Cloud computing costs can spiral if they are not managed from the start. Numerous short-term and long-term cost optimization strategies for cloud configurations can help keep budgets in line.
Start with choosing the right provider. There are different ways to run an application: hosted on VMs on a service, containerized, or hosted in a serverless computing environment. Each has varying cost and management complexity. The trick is to find the right balance between cost and enterprise needs. Apply the following considerations:
- Determine how much redundancy your application needs.One way to achieve cloud redundancy is to pick a hosting option that distributes workloads across multiple data centers within a region. This is a low-cost strategy but has the least amount of redundancy. Another way is for users to mirror workloads across more than one region, which offers more redundancy but at a higher cost.
- Determine the appropriate size and scale for your installation.Tools can help identify a more efficient --- meaning less expensive -- VM instance for the workload you want to run. Reserved instances cost less than on-demand VMs, though they must be booked in advance. Preemptible instances are cheapest but risk interruption by the cloud service provider, so they aren't a fit for consistent workloads that require uptime. Autoscaling, typically part of a cloud vendor's overall framework, can increase or decrease resources as demand shifts.
- Minimize data movement.Cloud providers charge for data egress. If you move data frequently, choose the appropriate cloud services setup for that. Also, recognize that moving data can increase security risks.
- Consider third-party tools.Third-party cost-management tools may offer better capabilities for management, monitoring and security than a cloud platform's native services. They also tend to work in multi-cloud environments.
- Look to advanced technologies for assistance.Cloud management can be tricky, even if you do everything right. Some users and experts believe artificial intelligence and machine learning can efficiently and significantly reduce cloud costs. Vendors already offer tools that incorporate capabilities to scan cloud workloads, quickly detect anomalies and alert administrators about an issue that might affect the cloud bill.
Cost management challenges
Detailed information about cloud costs may not be easily accessible. A customer might search across regions, accounts and numerous attached cloud services to calculate the total cost for just one individual service, such as backup snapshots.
The COVID-19 pandemic and related economic factors spurred enterprises to move more workloads to the cloud, which underscores the need for cost optimization practices.
AI tools and machine learning supplement the actions of humans but don't replace them. Software can identify additional information that staff may miss, but people must collaborate when analyzing cloud cost strategies and make judgment calls based on resources and experience. In-house staff should know how cloud usage affects the bottom line, both in IT and business lines.
