With cloud spend on the rise, many companies are focusing on FinOps to control their AWS infrastructure expenses. A 2021 Flexera survey confirms this, finding that over 50% of companies cite managing cloud costs as a top challenge.
While adopting a FinOps model can help with cloud spend, choosing the right mix of strategies to reduce your expenses without sacrificing user experience is crucial.
That’s why knowing the difference between rate optimization and usage optimization is so important.
While related, these cloud cost management models require very different strategies.
Below, we’ll cover their key differences and help you build a comprehensive FinOps strategy. We’ll discuss best practices for rate negotiations, purchasing models, resource tagging, and utilization monitoring and explain how these different elements play out for each approach.
What is cloud rate optimization?
Cloud rate optimization reduces the per-unit pricing of cloud services like compute, storage, databases, and more.
It focuses specifically on negotiating better rates, purchasing reserved capacity, using discounts and commitments, and finding other ways to minimize the hourly or monthly fees associated with cloud resource usage.
Instead of optimizing for resource efficiency, cloud rate optimization is a financial exercise to cut the cost of cloud services. Teams work to understand spending patterns, identify savings opportunities, implement pricing improvements, and track savings over time. The goal is to pay as little as possible for each unit of cloud capacity consumed while maintaining business needs and service levels.
Effective cloud cost optimization allows organizations to increase return on investment (ROI) while reducing operating expenses. As cloud usage grows exponentially, rate optimization is crucial for cost-conscious enterprises. By prioritizing cloud economics, businesses can fund innovation and growth without runaway technology costs.
Components of cloud rate optimization
- Pricing models: Cloud providers offer various pricing models like On-demand, Reserved Instances, and Spot Instances. Selecting the right model per workload reduces hourly/monthly fees. For example, Reserved Instances can cut compute costs by 40-60%.
- Contract negotiations: Large customers can negotiate custom enterprise agreements, volume discounts, and other benefits. Even smaller users gain leverage via third parties to improve public cloud rates.
- Term length and prepayment options: Committing to a three-year term versus a one-year term can increase discounts by 20–30%, and choosing to prepay some or all of the commitment upfront can increase discounts by 5–10%.
- Billing monitoring: Granular visibility into cloud billing and amortized costs is vital for optimization. Usage spikes, unused resources, and waste become visible.
- Cost management tools: Automated tools analyze cloud spend, identify savings opportunities, implement reserved instance purchases, right-size workloads, and more. They amplify optimization via automation.
What is cloud usage optimization?
Cloud usage optimization is the process of maximizing the efficiency and value derived from cloud computing resources. Rather than simply paying less per unit, cloud usage optimization focuses on allocating and using cloud infrastructure as efficiently as possible.
The goal is to closely match provisioned resources to actual usage, reducing overprovisioning and waste. This saves money and enhances the performance, reliability, and sustainability of cloud environments. Effective usage optimization includes right-sizing workloads, eliminating idle resources, scaling dynamically with demand, and load balancing efficiently.
While rate optimization lowers cloud costs, usage optimization makes every dollar work harder. The two approaches work together to minimize expenses and maximize returns on cloud investments.
Components of cloud usage optimization
- Right-sizing resources: Provisioning optimal compute, storage, and memory capacity based on historical or estimated usage metrics. Both over and under provisioning waste money.
- Identifying idle resources: Discovering and eliminating resources like EC2 instances, RDS databases, and S3 buckets that are rarely used (or unused entirely).
- Auto-scaling services: Configuring services and infrastructure to scale out and in automatically as usage and traffic fluctuate. This aligns capacity closely with demand.
- Efficient load balancing: Distributing application traffic evenly across resources to prevent overutilization and bottlenecks while maximizing provisioned capacity usage.
- Continuous monitoring and optimization: Tracking detailed usage data and cloud bills to identify savings opportunities, adjust configurations, and right-size workloads over time.
Key differences between rate and usage optimization
FinOps teams working with AWS need to work out a comprehensive strategy that combines both rate and usage optimization to create an immediate yet sustainable framework for reducing cloud costs.
Let’s examine some of the biggest differences between the two cost optimization techniques to illustrate why businesses need to consider both.
The goal of rate optimization is to minimize the per-unit cost of cloud services. It aims to pay less per hour or GB. Usage optimization instead focuses on maximizing the efficiency and value of cloud resources. It reduces waste through better utilization.
Focus of optimization
Rate optimization centers on negotiating better pricing and discounts to reduce the hourly or monthly fees for cloud infrastructure and services. Usage optimization targets architectural efficiency, workload sizing, and automation to minimize wasted resources and over-provisioning.
Rate optimization leverages reserved instances, custom contracts, volume discounts, and spot pricing to pay less per unit. Usage optimization right-sizes workloads, eliminates unused resources, implements auto-scaling, and load balances efficiently.
Impact on cloud architecture
Rate optimization is largely financial, with minimal architectural impact. Usage optimization often requires reconfiguring deployments for efficiency, like auto-scaling groups and optimized database instances.
Measurement of success
Rate optimization focuses on cost savings per GB, vCPU hour, or other resource unit. Usage optimization measures success via utilization percentages, resource saturation, and efficiency benchmarks.
Rate optimization delivers quick cost savings that compound over time. Usage optimization establishes sustainable efficiency and lean resource usage, driving enduring value. Together, they enable cost-efficient innovation.
Integrating rate and usage optimization into your optimization strategy
The key advantage of integrating both rate and usage optimization is compounding savings from reduced costs and enhanced efficiency. The order and timing of implementing each method can vary based on organizational needs and capabilities. But when used together, they maximize value and minimize waste across cloud deployments.
Usage optimization enhances efficiency and eliminates waste, establishing a lean cloud footprint. Rate optimization then minimizes the per-unit cost of running optimized workloads. Together, these compound savings and value.
One approach is implementing the adjustments required for usage optimization (like auto-scaling policies) first, then applying rate optimizations like reserved instances to the efficient architecture. This coordinated approach aligns technical and financial teams to drive enduring ROI.
Alternatively, beginning with quick-win rate optimizations can fund the more complex initiatives of usage optimization. By employing both strategies simultaneously and continuously, businesses can optimize their cloud return at every level.
Rather than a one-time initiative, the optimization effort becomes a disciplined cloud management process, adapting to evolving usage patterns and business needs over time. The combined approach unlocks substantial savings and provides a complete framework for cost-efficient innovation.
Simplify and improve cost optimization with ProsperOps
Cloud optimization requires coordinating usage and rate strategies for maximum efficiency and savings. Traditionally, organizations implement usage optimization first to establish an efficient architecture before applying rate optimizations, but ProsperOps offers a unique advantage.
With its real-time coverage adjustments continuously matching changing environments, ProsperOps helps businesses optimize rates from day one without restricting workload changes. Engineers are free to architect and right-size workloads, while ProsperOps handles rate optimization autonomously.
By delivering the best of both worlds (cost-efficient architecture and optimized pricing), ProsperOps maximizes automation, unburdens engineers, and amplifies savings simultaneously.
Get a live demo to see how ProsperOps can unlock new levels of cloud optimization for your business.