10 cloud cost optimization strategies

Transitioning to the cloud is a significant change for many organizations that introduces greater efficiency and scalability. When moving to the cloud, it is important to accurately analyze, interpret, and forecast cloud requirements to ensure the resources are utilized effectively. Luckily, there are several cloud cost optimization strategies that can help minimize costs and maximize efficiency and ROI. 

Cloud cost optimization refers to managing and reducing expenses associated with cloud computing services. It involves implementing various strategies and best practices to ensure that organizations efficiently utilize their cloud resources while minimizing unnecessary expenditures. Cloud cost optimization aims to maximize the value derived from cloud services while minimizing costs.

Cloud cost optimization strategies can be as simple as consistent monitoring or as complex as using automation tools. Most cloud cost optimization strategies can be applied to one or more areas of cloud operations, including:

• Server time

• Storage space

• Databases

• Networking capabilities

• Software packages

• Analytics tools

• Intelligence and ML programs

Cloud cost optimization is crucial because it helps organizations make the most of their cloud investments. By managing expenses efficiently, businesses can prevent overspending, adhere to budgets and maximize the value they derive from cloud services. This optimization enables more efficient financial operations (FinOps), scalability and resource allocation, ultimately helping to improve business outcomes.

For instance, when companies launch a new app, they avoid overspending on resources by implementing cloud cost optimization strategies like monitoring usage patterns and rightsizing resources. By identifying and adjusting areas of overspending, they can maximize cost efficiency and ensure optimal performance and scalability as the app progresses through different stages of development and deployment. 

There are several best practices and strategies that can help teams optimize cloud resources and costs. Some cloud cost optimization strategies are broadly applicable across sectors and can be used for almost any cloud ecosystem, while others are more appropriate for niche applications. 

To balance cost and performance, forecast appropriately and address issues promptly, it’s important to monitor cloud costs as you would any other spend. Fortunately, monitoring is among the easiest and least resource-intensive methods teams can use to align available resources with projected needs.

Cloud cost monitoring lets you see precisely how your cloud resources are being used, by whom and how much they are costing. This gives stakeholders valuable insights into cloud usage and costs, such as identifying underutilized resources or cost anomalies.

The hub-and-spoke model used by AWS helps manage workloads across accounts. This approach centralizes key resources (the hub) while allowing individual teams or departments to operate independently (the spokes), thereby promoting organizational efficiency and scalability. While this model is becoming a common practice, it can complicate real-time cost monitoring due to the distributed nature of resources and activities across multiple accounts.

Ops teams developing in Snowflake can mitigate these complexities through various strategies. For instance, they can set up automated alerts to detect potential cost overruns as they occur. These alerts can be configured to monitor specific metrics, such as query performance, storage usage and virtual warehouse activity, providing timely notifications when thresholds are exceeded. Additionally, leveraging Snowflake's built-in cost management features, such as resource monitors and cost usage reports, can help teams track and control spending more effectively. By implementing these measures, Ops teams can maintain better visibility over their expenditures, quickly address inefficiencies and ensure that budgetary constraints are adhered to, even within the decentralized framework of a hub-and-spoke model.

Rightsizing is one of the most effective cloud cost optimization strategies in cloud computing. At its simplest, this is the practice of aligning resources used to the need at hand.

Rightsizing can address under-provisioning or over-provisioning of resources. 

Under-provisioning happens when too few resources are available, such as when an MLOps team runs out of storage space in the cloud. This can lead to issues like slowed response times, application failures during peak times and constrained resources hindering team efficiency.

Over-provisioning happens when more resources than required are allocated to systems or applications. This can lead to inefficient resource utilization, increased costs, and wasted resources.

Considering the drawbacks of both situations, it’s worth the effort for most projects to map out the resources teams are likely to need and then add a small safety margin for overruns. A project planner might, for instance, decide to pay for medium traffic bandwidth that can accommodate 50-100 site visitors simultaneously. The manager can then project a 20% monthly increase, plus or minus another 10% for unexpected spikes. Any less than this could lead to lag times and denial of service, but paying for more introduces waste.

AWS also offers a payment structure that could almost be thought of as cloud cost optimization services in themselves. These are reserved instances (RIs) and savings plans. Both of these plans offer potentially steep discounts for enterprise-scale users but in different ways.

Reserved Instances (RIs) provide customers with discounts on hourly services in exchange for a commitment to use a specific amount of computing power over a period of 1 to 3 years. 

Savings plans offer discounts by committing to a certain level of spending, measured in dollars per hour, regardless of the specific amount of computing power used. 

For example, in AWS RIs provide a discount for pre-purchased computing power for a set duration, whereas savings plans offer a price cut for customers who commit to spending a certain amount per hour over a set period.

Both reserved instances and savings plans can offer significant savings for customers with consistently high needs, or those whose projected needs are significant enough to plan for in advance. 

Many large enterprises already save money by running certain applications on the public cloud. These are known as instances, such as Amazon’s popular EC2 instance. Blocks of this service can be purchased on-demand or as a spot instance.

Spot instances are services that were previously purchased by other clients that end up going unused. Rather than letting these reserved services go to waste, providers such as AWS may offer them at a discount to customers willing to purchase them in large blocks. Advantages of spot instances include:

• Potentially significant cost savings

• Flexible price structures for many clients

• Resistance to service interruptions from unexpected events

• Predictable use patterns based on advance orders

• Low overhead for team managers and project planners

Storage is a core part of the data management lifecycle and it’s one of the key areas teams can optimize their cloud services. There are several ways to do this:

• Self-encrypting drives (SEDs). SEDs automatically encrypt data stored on the cloud. This is primarily done for security reasons, but routine data encryption also acts as a compression function that reduces demand on storage space for data that’s rarely accessed.

• Secure data erasure. Secure data erasure is a common technique for securely and permanently erasing old data in a way that can’t be recovered. Like SEDs, this is mainly a security feature, but development teams can use it to routinely clean out old or obsolete training sets and unused prior versions of bulky applications. 

• Ongoing data lifecycle management. Data lifecycle management involves categorizing files based on their value to users, as measured by access frequency, and storing them at varying levels of retrieval and disk space efficiencies. Data that’s frequently accessed can be stored in a less efficient but more easily retrieved location, while less commonly accessed files can go into a kind of data deep storage that’s harder to search through but which takes up less overall space.

• Horizontal and vertical scaling. The need for cloud services tends to increase as operations scale up, which drives a lot of the cost increases over time. Teams using cloud services can choose to scale their ecosystem vertically or horizontally. Horizontal scaling involves adding extra components, such as additional servers, to augment the tools already in use. Vertical scaling typically requires expanding the capacity of elements already paid for, such as buying more bandwidth on the system currently in use. 

• Cloud and multi-cloud storage. Multi-cloud strategies are a bit more complex than simple cloud approaches. A true multi-cloud program combines two or more cloud services, usually from different providers, to build out a custom application and services profile. These can both be private clouds, both public or mixed public-private clouds. Taking this approach helps optimize storage and service delivery for organizations with complex needs.

Optimizing cloud costs can, to a certain extent, be automated. Automated platforms scan for the most efficient resources based on historical and projected usage patterns, and adjust utilization accordingly. They can automatically rightsize virtual machines, storage and workloads, and adjust to unexpected interruptions.

Infrastructure as code (IaC) is helpful for this. IaC manages to configure the cloud ecosystem uniformly or according to pre-set requirements set by the team. This helps reduce error, slim down operating margins and eliminate much of the waste that crops up from time to time in even a well-designed cloud computing environment.

Cost variance is a useful accounting tool, and it helps in all areas of budget and resource allocation to identify irregularities. In cloud cost computing, a cost variance might be caused by price spikes, bandwidth limitations or even the geographical location of the physical infrastructure.

The location of cloud infrastructure may not seem like a major consideration at first because internet services cross borders and can theoretically be available anywhere. There is a variance, however, because traffic has to be routed over distance and access requests are partly region-dependent. Long distances can introduce latency lags that add to costs over large data volumes and high traffic flows.

This variance can be partly overcome by leveraging multi-region deployments. An enterprise might, for example, choose to colocate servers in Virginia to handle East Coast traffic, while the main storage servers are maintained in California.

This approach dovetails with the development of content delivery networks (CDNs). These act something like web hosts, except they don’t directly host content themselves. CDNs host content close to end users, which lowers latency, decreases lag time and reduces costly load on streaming and file transfer systems. This approach actually serves the majority of current web traffic, including major streaming platforms such as Facebook and Netflix.

Storage and retrieval are some of the key functions for cloud services, as enterprises move large data files up onto the cloud to take advantage of the economies of scale these platforms offer. There are different ways to organize a database, however, and some are better optimized than others.

Much of the database management enterprises use is a balance between efficient storage and efficient search functions. Cloud service optimization techniques, such as local caching and query optimization, shave milliseconds off of data transactions, which can add up to significant cost savings over large databases and high traffic volumes.

In the early days of cloud services, it was fairly typical to see a single team, or even just one person, in charge of managing cloud resources. This degree of centralization is no longer practical, as cloud service demands have increased for most enterprises. That creates a rift between greater centralization and wide-open distribution of control. To strike a balance, some businesses are adopting a federated data management approach—empowering data stakeholders in various lines of business to manage their own data. 

Capital One shifted to a federated approach by decentralizing data management, giving lines of business control over its own data and treating it like a product. We established clear enterprise standards for metadata, data quality and access based on data sensitivity. We also created centralized tools for self-service data management, like Capital One Slingshot, to streamline governance while enabling teams to own and manage their data. This reduced bottlenecks, improved data access and cut manual processes, saving 55,000 hours of work.

Slingshot gives Snowflake customers an intuitive user interface that enables teams to manage their Snowflake data, while streamlining workflows, improving performance and reducing waste. 

Optimization should be ongoing. Building out a culture of continuous improvement helps identify areas for improvement. It’s also helpful as organizations scale, as the need for cloud services is always changing and expanding.

By curating a culture of continuous improvement, training teams in cost optimization measures and enabling them to elevate or address areas of improvement, organizations can keep cloud cost management front and center. 

Capital One developed Slingshot to help teams balance cost and performance in Snowflake through enhanced visibility, query guidance and warehouse recommendations. :

• Enhanced cost visibility. Slingshot includes detailed dashboards and insights that enable more granular investigation into cost, performance and usage of Snowflake It’s Cost Breakdown Report uses Slingshot tagging capabilities to provide even greater visibility into Snowflake spend across custom categories. Tags can be assigned to objects or to specific users so teams can track Snowflake spend in a way that makes sense for them.

• Continuous monitoring. In addition to granular cost insights, Slingshot also offers proactive alerts so users can stay up-to-date on credit usage or be notified of cost spike anomalies. 

• Warehouse recommendations and dynamic scheduling. Slingshot’s warehouse recommendations can help reduce costs and improve performance in a Snowflake environment by using historical warehouse metadata to make suggestions that rightsize warehouses. Dynamic scheduling also tunes warehouses based on size and scaling policies, sets inactivity controls and more, to increase spend efficiency and reduce data spillage. 

• More efficient queries. Query optimization enhances performance by executing SQL statements in the most efficient manner possible, aiming to minimize the time and resources needed to access data. Well-optimized queries are essential for effective data retrieval and analysis, saving businesses time and resources while ensuring reliable results.

Managing the cost of cloud services, particularly at scale, can be challenging. As the volume and sources of data ingested increase, cost optimization strategies are critical to implement in order to operate efficiently in the cloud. With the best practices outlined in this article and tooling to support them, businesses can identify inefficiencies, allocate resources effectively, and ultimately balance cost and performance.