Summary
Give us 30 minutes, and your cloud environment will never look the same again.
We will show you how to identify your biggest savings opportunities and implement a simple FinOps governance framework.
Cloud cost optimization often feels like a constant battle: costs go down one month, then rise again shortly after. Why? Because “reducing cloud costs” is not a one time action. It is a combination of technical and organizational levers that need to be activated in the right order, with rigorous ROI measurement.
The goal of this article is simple: give you an actionable overview of the main optimization levers such as compute, storage, data transfer, commitments, and operational hygiene, while providing a practical framework to prioritize by impact: what to tackle first, what to avoid, and what level of savings to expect.
Looking for a practical, field driven approach? Perfect. No theory here. We are talking about savings opportunities, risks, execution order, and measurable metrics.
Before getting started: optimize, yes… but optimize what exactly?
A cloud bill typically increases for three main reasons:
- your usage increases, with more customers, more traffic, or higher platform activity
- your architecture or configuration settings are inefficient, creating unnecessary waste
- you are paying standard on demand pricing due to a lack of commitments and governance
Cloud cost optimization is about separating normal business growth from waste and unmanaged technical decisions.
The 3 categories of optimization levers
- Hygiene and waste: orphaned resources, non production environments, and overprovisioning.
- Technical efficiency: architecture, scaling, databases, and Kubernetes.
- Purchasing model: commitments such as Reserved Instances and Savings Plans, contracts, and negotiated discounts.
Most organizations achieve faster results by starting with operational hygiene and rightsizing, then securing commitments on a stabilized baseline before moving on to heavier architecture refactoring initiatives.
Good to know
The biggest cloud savings do not always come from complex architecture redesigns. Operational hygiene, rightsizing, and shutting down non production environments often generate the fastest ROI.
The right approach is to prioritize each optimization lever using three simple criteria: financial impact, implementation effort, and level of risk.
Compute: where most of the ROI is hidden
Compute resources such as virtual machines, containers, serverless workloads, and managed databases often represent the largest share of the cloud bill, making them the biggest source of ROI opportunities.
1) Rightsizing: the most profitable method, when applied with discipline
The cloud instance rightsizing method consists of reducing oversized instances or switching to a more suitable instance family based on actual usage data.
How to do it properly:
- target the top 10 or top 20 most expensive resources
- analyze 14 to 30 days of metrics including CPU, memory, I/O, and network usage
- apply a safe change such as reducing the instance by one size level, then test, monitor, and keep rollback capabilities available
Things to monitor:
- CPU usage alone is not enough. Many applications are constrained by memory usage or I/O performance instead
- traffic spikes: be careful with burst workloads such as batch processing jobs or sudden traffic peaks
Typical quick win: 10% to 30% savings on a targeted scope, sometimes even more when the environment has historically lacked governance or optimization.
2) Autoscaling, but not blindly
Autoscaling and rightsizing are complementary approaches:
- rightsizing ensures the right “average” resource size
- autoscaling adapts resources to traffic spikes and workload peaks
A good practice is to define clear objectives such as latency and throughput targets, along with minimum and maximum scaling limits to avoid uncontrolled cost increases.
3) Spot and preemptible instances: significant savings, but not suitable for every workload
Spot instances on AWS, Spot VMs on Azure, and Preemptible instances on GCP can significantly reduce costs for:
- batch processing, CI/CD pipelines, data jobs, rendering workloads, and stateless workers
However, you must be able to handle interruptions through retry mechanisms, queues, and stateless architectures.
4) Kubernetes: a cloud cost black hole when left unmanaged
Kubernetes cost optimization is a topic on its own. The most common pitfalls include:
- oversized nodes
- poorly configured requests and limits
- multiple development, testing, and production clusters running continuously without shutdown policies
- uncontrolled costs from managed services and observability tooling
Concrete optimization levers:
- node rightsizing and better workload binpacking
- adjust requests and limits based on real observability data
- use Cluster Autoscaler and HPA
- rationalize the number of clusters and namespaces
- implement governance through quotas and policy management
5) Serverless: granular billing, but real risks of cost drift
Serverless reduces idle infrastructure costs, but expenses can quickly grow if:
- there are no concurrency limits in place
- uncontrolled execution loops occur
- logs et egress non anticipés
Leverage: implement budgets and alerts and monitor “cost per request”
Storage: fast savings through hygiene and lifecycle policies
Storage is often “silent”: data accumulates, gets forgotten, and continues generating costs.
1) Clean up: snapshots, volumes, and orphaned objects
Common examples:
- snapshots that are never deleted
- volumes détachés
- “catch all” buckets
- logs kept indefinitely
Action: define a retention policy by data type and automate cleanup processes.
2) Lifecycle policies: one of the most underused optimization levers
Long tail optimization lever: optimize S3 and Blob Storage lifecycle policies.
Examples:
- “hot” for 30 days → “cool” for 90 days → “archive” for 1 year
- deletion of old versions beyond X days
- compression and more efficient formats such as Parquet for data analytics workloads
ROI: often very fast because the runtime environment itself is not impacted.
3) Choose the right storage type
- block vs object vs file
- performance (IOPS) vs cost
- multi-zone replication: useful, but not for everything
Pitfall: paying for “premium” performance on cold data.
Data transfer: egress, where the surprises happen
Reducing cloud data transfer egress is a critical lever, especially if you have:
- multi-region architectures
- data pipelines
- a lot of outbound traffic (CDN, APIs, clients)
1) Map: from where to where?
Before optimizing, you need visibility:
- inter-zone traffic
- inter-region traffic
- transfers between services
2) Concrete levers
- use a CDN properly (cache hit rate)
- bring compute and data closer together (avoid unnecessary cross-region traffic)
- reduce inter-AZ transfers where possible
- compress / reduce chatter (payloads, polling)
- for data: prefer “in-place” processing rather than moving it around
3) Watch out for logs, monitoring, and data exports
Observability platforms can generate significant outbound traffic.
Lever: filter, sample, define retention policies, optimize formats.
Commitments: RI / Savings Plans, highly powerful “financial” ROI
Commitments (Reserved Instances / Savings Plans on AWS, Azure Reservations, CUDs on GCP) are often the biggest ROI lever without changing the architecture… provided they are purchased correctly.
Reserved Instances vs Savings Plans: how to choose?
- Reserved Instances: more specific (type/region), often more “rigid”
- Savings Plans : more flexible depending on the model (compute vs EC2) and usage
The practical rule:
- commit on the stable baseline (constant workloads)
- start with 1 year if your visibility is low
- monitor coverage / utilization / waste monthly
The pitfall: overcommitting
Overcommitting creates “waste” (you pay even if you do not use it).
Governance must define: who decides, the target coverage level, and how adjustments are made.
Environments & hygiene: the safest quick wins
If your goal is “how to reduce cloud costs quickly,” this is often where to start.
1) Shutting down non-production environments
Set up a scheduler for:
- dev / test / staging
- ephemeral environments (PR environments)
- secondary clusters
Result: immediate savings if your environments are running 24/7 unnecessarily.
2) Orphaned and “zombie” resources
Detached volumes, IPs, load balancers, snapshots, images…
This is “dead” money, easy to recover with a routine.
Standardize tagging (to manage and optimize)
Without tags: no owner, no showback, no relevant budgeting, no prioritization.
Tagging is an optimization lever because it makes costs actionable.
Prioritize by impact: the simple method (ROI + risk + effort)
This is what separates teams that “make savings” from teams that truly manage costs.
Step 1: categorize your optimization opportunities
Create a list of optimization opportunities by category:
- hygiene quick wins (low effort, low risk)
- rightsizing & scaling (medium effort, low to medium risk)
- commitments (low effort, medium financial risk)
- refactoring / architecture (high effort, medium risk, variable but long-term ROI)
Step 2: score each action (impact / effort / risk)
For each action:
- estimated monthly impact (euros)
- effort (days)
- risk (low/medium/high)
- prerequisites (observability, testing, deployment windows)
You get a prioritized backlog: this becomes your cloud cost savings prioritization model.
Step 3: combine “short-term ROI” with “sustainable ROI”
A healthy plan includes:
- 60–70% quick wins + rightsizing (fast gains)
- 20–30% commitments (depending on stability)
- 10–20% targeted refactorings (sustainable gains, but carefully selected)
Step 4: measure ROI properly
Measure:
- before/after over a comparable period
- impact on performance / incidents
- shifted costs (e.g. less compute but more data transfer)
- net gains (not just theoretical ones)
FAQ
To optimize cloud costs, it is recommended to start with quick wins: cleaning up unused resources, shutting down non-production environments, and rightsizing.
These actions help reduce cloud costs quickly with limited risk.
Cloud refactoring efforts (architecture, data, rewrites) provide more sustainable long-term gains, but require greater effort.
The right approach is to combine fast wins with structural optimizations.
Cloud cost optimization generally makes it possible to achieve:
- 10 to 30% savings on poorly governed environments
- 5 to 15% on already optimized environments
The most sustainable gains then come from a structured FinOps approach, which helps stabilize and manage cloud costs over time.
The main levers for cloud cost optimization are:
- compute (rightsizing, autoscaling),
- storage (lifecycle policies, cleanup),
- data transfers (reducing egress),
- commitments (Reserved Instances, Savings Plans),
- cloud hygiene (unused resources, non-critical environments).
In most cases, compute and cloud hygiene provide the best return on investment.
Prioritizing cloud optimization actions is based on three criteria: financial impact, implementation effort, and level of risk. An effective strategy consists of:
- launching quick wins to generate fast savings,
- securing commitments on stable workloads,
- then addressing more complex optimizations.
This approach helps maximize ROI while limiting risks.
