Reducing cloud costs doesn’t need to be a big or complex consulting project. The key steps to follow:
Discover and understand your cloud infrastructure. Because cloud resources and services can be provisioned in just a few clicks or API calls, it’s easy to quickly lose track of what is deployed. Tools that can
automatically discover and map your infrastructure can help you get that full picture, not only showing information about instance counts and summary costs across all of your cloud accounts but also providing the ability to look at that information organized by tags, pods, clusters, services, and applications.
Assess and prioritize savings opportunities. Mapping your cloud infrastructure footprint can leave you with an overwhelming amount of information. To identify the best places to reduce costs, it is important to balance the potential savings against the complexity and resources required to realize those savings. A systematic approach that assesses compute, storage, and network infrastructure is crucial. Modern tools that calculate potential savings, not just summarize current costs, are important to leverage in this process.
Take steps that reduce costs today and in the future. One-time cost reductions are often the first step taken to save money, however even more important is ensuring that cloud infrastructure costs are kept under control in the future as well. Without that, inefficiency is almost certain to increase again until the next cost reduction project.
Compute infrastructure is typically one of the largest parts of an organization’s cloud bill, and as a result often the source of the most significant opportunities to reduce costs. Here are key ways to quickly reduce those costs.
Identify idle compute resources
Provisioning in the cloud is easy but risky without proper governance in large teams. It's common to over-provision resources and make deployment errors, leaving them unused or orphaned. To find such resources, use cloud management tools that analyze metrics like network traffic and CPU load. Advanced tools with automation can continuously monitor and shut down unused resources. "Showback" tools revealing costs by instances, tags, pods, clusters, etc., are also valuable for cost transparency.
Optimize purchasing strategies
Cloud platforms offer diverse pricing models for compute resources, including on-demand, reserved, convertible reserved, preemptible, spot instances, and Savings Plans. Prices vary based on factors like region and pricing plan. Intelligent purchasing, like using reserved instances (up to 75% cheaper) or spot instances (up to 90% cheaper), can yield substantial savings. Implementing tools to identify suitable options and continuously optimizing pricing mixes can achieve significant savings without disrupting infrastructure.
Right-size your compute resources
Traditional capacity planning challenges persist in the cloud due to overprovisioning and the need for buffer capacity. This is especially true in container infrastructure, where inefficient bin packing leads to excess resources. Monitoring tools can assess actual resource usage, aiding in workload support and future predictions. With this data, resources can be accurately sized and deployed at optimal times, enhancing infrastructure efficiency.
Release unneeded storage capacity
Cloud storage's ease of provisioning and limitless nature often leads to unnecessary capacity. Oversized volumes, unused allocations, orphaned volumes, and unnecessary snapshots are common issues. Cloud vendors and third-party utilities can swiftly identify and remove orphaned volumes and snapshots, regardless of the platform. Usage data aids in right-sizing overprovisioned volumes, and reviewing snapshot retention policies ensures obsolete storage snapshots are not needlessly retained.
Leverage storage tiering
Cloud storage options vary in latency, throughput, and cost. Choosing the right storage tier and transitioning data appropriately can significantly cut cloud storage expenses. For active data, high-performance options like solid-state disks (SSDs) work, while "warm" data can use lower-cost spinning disks. Object storage suits less latency-sensitive needs, and cold storage tiers are ideal for long-term archives. Cloud platforms offer storage lifecycle policies to automate data migration between tiers, optimizing costs efficiently.
Align storage redundancy with requirements
Cloud storage services like Amazon S3 and Azure Blob Storage provide customizable redundancy options. Applications often offer their own data redundancy. Opting for reduced redundancy configurations for non-critical data can cut storage costs significantly – for instance, reduced redundancy object storage can be up to 50% cheaper than standard options.
Reduce traffic across zones and regions
Overlooking network traffic between datacenters can lead to substantial cloud costs, especially between regions or availability zones. Redundancy and easy service provisioning can inadvertently generate significant, unnecessary data transfer costs. Rebalancing services across zones intelligently can reduce cross-datacenter communication without compromising resiliency, enhancing performance. Network tracing tools can identify misconfigurations causing needless traffic, enabling quick reductions in cross-datacenter communication costs.
Optimize network configurations
Network configuration greatly influences data transfer costs in the cloud. Modifying how traffic is routed, such as opting for private IP addresses over public or elastic ones, can substantially reduce costs without affecting available throughput.
Deploy distribution and caching solutions
Applications often request the same data from cloud services, leading to increased costs, especially for large datasets or media objects. Repeated data transfers, like those from Amazon S3, can drive up expenses based on location and configuration. Implementing content distribution networks and caching services can cut costs by reducing repeated transfers. However, it's crucial to assess the costs of these solutions to determine potential savings before deployment.
These best practices are critical to ensuring that you are continuously operating as efficiently as possible in the cloud:
Use machine learning and analytics for smarter cost management
While there's no shortage of tools for generating charts, graphs, reports, and alerts, having access to too much data can lead to information overload. SRE and CloudOps teams can become overwhelmed with data and false alarms. Modern tools that employ machine learning and artificial intelligence are valuable as they can learn resource usage patterns, allowing them to identify genuine anomalies and alert you only to changes that genuinely warrant closer examination, reducing false alarms.
Schedule resource usage
Anticipated business events, like end-of-quarter processing or Monday morning activity, drive predictable utilization changes for applications and services. Scheduling scaling and resource allocation in advance based on this knowledge ensures resources are available when needed. Moreover, it prevents resources from running idle and accruing costs when they're no longer required, effectively optimizing costs.
Continuous application of best practices demands automation. Overprovisioning due to concerns about scaling speed can be mitigated by intelligent automation. Automated solutions can detect the need to scale automatically and act based on pre-configured rules, reducing delays and the necessity to overprovision infrastructure. This efficient process addresses overprovisioning costs effectively.
Reducing cloud costs with Spot
Managing cloud infrastructure costs requires continuous effort and diligence, which can strain CloudOps teams. Spot offers a suite of products using unique machine learning and analytics. These tools monitor cloud workloads and resources, providing visibility, guidance, and automation to optimize costs without compromising availability, performance, or flexibility. Spot's solutions work across major cloud platforms, ensuring optimized infrastructure for various applications, including containers, Kubernetes, and autoscaling applications.