’t just about cutting expenses; it’s about achieving a balance where you’re investing smartly in cloud resources without sacrificing performance. Here are the questions we most frequently encounter, along with insights that could transform your approach to managing cloud costs.

Databricks optimization

1. What Best Practices Can We Use to Optimize Our Cloud Infrastructure for Reduced Cost?

The journey to cost optimization starts with scrutinizing your current cloud infrastructure. Key strategies include:

  • Rightsizing resources: Ensuring your cloud services match your actual needs can lead to substantial savings. When using Kubernetes, you can employ pod resource rightsizing, instance resource rightsizing or both, depending on your needs. For more on this, consider exploring autonomous rightsizing solutions so there isn’t too significant a demand on your team’s time and energy.
  • Identifying underutilized resources: Tools and practices for monitoring cloud usage can reveal idle resources that are adding unnecessary costs. Cloud native code profiling is more important than ever to help applications run at peak efficiency and improve the customer experience while saving time and resources.
  • Exploring pricing models: Switching to reserved instances or considering spot pricing can significantly cut costs. Doing your research on cloud pricing models can pay huge dividends toward making the most of your existing cloud infrastructure.

2. How Can We Leverage Automating Scaling Policies to Manage Cost Effectively?

There can actually be a hefty price tag for not using automation in your scaling and optimization strategies. Implementing scaling policies that adjust to real-time demand ensures you’re not overprovisioning.

Technologies like KEDA (Kubernetes Event-Driven Autoscaling) exemplify how event-based scaling can lead to more efficient resource use. There are a number of best practices for automated scaling for Kubernetes, and it’s worth it to explore methods for improving data platform autoscalers as well.

3. Which Tools Increase Cost Visualization and Mark the Cost-Reduction Journey?

Understanding where your funds are going is the first step toward optimization. Cost-visualization tools break down expenses by cloud components, allowing for targeted strategies to manage expenditures. Investigating cost-management tools can offer strategies for better financial oversight.

Webinar Optimizing Databricks with Deloitte

4. How Can We Automate Cost Savings in the Cloud?

Automation can also extend to cost-saving measures. This includes:

  • Autoscaling: Matching resource usage with demand not only enhances efficiency but also optimizes costs.
  • Policy-driven resource management: Implementing off-hour shutdowns and automatic resource clean-up can lead to significant savings. For more on this, explore how automation can support efficiency in hybrid cloud environments.
  • Predictive analysis: Using tools that forecast resource needs can help allocate cost-effective resources like spot instances during peak times, ensuring SLA compliance without overspending.

5. How Can Improving Performance Lead to Cost Reduction?

Enhancing performance doesn’t just lead to faster, more reliable services; it can also reduce costs. Tools that offer autonomous performance improvement and continuous rightsizing, especially for Kubernetes or big data clusters, not only ensure optimal operation but also bring down costs. Investigating performance-optimization tools could reveal new opportunities for savings with a high return on investment.

This was originally published on The New Stack.

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