Migrating operations to the cloud is the natural step for any company that seeks to lead. However, one of its main risks lies in the spending behind cloud implementations carried out without unifying operational and financial aspects under a single, organized methodology.
This will not be your situation. You can optimize your cloud bill with an effective cloud technology strategy that accelerates your return on investment through efficient and controlled spending. This is our step-by-step guide to balancing performance and budget.
Making Cloud Spend Visible
The first pillar of the FinOps methodology is visibility, and the reason is clear: you can’t optimize what you don’t understand. You need every dollar spent on AWS to have an owner, a purpose, and business context.
As obvious as this may sound, many organizations choose not to make spending visible. N0ps reports that even with the adoption of native and third-party tools, 44% of organizations have limited visibility into their cloud spending.
When you ensure visibility, you can compare spending and revenue by product, identify which teams generate more costs without return, and prioritize optimization actions with a direct impact on ROI.
Without visibility, any attempt at cutting costs risks breaking critical services or creating friction with teams. There will be no traceability, defined responsibilities, or accountability.
Implementing a Tagging Policy
Your tagging policy is the backbone of FinOps in AWS because without consistent tags, there is no cost attribution. The practical rule is that no relevant resource is created without a minimum set of mandatory tags.
A base set you should have as a CTO includes tags such as:
- Application.
- Owner/Team.
- Environment (Production/Development).
These tags group costs by product, business unit, or client, and enable showback/chargeback models without fighting with Finance.
It is key to define this policy in writing, with examples of good and bad practices, and to socialize it across all engineering teams to establish a common language that avoids confusion and rework.
Configuring the Monitoring Dashboard
The next step is to visualize these cost data in an intelligible way. AWS offers native tools such as AWS Cost Explorer and AWS Budgets which, combined with a solid tagging policy, allow you to create powerful monitoring dashboards.
Cost Explorer makes it easy to filter and group costs by tags, services, regions, and even usage types, offering a detailed view of your historical spending and forecasts.
Another good starting point is to use the Cost and Usage Report (CUR) and AWS Cloud Intelligence Dashboards or similar tools, which already include FinOps KPIs ready to use. These dashboards allow you to track metrics such as:
- Total spend.
- Savings plan coverage.
- Overprovisioned hours.
- Previous generation instance usage.
- Cost anomaly frequency.
Technical Optimization
Once visibility is in place, the second pillar is the technical optimization of resources without touching your product logic. This is where a significant portion of the 30% target is achieved simply by correcting overprovisioning and choosing more efficient instance families.
Right-Sizing
Right-sizing is the most direct and effective optimization strategy. In simple terms, it means ensuring that AWS resources match their workload. The main goal is to avoid overprovisioning.
It’s the equivalent of adjusting a suit size: most workloads run on instances that are too large “just in case.” Reducing excess capacity can cut up to 35% of compute costs without rewriting code or changing architecture.
In AWS, tools like Compute Optimizer and recommendations from the Cost Optimization Hub show you overprovisioned resources based on CPU and memory, giving you insights to apply strategies such as:
- Downgrading from m6i.2xlarge to m6i.xlarge.
- Switching from general-purpose to specialized instances.
- Moving databases to smaller sizes if actual usage allows it.
Spot Instances and Graviton
These are two typical FinOps fundamentals to reduce compute costs without changing application logic—only the underlying infrastructure.
Spot Instances take advantage of unused AWS capacity with discounts of up to 60–90% compared to on-demand for interruption-tolerant workloads, such as batch processing or certain stateless microservices.
On the other hand, AWS Graviton processors, based on the ARM architecture, offer a significant improvement in price/performance compared to x86-based instances.
Many applications developed for x86 architectures can run on Graviton instances with minimal changes, especially if they are based on languages such as Java, Python, Node.js, Go, or .NET Core.
A mature strategy combines right-sizing, Graviton for baseline capacity, and Spot for spikes, pushing total compute savings above 50–70% compared to the starting point.
Financial Commitments
Once you optimize your baseline consumption, you can commit to AWS to get strong discounts without overbuying. At this stage, your motto should be “optimize first, then commit.” Otherwise, you make an inefficient consumption pattern cheaper.
In practice, the current recommendation is to mainly use Savings Plans for the predictable base layer (for example, 60–80% of your stabilized consumption) and leave the rest as on-demand/Spot to maintain flexibility.
Going back to the dashboard you prepared, it should show your coverage and usage: what percentage of your spend is covered by commitments and how well you are utilizing them. This helps you adjust purchases quarterly and align them with real business growth.
Automation and Culture
Optimizing cloud spending is not a one-time event, but an ongoing process that requires automation and an organizational culture of awareness. It is the best way to sustain cost reductions over time.
CloudZero data confirms this: 82% of organizations state that automation is a very valuable element for maximizing the ROI of their cloud operations while you focus on more pressing tasks.
As a CTO, you must foster an environment that prioritizes efficiency for all teams, not just the finance or operations team. Automation frees engineers from repetitive manual tasks, allowing them to focus on innovation.
Shutdown Schedules
One of the simplest ways to save without touching code is to shut down non-production environments when no one is using them. Labs, QA, staging, and demo environments often consume resources 24/7 even though they are only used 8–10 hours a day. That’s easy waste to eliminate.
The key is to document these windows and provide a simple way to turn resources back on outside of schedule when needed. You avoid friction, maintain trust in automation, and create an organizational habit of “not leaving the lights on” in the cloud.
Removing Orphaned Resources
These are a silent money leak in almost every mature AWS environment. They don’t break anything because they no longer serve any application, but they keep showing up on the bill month after month. We’re talking about:
- EBS volumes without instances.
- Obsolete snapshots.
- Unused Elastic IPs.
- Orphaned load balancers.
An effective FinOps practice includes a recurring process (monthly or biweekly) to identify and remove these resources, supported by Cost Explorer reports, the Cost Optimization Hub, and scripts that cross-reference active resources with tags and states.
Automating this process is the most effective approach. You can mark unused resources for n days, notify owners, and then delete them if no one objects.
Over time, this cycle becomes routine and teams internalize that leaving clutter in the cloud has a direct cost.
