The hidden costs of cloud services and cloud computing
For a relatively low up-front investment, businesses of every size can buy into secure storage, advanced monitoring and cloud-native apps from dedicated remote servers. While migrating to the cloud has been productive for many businesses, it's not as simple as a yes or no decision. Many of the data costs for platforms have the potential to negatively affect the profitability of switching over, and this influences the decision of how much to invest in the cloud for even the largest organizations.
Some costs of cloud computing services are more obvious, like the costs of bandwidth, tech support and licensing agreements, but others are not. It's important to account for all of these variables when deciding to move to or operating in the cloud. Sometimes data platform inefficiencies can result in less visibility into these costs, but this can usually be addressed with a few best practices and optimization strategies.
Calculating the costs of cloud services
Cloud costs, visible and hidden, can add up quickly, depending on the services and scale required. Fortunately, many cloud services are offered à la carte, which helps manage data costs for companies that only need limited services. In general, the biggest expenses are for storage, compute, data transfers and advanced or managed services. These costs can scale as demands increase but they don't always scale proportionately.
Cloud services are often available as stand-alone options. A company may buy into storage but keep its own processing and applications or invest in faster processing and secure encryption while running its own troubleshooting and data management apps. For this reason, organizations have a lot of control over costs. To maintain that control, it’s important to identify the cost factors that contribute to the final price of cloud services.
Identification of cost factors
While cost factors vary, customers can develop an idea of price structure with an easy formula before agreeing to a cost structure.
The formula is: total cloud computing costs are equal to the services agreed to multiplied by the unit cost and the total volume used or usage.
How to estimate and forecast cloud costs
Cost estimation and financial forecasting aren't new concepts. Many of the same forecasting techniques used for the payroll and accounting departments also apply to forecasting the costs of cloud services. These include:
- Historical analysis: The past can be a guide to the future. The price per unit paid in the past can be a useful guide to ongoing and future pricing. By taking historical costs for certain services, allowing for reasonable increases over time and adding the similarly enhanced costs of additional or expanded services, companies can create a rough guide to future costs. This isn't definitive, but it can be a decent guidepost for what the ballpark expenses should be.
- Premium costs: Premium services come with extra costs. These may include faster processing, extra backups and guarantees of low downtime, and they all add to the cost of a cloud service package. Upgraded and premium applications are also major sources of enhanced costs. A licensed or proprietary software package, such as a high-end encryption or security package, is likely to drive up costs.
- Scaling considerations: Once an organization makes the move to the cloud, the ease and expanded capabilities these services offer encourage ever-greater reliance on them. Scaling up this way increases the demands on the budget in real terms, but the per-unit cost for services does tend to decline with larger scale service packages where bulk discounts are generally available.
Ongoing monitoring and optimization
Estimates are a good place to start managing costs, but ongoing monitoring is a crucial part of operational cost containment. Without some mechanism to evaluate actual costs against projected costs, it's easy to let expenses run away and drive expensive cost overruns. There are several ways to work monitoring into the cost model for cloud services, most of which can be combined to provide full-spectrum accountability and prevent unaccounted-for costs from piling up and becoming unwieldy.
- Cloud provider tools: Many of the tools development teams use to control cloud costs are provided directly from the cloud service. For example, AWS is an industry leader in cloud services and applications, and the platform provides monitoring and tracking services that assist in ongoing expense management.
- Third-party tools: While cloud provider tools are useful, third-party providers also offer helpful tools for cost tracking and optimization that can provide even greater visibility and control. Some of these are stand-alone apps available to any user, while others are provided by other cloud services and compatible with common platforms. Capital One Slingshot, for example, gives Snowflake users a more granular view into their Snowflake data costs and allows users to identify where they can optimize those costs.
- Resource tagging: Many of the hidden costs for cloud services are found in minor or unaccounted-for applications that aren't being adequately tracked. For example, a security suite that charges on a per-use basis might be getting more use than expected. Tagging this resource and getting an alert when it's used can help boost awareness and inform policy changes to contain the cost.
- Rightsizing: It's important to ensure the use of cloud resources aligns with the cost. Otherwise, teams pay for functionality that might never get used. For instance, investing in a comprehensive suite of cloud computing capabilities, such as storage and processing power, may seem beneficial, but if the team only requires a fraction of these resources, it can lead to unnecessary expenses. Instead, by scaling and optimizing cloud resources to match actual usage patterns, organizations can maximize efficiency and cost-effectiveness. This might involve dynamically adjusting resource configurations, leveraging auto-scaling capabilities or utilizing cost-effective instance types, such as spot instances, to better align with workload demands and optimize expenditure.
- Reserved instances: Organizations with predictable usage patterns can sometimes save costs with reserved instances. These are agreements to use a specified amount of a given resource, such as bandwidth, in exchange for a discounted rate. The predictability and reliability this offers justify the discount, and most large cloud providers are willing to offer them.
- Automatic scaling: Auto-scaling is ideal for less predictable usage patterns. With automatic scaling, the resources paid for vary with the actual use patterns, rather than by purchasing services in bulk and hoping the projection was correct. The per-unit cost of this can be higher than other arrangements, but for teams with low or variable usage, it can trim down the final cost.
- Cost allocation reports: Effective cost allocation reports break down services paid for by the department, project, teams or the resources being used. These provide a window into how the budget is being used, and the granularity of some reports can reveal obvious areas for lowering cloud costs.
- Cloud cost management consulting: When all else fails, hire an expert. Outside data and accounting professionals that can assess the entire cloud project, identify areas for optimization and deliver a final report with helpful suggestions for effective cost management are available for any organization.
What are the hidden costs of cloud computing?
The huge variety of cloud services available makes it hard to generalize the costs organizations are likely to pay for them. Most are familiar with the data storage and bandwidth costs, but these only scratch the surface. Data warehousing inefficiencies, inefficient queries, bloated datasets and more all contribute to the overall cost of cloud computing.
Warehouse inefficiencies
Inefficient data warehousing imposes costs of its own. There's the direct cost of storing more data than what’s valuable , such as old or outdated datasets, and the opportunity cost of poor performance, high latency and inefficient search and retrieval operations. Data warehouse inefficiencies can include:
- Redundant data
- Outdated data
- Poorly indexed data
- False data
- Corrupted data
- Compromised data
- Misclassification of data
Query overload
Query overload describes the problems that grow out of having excessive or inefficient querying practices with data stored in the cloud. Overly complex or inefficient queries strain the network and incur extra costs from several issues, including:
- Increased resource consumption beyond what's strictly necessary
- Reduced performance issues that hinder development operations
- Suboptimal query results, which may call for a second or third query at cost
You can solve for query overload and manage costs by implementing a few best practices. Following industry-standard practices can empower teams to efficiently address this challenge and keep costs low:
- Query optimization: Efficient queries are brief, specific and highly efficient in finding the requested data. Training teams to properly structure their queries to remote servers can have a big impact. Even a slight improvement here can generate significant savings across long-running projects and organizations.
- Caching: Some data tends to get queried a lot, which means it doesn't need to be dredged up with a remote search every single time. By caching results locally, a team can make certain high-demand information more readily available and reduce the number of queries that need to be run, especially by teams working in parallel.
- Utilization monitoring: Using the tools available on the teams' cloud platform, project managers can implement utilization monitoring to track usage across operations. This gives early warning of cost overruns, and it identifies frequently occurring inefficiencies that should be ironed out.
- Appropriate scaling: Scaling resources up or down based on need (such as environment, day of the week or time of day) reduces waste by only using resources when they’re needed.
Dataset bloat
Datasets held in cloud storage grow with time, and they don't always grow efficiently. The accumulation of outdated and obsolete information, or of valid files that are rarely used, is called dataset bloat. This issue can be the result of poor data management, lack of adequate governance or not having data life cycle management policies. At a large scale, it inflates the cost of cloud storage beyond what was originally budgeted. It can also impair efficient search and retrieval functions, since every query has more information to sift through to return results.
Fighting bloat is sometimes as simple as regular cleanup and archiving. Eliminating duplicate files and obsolete versions of current data is also helpful. Whatever the case, proactively managing dataset bloat can help optimize cloud costs.
Data egress charges
Moving to the cloud comes at a cost, but moving off can also be expensive. Data egress charges are fees charged by a cloud provider for moving data off the platform. On a small scale, this might not be enough to notice, since per-unit costs of downloading data to local storage hover around $0.09 per gigabyte. That's about 1,000 minutes of MP3 audio for 9 cents. A large AI training set, however, might run past 50 terabytes, while a company's entire dataset could reach the petabyte range. At 9 cents a gig, moving one petabyte to local storage can cost $90,000.
Use Capital One Slingshot to track and optimize Snowflake costs
As a large financial institution that’s all in on the cloud, we’ve had a lot of experience managing the costs of cloud services, especially in a highly regulated environment with high demands on storage, security and cost containment. We adopted Snowflake to help us manage and scale our data ecosystem efficiently.
For even greater control over costs, we built Capital One Slingshot as a tool to help us manage our Snowflake data more effectively at scale. Other businesses can now use Slingshot to balance cost and performance in Snowflake.
Gain actionable insights
Slingshot allows you to monitor spend at a granular level with greater predictability. Detailed dashboards and actionable insights provide enhanced visibility into your cost, performance and usage of Snowflake. Teams can track costs by line of business to understand where the most resources are being used and then allocate those resources efficiently.
Continuously optimize performance
Enhanced visibility allows teams to continuously optimize their use of cloud resources. For example, Slingshot's Query Advisor helps identify inefficient queries in Snowflake so they can be tuned for better performance. Similarly, warehouse recommendations use historical usage patterns to suggest new schedules that can rightsize warehouses for more efficient usage.
By leveraging these features, companies can proactively manage Snowflake cloud costs, minimize query overload and optimize warehouse usage, maximizing their Snowflake cloud investment. Additionally, Slingshot's flexible permissions and scaling capabilities empower team managers to adapt resources dynamically to meet project needs.
Confidently control data in the cloud
Slingshot's proactive alerts and automated governance workflows put powerful tools in teams’ hands for managing data in the Snowflake cloud. This lifts responsibility away from dev teams, who are now free to focus on engineering challenges and core competencies, without sacrificing the monitoring and cost containment options.
Using Capital One Slingshot for Snowflake cost optimization
Cloud services are an increasingly important and expensive part of operating any organization. As the workload grows, so do costs, but proper data management and the use of appropriate tools can help control the hidden costs of cloud services and cloud computing in Snowflake. Learn more about how to optimize Snowflake costs with Capital One Slingshot.