In FinOps, rightsizing means adjusting cloud resources (instance types, number of CPUs, amount of memory, storage, databases, containers, and many other configuration parameters) to match actual workload requirements. It’s one technique among many FinOps practices, alongside unit economics, pricing strategy, architectural improvements, demand shaping, and more.
Consider: Average CPU utilization across Kubernetes clusters sits at just 10%, according to Cast AI’s 2025 Kubernetes Cost Benchmark Report. Meanwhile, 50% of practitioners rank workload optimization and waste reduction as their primary focus, according to the FinOps Foundation’s State of FinOps 2025, although it’s not the only or even primary lever in many programs.
That being said: When done well, it improves performance through capacity-demand alignment while supporting unit economics by lowering cost per customer, transaction, or feature.
Three forces are shaping how teams approach rightsizing in 2025:
AI’s cost explosion: GPU instances cost 10-50x standard compute, yet only 51% of organizations can effectively track AI ROI. Plus, there’s the financial impact of what we like to call AI’s false efficiency curve. Without rightsizing discipline, AI experiments become budget black holes.
Economic reality: The “growth at any cost” era is over. Every dollar wasted on idle resources is a dollar not invested in innovation. Rightsizing aligns with this approach.
Scale breaking point: Manual rightsizing worked with dozens of instances, not thousands. This means teams are increasingly relying on automated recommendations and engineer-in-the-loop reviews.
Reactive cost management doesn’t scale well. Companies that delay addressing workload alignment risk higher costs and reactive cuts later. The goal isn’t solely cost reduction. It’s achieving optimal resource-to-workload alignment while maintaining performance.
Addressing The Rightsizing Resistance
Let’s tackle the objections that kill rightsizing initiatives before they start:
“Rightsizing risks our application stability”: Start in non-production environments with gradual reductions and rollback triggers. Modern tools maintain performance buffers with complete visibility. Worst case: look into the metrics, understand peaks and valleys, and make a decision based on the data.
“Our workloads are too variable”: For variable workloads, combine autoscaling and right-sized baselines; use burst headroom where SLOs require. Static provisioning for dynamic workloads is the real waste. Automation based on the need defined through data.
“We don’t have time for this”: 57% of companies still track costs manually with spreadsheets. A one-time setup (dashboards, tags/labels, alerts) cuts the effort to evaluate and implement changes.
“We tried before and saved nothing”: You probably only looked at CPU, missing memory and network bottlenecks. Or tracked “potential savings” instead of realized invoice reductions.
“Leadership won’t support downtime risk”: Reframe around ROI: “Database savings fund next quarter’s innovation project.” Position rightsizing efforts as improving both costs and reliability.
The Five Inputs You Actually Need
To rightsize effectively, you need visibility across five critical categories:
1. Performance and Resource Utilization: CPU, memory, I/O, network metrics
2. Cost Visibility and Allocation: Real-time spend tracking, cost per team/product/feature, and Effective Savings Rate (ESR).
3. Optimization Tracking: Rightsizing recommendations, realized vs. potential savings, and implementation success rates.
4. Business Context: Seasonal patterns, availability requirements, compliance constraints that affect sizing decisions, and users or processes driving usage up and down.
5. Predictive Analytics: Anomaly detection, capacity forecasting, and trend analysis for proactive optimization.
Without all five categories, you’re making decisions blind. For example, you might rightsize a database based only on CPU, missing that it’s actually network-bound.
From Data To Decision: Why Most Rightsizing Fails
Having the right data is table stakes. The real gap? You’re probably drowning in metrics without a system to act on them.
Here’s what typically happens: Your monitoring shows 30% CPU utilization across your fleet. Great. But then you discover those same instances are memory-bound at 85%. Your database team warns about planned failover requirements. Finance wants immediate savings. Engineering fears weekend pages. Everyone retreats to their corners. Nothing changes.
In fact, CloudZero’s Director, Cloud Platform & FinOps Larry Advey has real-life horror stories on rightsizing. Check out his stories here.
The difference between organizations that successfully rightsize and those that don’t isn’t the data they collect — it’s how they systematically turn that data into decisions. You need practices that cut through the paralysis, create clear accountability, and balance competing priorities.
These aren’t suggestions. They’re battle-tested practices from organizations that actually reduced their cloud spend without breaking production, like LawnStarter did with CloudZero.
Best Practices That Actually Work
Rightsizing is one lever; in many programs, commitment management, architecture simplification, and demand shaping deliver larger, more durable savings. Master these four best practices before attempting anything else.
1. Track everything, not just CPU
You might only monitor CPU, missing that your instances are memory-bound at 85% or network-constrained. A database showing 30% CPU might actually be optimized if it’s hitting memory limits or needs failover headroom.
CloudZero’s complete visibility across CPU, memory, network, storage, and I/O helps to prevent optimizing the wrong dimension.
2. Measure realized savings, not potential
Stop celebrating dashboard projections. Track what actually hits your invoice.
Use Effective Savings Rate to measure real ROI from rightsizing efforts. If your “savings” don’t show up on the bill, they didn’t happen.
3. Look at context before cuts
That “wasted” 40% capacity might be intentional. Seasonal peaks, compliance requirements, or failover needs are realities.
Understand why resources were provisioned before rightsizing them. Track implementation success: acceptance rates, rollback frequency, and SLA impact post-change.
4. Automate the obvious, engineer the complex
Cloud-native tools handle basic rightsizing (5-15% savings). But engineering-led optimizations, such as query tuning, caching strategies, architecture changes, deliver 20-30%+ in reductions.
Use CloudZero’s automated cost intelligence capabilities to free engineers for high-impact optimization work.
Rightsizing Cloud Infrastructure: The Path Forward
Rightsizing only works when it’s grounded in full visibility, business context, and engineering engagement. Done poorly, it risks degraded performance or wasted effort. Done well, it delivers measurable savings and stronger reliability.
The challenge intensifies with AI workloads. While most practitioners now track AI spend, fewer than a quarter have established optimization practices for GPU clusters and training environments. AI is following the same pattern cloud infrastructure did years ago: visibility first, optimization lagging. AI/ML does complicate capacity planning and rightsizing helps to alleviate this, but you also need workload placement, scheduling, and policy guardrails to fully control cost.
Rightsizing remains a top-three value driver in FinOps. To succeed, you need to separate true waste from intentional design, base decisions on complete data, and track realized savings, not dashboard projections.
Rightsizing isn’t the program. It’s one practice in a FinOps system. Use it where performance and reliability allow, alongside commitments, pricing, architecture, and unit economics.
The Cloud Cost Playbook
The step-by-step guide to cost maturity
