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Cloud Cost Management Guide

The cloud offers organizations unsurpassed opportunities to improve the agility, scalability, and cost-effectiveness of their digital operations. However, recent research suggests that only 30% of companies know exactly what they’re paying for in the cloud, which makes it difficult to keep costs in check or ensure operational efficiency. Various factors like under-utilized cloud resources, surging data growth, and hidden fees all contribute to a company’s inability to plan and manage cloud budgets.

Cloud cost management (CCM) refers to a collection of tools and strategies organizations use to reduce cloud spending and improve efficiency. This guide explains what cloud cost management is and why it’s essential to businesses before discussing the best methods and technologies to help reduce cloud expenses. 

Table of Contents

What Is Cloud Cost Management?

Cloud cost management (or cloud cost optimization) involves analyzing cloud infrastructure, services, and applications to ensure a company is only using the resources it actually needs. CCM teams eliminate or reallocate resources as needed to reduce costs and better support the business. In addition, cloud cost management aims to optimize cloud-based operations to improve performance, reliability, and workload efficiency, all of which give organizations a better return on their cloud investment.

Why Is Cloud Cost Management Essential?

Most organizations move valuable applications, workloads, and data assets to the cloud to increase the agility and speed of creating new products and services, especially those powered by data. They also migrate to ensure adequate storage capacity and accessibility for large and fast-growing data stores. In addition, many companies leverage cloud services to save money on infrastructure, enable efficient growth, and align spending with value via OPEX (operational expenses), unlike CAPEX (capital expenses).

Regardless of your brand’s motivation, the inevitable recurring cloud costs may still be unpredictable because of auto-scaling or the ability to add new features to your cloud service with the click of a button. Other factors that contribute to ballooning cloud costs include:

  • Under- or mis-utilized cloud resources.
    Allocating more CPU power, storage capacity, or features than needed for a particular workload, and then purchasing more for new instances instead of reallocating existing resources.
  • Poor data governance.
    Paying to store redundant or irrelevant data in cloud data lakes because you lack the tools or policies for identifying, categorizing, losslessly compressing, and intelligently deleting cloud data.
  • Hidden fees/complex billing.
    Adding cloud features without understanding all the fees involved, having different payment schedules for each cloud vendor or service, and lacking the ability to predict usage fluctuations.
  • Shadow IT.
    When individual departments or users purchase the cloud services they need without notifying IT or researching whether an existing tool could handle their workflow. 
  • Surging data growth.
    Continuously generating and storing more data in the cloud, especially in data lakes and data mesh environments for business intelligence analytics and AI/ML training use cases, inflating monthly data storage costs.

Most organizations move valuable applications, workloads, and data assets to the cloud to increase the agility and speed of creating new products and services, especially those powered by data. Cloud migration also helps ensure adequate storage capacity and accessibility for large and fast-growing data stores. In addition, many companies leverage cloud services to save money on infrastructure, enable efficient growth, and align spending with value via OPEX (operational expenses), unlike CAPEX (capital expenses).

Regardless of your brand’s motivation, those inevitable recurring cloud costs may still be unpredictable because of auto-scaling or the ability to add new features to your cloud service with the click of a button. Other factors that contribute to ballooning cloud costs include:

  • Under- or mis-utilized cloud resources.
    Allocating more CPU power, storage capacity, or features than needed for a particular workload, and then purchasing more for new instances instead of reallocating existing resources.
  • Poor data governance.
    Paying to store redundant or irrelevant data in cloud data lakes because you lack the tools or policies for identifying, categorizing, losslessly compressing, and intelligently deleting cloud data.
  • Hidden fees/complex billing.
    Adding cloud features without understanding all the fees involved, having different payment schedules for each cloud vendor or service, and lacking the ability to predict usage fluctuations.
  • Shadow IT.
    When individual departments or users purchase the cloud services they need without notifying IT or researching whether an existing tool could handle their workflow. 
  • Surging data growth.
    Continuously generating and storing more data in the cloud, especially in data lakes and data mesh environments for business intelligence analytics and AI/ML training use cases, inflating monthly data storage costs.

Cloud cost management strategies

The following cloud cost management strategies help organizations reduce inefficiencies and lower their recurring cloud costs.

Cloud Cost Management Strategies

Right-size your cloud resources to reduce usage

Cloud compute resource utilization (a.k.a. CPU and GPU power) is typically one of the largest cloud expenditures, so any inefficiencies can significantly increase your recurring cloud costs. For example, IT teams often provision new cloud instances with more compute power than they need for a given task, which quickly increases the hourly unit cost above what is actually required. Many teams also forget to stop old instances or purchase new instances rather than repurposing unused ones, all of which drives the total costs up.

 

Right-sizing your cloud instances involves identifying and eliminating utilization inefficiencies so CPU and GPU resources can be reallocated or stopped to reduce recurring costs. Although IT teams could track cloud utilization manually, such inefficient practices are prone to human error. Using a cloud cost management tool helps automate this process with features like resource discovery and utilization monitoring.

 

Take advantage of commitment-based discounts

Reserved instances (RIs), Savings Plans, and other commitment-based discounts allow customers to reserve a certain capacity in a specific region for a pre-determined length of time (usually one or three years). These discounts offer significant savings without changing your underlying infrastructure, which makes them a great top-line strategy for cost reduction. However, long-term commitments come with downsides, like being locked into a cost schedule even if a provider cuts prices, and no options to upgrade or scale on-demand. AWS allows customers to sell off their RIs on Amazon’s Reserved Instance Marketplace, where other companies can buy RIs at an even steeper discount.

Losslessly compress data for data lake cost reduction

Cloud data lake storage presents another major cloud cost. Surging data growth - when combined with automatic scaling - can make recurring cloud bills extremely unpredictable. Quickly identifying the most important data is extremely challenging, especially in data lake and data mesh environments. Many teams just don’t have the time or technology to sort through volumes of data with the required precision to maintain AI training quality or meet compliance requirements. Companies often choose to err on the side of caution and simply keep everything.

Data compression decreases cloud data lake storage costs by losslessly reducing the physical size of data files. This has the effect of lowering the effective unit cost of cloud data lake storage, while keeping data in its existing tier. With data compression, organizations can keep data in the “Standard” tier, with full availability SLAs, and still manage costs down. Compression has other potential benefits, like improved performance for I/O bound applications, faster data transfer, and lower carbon footprint.

However, the act of compressing and decompressing data as it is read takes time, and also costs compute. Most off-the-shelf compression tools aren’t optimized for speed, cost, and scale in the cloud, so it’s important to use enterprise-grade lossless compression tools to maintain scalability and performance.

Use data tiering to further reduce data lake storage costs

Sometimes, data compression alone isn’t enough to reduce cloud data lake costs to the desired level. The practice of data tiering prioritizes data according to urgency and importance and moves it to the appropriate storage location. For example, the data you’re actively using for AI training and analytics should stay in expensive “hot” storage, whereas data for compliance or other recordkeeping purposes can be moved to less-expensive “cold” storage.

 

Some cloud cost optimization tools include data tagging features to streamline this process, but high-volume use cases, like data lakes for AI/ML, should consider a specialized data visibility solution for greater accuracy and efficiency.

Some cloud providers offer automated data tiering, such as AWS S3 Intelligent Tiering, which moves data based on the time since the last access. However, lower-tiered data has lower availability than “Standard” data, which is why many organizations want to keep as much data as possible in the Standard tier. Standard storage costs more, though, which is why data tiering should be paired with compression to bring those expenses down.

Track cloud spending across departments

Many cloud services are user-friendly enough that individual departments can purchase and deploy them without any intervention from IT teams. This scenario is known as “shadow IT,” which often creates the following risks and inefficiencies:

  1. When cloud services aren’t properly configured or onboarded, IT teams can’t patch vulnerabilities, monitor for potential breaches, or deploy security tools to protect them.
  2. A department might purchase a new tool when the company already pays for a cloud service that provides the same capabilities.
  3. IT teams may have an incomplete understanding of how much the company truly spends on cloud infrastructure and services, which makes it difficult to create accurate budgets or implement effective cost-management strategies.

To fully realize the efficiency and cost-saving benefits of cloud cost management, you need a way to track cloud usage and spending across departments. Cloud discovery tools can help by identifying all the applications and services in use by an organization. Some cloud cost management platforms also offer features like cost allocation tags so IT can track cloud spending by department, workload, or other helpful categories.

Cloud cost management tools

Organizations can choose from a variety of available software tools and platforms to assist with the cloud cost management strategies listed above. There is no single platform that offers every component, so many companies combine two or more tools to cover all the capabilities they require. Below, we list some examples of the most popular cloud cost management tools and the features they provide.

Cloud Cost Management Tool Comparison

Platform

Features

Granica

  • Lossless data compression
  • Cloud data access operation optimization
  • Cloud data visibility
  • Cloud data privacy

IBM Apptio Cloudability

  • Cloud cost allocation
  • Cloud budgets and forecasting
  • Resource identification/tagging
  • Business mapping

AWS Cost Explorer

  • Cost allocation
  • Cost and usage forecasting
  • Business insights
  • Customized dashboards and reports

Datadog

  • Cloud cost allocation
  • Cost validation
  • Cloud resource monitoring
  • Automatic cost and performance alerts

Google Cloud Cost Management

  • Budgets with automatic alerts
  • Resource hierarchy and access control
  • Reports and dashboards
  • Recommendations and insights

NetApp Cloud Volumes ONTAP

  • Software-defined cloud storage
  • File & block data management
  • Cloud data replication & migration
  • DR validation

VMware Tanzu CloudHealth

  • Resource right-sizing and management
  • Cost allocation and chargeback
  • Budget management and forecasting
  • Reporting and dashboards

Granica

Granica logo

Granica is a data management platform that helps reduce cloud object storage costs for large-scale analytics and AI training data sets in data lake and data mesh environments. Granica offers three cloud cost management tools for Amazon Web Services (AWS) and Google Cloud Platform (GCP):

Granica Crunch - The world’s only data lake compression service, which uses novel compression and deduplication algorithms to losslessly reduce large scale data sets, especially AI training data.

Granica Chronicle AI - A data visibility service that works with Amazon and Google data lakes to provide actionable insights for greater cost optimization as well as enhanced security.

Granica Screen - A data privacy service that helps identify and protect data PII and sensitive information  in cloud data lakes to reduce security risks and unlock more data for safe use in downstream AI workflows.

IBM Apptio Cloudability

The IBM Apptio logo

The IBM Apptio Cloudability platform provides cloud cost allocation and optimization features like resource identification, cloud cost allocation tagging, budgeting and forecasting, and dashboard visualizations. Apptio works across multiple platforms, including AWS, GCP, Microsoft Azure, and Oracle Cloud Infrastructure (OCI), and also supports integrations with Atlassian Jira, Datadog, and PagerDuty.

AWS Cost Explorer

The Amazon Web Services logo

AWS Cost Explorer works within Amazon cloud environments to help visualize, manage, and forecast cloud expenses. 

Datadog

The Datadog logo

Datadog’s Cloud Cost Management tool works in AWS and Azure clouds to identify, allocate, and visualize cloud expenses. An add-on Container Monitoring service extends cloud cost management to Kubernetes clusters in AWS.

Google Cloud Cost Management

The Google Cloud logo

Google’s Cost Management feature provides granular cloud cost visibility and control for GCP environments. The tool includes the Recommender service that provides recommendations and insights for cloud resource utilization.

NetApp Cloud Volumes ONTAP

The NetApp

NetApp Cloud Volumes ONTAP is a software-defined storage product for managing cloud-native block storage costs. Cloud Volumes ONTAP helps identify and delete unused/unattached volumes to reduce costs and improve performance for native cloud storage.

VMware Tanzu CloudHealth

The VMware Tanzu CloudHealth logo

VMware Tanzu CloudHealth (formerly VMware Aria Cost Powered by CloudHealth) provides intelligent cloud cost, data governance, and resource management insights across AWS, Azure, GCP, and OCI environments.

Cloud Cost Management With Granica

The preceding tools and strategies can help your business reduce overall cloud spending, operate more efficiently, and support sustainable growth and innovation. While other cloud cost management platforms on the list focus on allocating cloud costs and optimizing compute resource utilization, they leave a big gap when it comes to large-scale cloud data management.

The Granica platform fills this gap with a suite of data visibility, compression, and privacy tools for large-scale cloud data. Granica’s primary focus is reducing unstructured AI training data in AWS and GCP cloud data lakes to help decrease surging cloud data storage costs by up to 80%. Our lossless data compression algorithms, AI-powered cost optimization features, and ML PII classification and masking technology can help you meet your cloud cost management goals while improving data quality. 

Request a free demo to learn how Granica’s cloud cost management tools can help you improve the efficiency and ROI of your data lakes.

Granica
Post by Granica
March 05, 2024