What is Cloud Rightsizing?

Key takeaways

• Cloud rightsizing is matching cloud resources — compute, memory, storage, database, and Kubernetes pods — to actual workload demand, so you stop paying for capacity you do not use.

• Industry research commonly reports 30–35% of cloud spend sitting on over-provisioned or idle resources. Rightsizing is typically the highest-impact, lowest-effort lever to recover that spend.

• Rightsizing is not a one-time project. Workloads change, new instance families appear, and pricing moves — a credible programme runs a monthly rightsizing cycle.

• Always rightsize before committing (Reserved Instances, Savings Plans, Committed Use Discounts). Buying a three-year reservation for an over-provisioned resource locks in the waste.

• CerteroX Cloud Management customers achieve an average of 38% cloud cost savings through rightsizing, commit optimisation, power scheduling, and waste removal. Certero is a FinOps Certified Platform.

What is Cloud Rightsizing?

Cloud rightsizing is the practice of analysing how cloud resources are actually used and adjusting their configuration so capacity matches demand — no more, no less. When resources are rightsized, the organisation stops paying for headroom that never gets consumed.

The problem rightsizing solves is that cloud resources are typically provisioned at peak demand, on a vendor-suggested default, or on a conservative estimate — producing instances that run at 10–30% average utilisation while being billed at 100% of the sticker rate. Aggregated across hundreds or thousands of resources, the waste becomes material.

Rightsizing applies across the estate:

• Compute instances (EC2, Azure VMs, Compute Engine, OCI VMs)

• Managed databases (RDS, Azure SQL, Cloud SQL, Autonomous Database)

• Container services (EKS, AKS, GKE, OKE)

• Kubernetes pods (requests and limits)

• Storage volumes (EBS, Azure Disks, Persistent Disks)

• Serverless / function memory configuration

• Managed services with capacity parameters (Redshift, Snowflake on cloud, OpenSearch, Elasticache)

Why rightsizing matters

The scale of the waste

Industry research — FinOps Foundation, Flexera, Virtana, CloudZero and others — consistently reports around 30–35% of cloud spend sitting on over-provisioned or idle resources. Exact numbers vary by industry, workload mix, and FinOps maturity, but the pattern is consistent: under-consumed compute, stale storage, forgotten dev-and-test environments, and old-generation instances running alongside new ones.

Why waste compounds

• Over-provisioned templates get copied into new environments by default

• Teams inherit previous instance-size choices and never revisit them

• The cloud bill arrives 30 days after the waste has already occurred

• Performance worries drive "better safe than sorry" sizing that never gets reviewed

• Nobody owns the ongoing optimisation work

Why it is the best-return lever

Rightsizing is typically the highest-impact, lowest-effort optimisation available:

• No application re-architecture needed

• Recommendations can be automated against real telemetry

• Savings are immediate and measurable

• When done with proper data, performance is preserved or improved

Types of rightsizing

1. Vertical rightsizing

Adjust the instance size within the same family based on utilisation evidence.

• Down: m5.xlarge → m5.large (when CPU, memory, network all run well below the limit)

• Up: m5.large → m5.xlarge (when any resource is regularly saturated)

2. Horizontal rightsizing

Adjust the number of instances in a cluster or auto-scaling group.

• Reduce when average capacity demand is below the current fleet size

• Increase when the fleet is routinely saturated before auto-scaling can respond

3. Instance family changes

Move to a more appropriate family based on workload profile.

• General-purpose → memory-optimised for memory-heavy workloads (databases, analytics, in-memory caches)

• General-purpose → compute-optimised for CPU-heavy workloads (batch, encoding, simulation)

• Previous generation → current generation for better price/performance (often a net saving even without a size change)

• x86 → Arm (AWS Graviton, Azure Cobalt, Ampere Altra) where the workload supports it — often 10–40% cheaper at equivalent performance

4. Storage rightsizing

• Reduce volume size where allocated space exceeds real use

• Change storage class (SSD → HDD, hot → cool, standard → archive) where access patterns permit

• Delete orphaned volumes and old snapshots

• Adjust IOPS provisioning (for provisioned-IOPS volumes) to match actual throughput

5. Database rightsizing

Managed databases have their own sizing parameters and their own commit models.

• Right-size RDS, Azure SQL, Cloud SQL instance classes

• Adjust Aurora Serverless capacity units to the observed demand curve

• Align reserved / committed database capacity with rightsized instances, not original instances

6. Kubernetes rightsizing

Kubernetes adds a layer: the node pool has a size and the pod has requests and limits.

• Right-size pod requests and limits to actual CPU and memory usage — over-requested pods lower cluster density and inflate node count

• Right-size node-pool instance types and autoscaler configuration

• Use HPA and VPA together, or a vendor tool, to keep pod sizes and replica counts aligned to real demand

7. Serverless and managed services

• Lambda memory (and therefore CPU and billing) can be tuned to the workload — often the optimum is higher memory for lower total duration cost

• Step Functions, API Gateway, managed queues and streams all have throughput dials that can be tuned against real usage

The rightsizing process

Step 1 — Collect utilisation data

Collect at a minimum:

• CPU utilisation (average, P95, P99)

• Memory utilisation — critical; cannot be derived from CPU; requires agent or cloud-native memory monitoring

• Network throughput

• Storage IOPS and throughput

• Application-level metrics where meaningful (request rate, queue depth, DB transactions)

Observation window: 14 days minimum, 30 days preferred, to capture weekly patterns, month-end batch, and typical demand variability.

Step 2 — Analyse patterns

From the telemetry, identify:

• Consistently underutilised resources — downsize or instance-family change candidates

• Resources at or near saturation — upsize candidates, to avoid under-sizing regressions

• Idle resources — termination candidates

• Peak vs average — whether auto-scaling or a single-size decision is correct

• Usage seasonality — monthly cycles, fiscal year-end, retail peaks, academic terms

Step 3 — Generate recommendations

A credible recommendation includes:

• Current configuration

• Recommended configuration and family

• Evidence (utilisation distribution) that justifies the change

• Estimated monthly saving at current pricing

• Risk rating — high-confidence downsize vs cautious adjustment

Step 4 — Validate and implement

• Verify with the application owner — some workloads have non-obvious peak behaviour (warm-up, batch windows, disaster-recovery tests)

• Test in a non-production environment where possible

• Implement during a change window, with rollback prepared

• Monitor performance for 24–72 hours post-change for regression

Step 5 — Continuous optimisation

Rightsizing is not one-off.

• Workloads evolve

• New instance families appear (often cheaper for the same work)

• Pricing changes

• Reserved / committed inventory expires and needs re-planning

A monthly rightsizing cadence, with quarterly deeper reviews aligned to commit and budget cycles, is a reasonable default.

Rightsizing vs other optimisation levers

Key rule: rightsize first, commit second. Buying a Reserved Instance on an over-provisioned resource locks in the waste for 1–3 years.

Per-provider notes

Third-party FinOps platforms (including CerteroX Cloud Management) reconcile recommendations across providers, add memory telemetry, and normalise the savings view — so the programme is not jumping between native consoles to assemble a picture.

Common pitfalls

• CPU-only rightsizing — memory-bound workloads get incorrectly downsized, causing performance regressions and re-provisioning churn

• Too-short observation windows — a 7-day window misses month-end batch and weekly patterns; 30 days is safer

• Ignoring peak behaviour — average utilisation can hide a nightly spike that requires the current size

• Rightsizing without commit alignment — downsizing a reserved instance strands the reservation

• Missing application context — warm-up, pre-staged capacity, HA requirements, disaster-recovery reserves — all legitimate reasons a resource looks "over-provisioned"

• One-shot projects — a 90-day rightsizing sweep that is never repeated will drift back

• Kubernetes pod over-requests — pods reserving more CPU / memory than they use inflate the node count and make node-level rightsizing pointless

• No post-change monitoring — without a performance check, regressions go unnoticed until a user complains

How CerteroX supports cloud rightsizing

CerteroX Cloud Management provides cloud cost management and FinOps across AWS, Azure, Google Cloud, Oracle Cloud, and Kubernetes, including continuous rightsizing analytics.

Capabilities

• Multi-cloud coverage — AWS, Azure, Google Cloud, Oracle Cloud, Kubernetes in one reconciled view

• Utilisation analysis — continuous CPU, memory, network, and storage telemetry; memory included (not CPU-only)

• Rightsizing recommendations — instance-size, instance-family, storage class, and Kubernetes pod recommendations with evidence and confidence rating

• Commit-alignment logic — rightsizing recommendations account for existing Reserved Instances and Savings Plans so you do not strand commit

• Savings modelling — see projected savings before making a change

• FinOps-native — allocation, unit economics, and reporting aligned to the FinOps Inform / Optimize / Operate loop

• FOCUS export — cost and usage data exportable to the FinOps Open Cost and Usage Specification

Results

CerteroX Cloud Management customers achieve 38% average cloud cost savings across a combination of rightsizing, commit optimisation, power scheduling, and waste removal.

Credentials

• FinOps Certified Platform

• #1 rated on Gartner Peer Insights for ITAM

• Four-time Gartner Customers' Choice — 2019, 2020, 2021, 2024 (the only vendor to achieve this)

• Oracle Certified Partner — the only ITAM / SAM vendor (relevant for OCI and Oracle-on-other-clouds rightsizing)

• 97% of customers recommend Certero

Frequently asked questions

How much can rightsizing realistically save?

Typical savings on compute are 20–40% of the affected spend. The exact number depends on how over-provisioned the baseline is. Organisations that have never rightsized often see larger opportunities; mature FinOps programmes with monthly cycles see smaller, continuous savings.

Will rightsizing hurt application performance?

Done with proper telemetry (including memory) and a sensible observation window, no. Many organisations see performance improve after rightsizing because the new configuration fits the workload better — for example, a memory-optimised instance replacing an under-memory general-purpose instance. Risk comes from CPU-only analysis and too-short windows.

What utilisation metrics do I need?

CPU, memory, network throughput, and storage IOPS as a minimum. Memory is critical and is the most common omission — AWS, Azure, and Google Cloud all require an agent to collect memory metrics; without it, memory-bound workloads get incorrectly flagged as downsize candidates.

What observation window should I use?

14 days is the minimum to capture a typical weekly pattern. 30 days is a safer default because it includes month-end and catches most monthly cycles. For workloads with quarterly or seasonal patterns, extend accordingly and verify with the application owner before making a change.

Should I rightsize before buying Reserved Instances or Savings Plans?

Yes, always. Buying a 1-year or 3-year reservation on an over-provisioned instance locks in waste for the full term. The correct order is: rightsize → verify stability for 30–60 days → then purchase commit at the rightsized level.

What if my workload is peaky?

Peaky workloads should be rightsized to handle the peak, not the average — or, better, auto-scaled. Rightsizing tools that ignore P95 / P99 utilisation mis-size peaky workloads. Bursty workloads often benefit from a combination of smaller baseline instances and auto-scaling, so the peak is handled by scale-out rather than permanent over-provisioning.

How do I rightsize Kubernetes pods vs nodes?

Two different decisions. Pod rightsizing adjusts the requests and limits that schedulers use for placement and throttling — over-request and you waste cluster capacity; under-request and you get throttled or evicted. Node-pool rightsizing adjusts the instance type of the underlying VMs. Both are needed; pod rightsizing usually yields the bigger saving because over-requested pods are endemic.

Is rightsizing the same as auto-scaling?

No. Auto-scaling adjusts capacity dynamically in response to demand — more instances, fewer instances. Rightsizing sets the baseline configuration — what instance type and size each unit in that fleet should be. The two are complementary: rightsize the baseline, then auto-scale for variable demand.

Does rightsizing apply to serverless / Lambda?

Yes. Lambda memory is also the CPU and network dial, so changing memory changes performance and total cost per invocation. Often the cost-optimal memory configuration is higher than the default, because the function finishes in fewer milliseconds and total billing goes down. Tuning tools like AWS Lambda Power Tuning automate this.

Does rightsizing apply to spot instances?

Yes, but with less leverage. Spot pricing is already 60–90% below on-demand, so rightsizing produces relatively smaller absolute savings. The more important decision for spot is whether the workload can tolerate interruption and whether the right instance family is available in the region's spot pool.

Can rightsizing be fully automated?

Recommendation generation can be fully automated. Automatic implementation is feasible for low-risk downsize recommendations on non-production resources, and a rising number of FinOps platforms support it. For production, most organisations prefer human approval and a change-window, to preserve the ability to roll back cleanly.

How often should rightsizing run?

Monthly as a baseline rhythm, quarterly alongside commit and budget reviews, and re-run after any architecture change or significant workload shift. Most waste does not reappear overnight, but untended estates drift over 6–12 months.

What is the difference between rightsizing and cloud cost management?

Rightsizing is one lever inside cloud cost management. Cloud cost management is the broader discipline — allocation, showback, budgeting, forecasting, commit planning, anomaly detection, and the various optimisation levers (rightsizing, scheduling, storage tiering, egress, spot). Rightsizing is usually the highest-return lever, but a programme that only rightsizes misses the others.

How does rightsizing relate to FinOps?

Rightsizing lives inside the Optimize phase of the FinOps Inform / Optimize / Operate loop. Inform establishes visibility and allocation; Optimize identifies and implements savings levers (rightsizing, commit, spot, waste removal); Operate embeds the cadence into engineering and product delivery. CerteroX Cloud Management implements the full loop and is a FinOps Certified Platform.

About Certero

Certero provides IT Asset Management (ITAM), Software Asset Management (SAM), SaaS Management, Cloud Management, and AI Management through the CerteroX product family. Certero is #1 rated on Gartner Peer Insights for ITAM, the only four-time Gartner Customers' Choice winner (2019, 2020, 2021, 2024), holds Oracle Certified Partner status (the only ITAM / SAM vendor), and is a FinOps Certified Platform. 97% of customers recommend Certero.

Related resources

• What is FinOps?

• What is Cloud Cost Management?

• What is IT Cost Optimization?

• What is FOCUS (FinOps Open Cost and Usage Specification)?

• FinOps Foundation

• Request a demo

Last updated: April 2026