# Five Strategies for Cutting Public Cloud Costs with IBM Turbonomic

Are you struggling to minimize your public cloud spending? Organizations can leverage IBM Turbonomic, a hybrid cloud cost optimization platform and the “Ops” in FinOps, to make the most of the cloud’s elasticity while effectively managing costs. According to a recent survey, 56% of companies exceeded their public cloud budgets in 2022 by 20% to 30%. To address unexpected cloud expenses, IBM Turbonomic offers five straightforward techniques to help optimize spending.

## Summary
– Proper resource utilization with rightsizing
– Meet demand with Autoscaling
– Managing reserve instances
– Leveraging spot instances
– Eliminate cloud waste through optimization

### 1. Proper Resource Utilization with Rightsizing
With Turbonomic’s AI-based insights, organizations can match workload types and sizes to instance performance and capacity requirements, ensuring costs are kept under control. The platform continuously analyzes application performance and demand to recommend optimal resource configurations, taking into account various factors, such as application performance, virtual memory, CPU, storage, and demand across the infrastructure.

### 2. Meet Demand with Autoscaling
Turbonomic employs machine learning to analyze application performance and demand, enabling real-time recommendations to prevent performance issues, scale resources based on demand, and rightsize cloud resources to avoid overprovisioning. Unlike traditional public cloud tools, Turbonomic focuses on controlling costs while ensuring optimal performance.

### 3. Managing Reserve Instances
Using AI insights and automation, Turbonomic leverages reserve instances by recommending when to use or purchase them, based on factors like current usage of cloud resources, predicted future demand, and the cost of on-demand resources. This streamlines the process, ensuring organizations do not overpay for on-demand cloud resources.

### 4. Leveraging Spot Instances
Turbonomic can automatically launch and terminate spot instances based on demand, enabling organizations to save costs by using these for workloads requiring periodic availability, such as development environments or batch processing jobs. It also forecasts potential savings and identifies spot instance usage to maintain application performance.

### 5. Eliminate Cloud Waste through Optimization
Turbonomic’s AI insights enable the automatic distribution of workloads across available resources, shutting down unused resources and adjusting resources on demand to prevent application performance issues, allowing organizations to run properly utilized workloads in the cloud at the lowest cost.

**Explore IBM Turbonomic Today:**
If your organization aims to reduce cloud waste and cost, IBM Turbonomic can be a valuable solution to achieve true cloud elasticity. Discover how IBM Turbonomic works across your entire cloud and on-prem hybrid environment through the IBM Turbonomic interactive demo.

IBM also presents a comprehensive approach to FinOps with [IBM Apptio Cloudability](https://www.apptio.com/products/cloudability/). Learn how you can operationalize and automate FinOps using Apptio Cloudability with IBM Turbonomic for an end-to-end solution.

Source: 451 Research – Voice of the Enterprise: Cloud, Hosting & Managed Services, Cloud Spending 2023 and Budgets & Outlook 2022

## FAQ
1. What is IBM Turbonomic?
IBM Turbonomic is a hybrid cloud cost optimization platform with AI-based insights that help organizations manage cloud resources with a focus on cost control and performance optimization.

2. How does Turbonomic address cloud over-spending?
Turbonomic offers techniques such as rightsizing, autoscaling, managing reserve instances, leveraging spot instances, and eliminating cloud waste through optimization to help organizations optimize their cloud spending and avoid over-spending.

3. What are some common factors contributing to unexpected spending in the cloud?
Common factors contributing to unexpected cloud spending include scaling resources to address unexpected demand, overprovisioning, lack of resource utilization governance, idle workloads, and failure to take advantage of cloud provider discounts.