Cloud cost optimization: how to regain control over spending and build an effective FinOps model?

Your cloud bill is increasing even though no major system or new project was launched in the past month. The IT team sees infrastructure costs going up, finance sees the budget being exceeded, but it is difficult to answer one simple question: what exactly is costing more, and why?

This is a common consequence of a growing cloud environment. New instances, test environments, data copies, logs, clusters or AI services can be launched quickly. What is much harder is maintaining an equally fast flow of information about who is using a particular infrastructure component, what business purpose it serves and whether it is still needed.

Effective cloud cost optimisation therefore goes far beyond removing a few unused machines. It requires financial data to be combined with technical knowledge: cost visibility, clear accountability, appropriate purchasing models, infrastructure optimisation and regular decision-making within a FinOps model.

Why do cloud costs get out of control?

In an on-premises environment, purchasing new infrastructure would usually require a decision, hardware procurement and time to deploy it. In the cloud, that threshold largely disappears. An engineer can provision additional compute capacity, a database or a test environment within minutes. This flexibility is one of the cloud’s greatest strengths, but without appropriate rules it can easily become a source of costs that no one controls at an organisation-wide level.

Oversized instances may be part of the problem, but so can smaller components left behind after projects: unused disks, snapshots, IP addresses or development environments running around the clock. On top of this come the costs of storing growing volumes of data, transferring data between regions and availability zones, observability tools and new AI-related workloads.

In a large organisation, the division of responsibilities adds another layer of complexity. The Cloud Architect focuses on performance and stability. The application team focuses on the product. Procurement negotiates contracts. The CFO controls the budget. If these perspectives are not connected within a single management model, the monthly invoice becomes the result of many locally reasonable decisions that may not be efficient at an organisation-wide level.

The first question should therefore not be “what can we switch off?”, but rather “do we actually know what we are paying for?”

Cost visibility – from a provider invoice to business information

Knowing that an organisation spent a certain amount on AWS, Azure or Google Cloud in a given month says little about how efficiently the environment is operating. It is far more useful to know how much a specific application, product, environment or business process costs.

This is why organising cost data should be the starting point for optimisation.

Tagging and assigning costs to owners

Tags allow resources to be described with information about a project, application, environment, team, owner or cost centre. This prevents infrastructure from becoming an anonymous line item on an invoice.

The CFO can see the cost of running a specific product. The IT team can identify how much the organisation spends on production and test environments. An application owner can link an infrastructure change to its financial impact.

This approach becomes particularly important in distributed and multi-cloud environments. The more teams and platforms use the infrastructure, the more important common tagging standards and clearly defined ownership become.

Monitoring, budgets and anomaly detection

The next step is continuous cost monitoring. Tools provided by the major cloud providers, such as AWS Cost Explorer, Azure Cost Management and Google Cloud Billing, make it possible to analyse spending, manage budgets and identify unusual changes.

An alert alone does not solve the problem. It needs to be linked to accountability. If a notification about a sudden cost increase is sent to an inbox that nobody regularly checks, the organisation has monitoring in place but still lacks control.

A well-designed process therefore connects three pieces of information: what became more expensive, who owns it, and whether the increase corresponds to a business or technical change.

Cloud costs should also be measured in a business context

The total cloud bill is necessary for budgeting, but it is not always the best measure of efficiency.

If application sales have doubled while infrastructure costs have increased by 30%, a higher bill may indicate improved efficiency rather than a problem. Depending on the business model, it may therefore be more useful to monitor the cost of a product, environment, transaction or user.

Only then does the optimisation discussion move beyond the question of how to pay less. It becomes a question of whether technology spending is creating sufficient value.

FinOps – how can cost optimisation become a continuous process?

Cloud environments change too quickly for optimisation to be carried out once a year. New applications, projects and data are introduced continuously, and the cost structure changes with them.

FinOps is an operating and cultural model that brings IT, finance and business teams together around the economics of cloud usage. Its goal is not simply to minimise the bill. It is about making informed decisions that balance cost, performance, security and value for the organisation.

In this model, responsibility does not end with the finance department. An engineer provisioning infrastructure should be able to see the cost of that decision. An application owner should understand what drives the cost of maintaining the application. Finance teams, in turn, need data that enables them to forecast spending and allocate it in a way the business can understand.

This way of working requires clear rules: tagging standards, budgets, owners, KPIs, reporting methods and review cycles. It is therefore closely connected with a broader Cloud Operating Model, which organises not only costs but also roles, governance and the way the environment is managed.

A one-off audit still has value, particularly when an organisation needs to identify quick improvement opportunities. Sustainable results, however, come from repeating the cycle: measuring usage, identifying a problem, implementing a change and verifying its impact.

How can cloud costs be reduced without redesigning the architecture?

Not every optimisation requires changes to the application. In many environments, the first opportunities can already be found in the way compute capacity is purchased.

The Pay-As-You-Go model provides significant flexibility because the organisation pays for current usage without making a long-term commitment. That flexibility comes at a price. If some workloads are stable and predictable, it is worth comparing this model with options based on committing to a defined level of usage.

AWS offers options such as Savings Plans and Reserved Instances, while other major providers have their own discount mechanisms for predictable workloads. They should not, however, be purchased simply because the nominal price is lower. An excessive commitment can itself become a cost if the organisation does not use the capacity it has contracted.

The decision should therefore be preceded by an analysis of historical usage and workload characteristics.

Another option is Spot Instances or other forms of lower-cost interruptible compute capacity. They work well where a task can be stopped and restarted without affecting a critical process, for example in some batch processing, testing or background workloads. They are not suitable for every production system.

At a larger scale, it is worth looking beyond individual services. Cloud costs are also influenced by contract structures, discount utilisation, support plans and the terms negotiated with providers. Financial optimisation therefore does not end with selecting a particular instance type.

Technical infrastructure optimisation – where does the budget most often leak?

Once an organisation understands what generates costs and how infrastructure is purchased, it can move on to the next question: does it actually need everything it is paying for?

Rightsizing and unused resources

Rightsizing means matching the size of a resource to its actual workload. If a virtual machine uses only a small proportion of its CPU and memory most of the time, it is worth checking whether a smaller and less expensive option could provide the required performance.

A separate category consists of orphaned resources, often referred to as zombie resources. These are infrastructure components that still exist and generate costs even though they no longer serve a necessary function. Examples include unattached volumes, old snapshots, unused IP addresses or environments left behind after a project has ended.

In large organisations, manual clean-up is not enough. A process is needed to identify these components regularly, together with clear rules defining when they can be removed.

Autoscaling and environment schedules

Autoscaling adjusts the number or size of resources to current demand. It allows capacity to increase during peak periods and decrease when demand falls.

Another, often simpler mechanism is scheduling. Development and test environments do not always need to run around the clock. Automatically switching them off outside working hours can reduce the amount of time during which the infrastructure generates costs.

In both cases, the mechanism should reflect how the system is actually used. Overly aggressive optimisation can affect performance or availability.

Data costs change throughout the data lifecycle

Not all data requires the same level of availability.

Data used intensively by an application should be available quickly. An archive that the organisation accesses only occasionally can instead be stored in a lower-cost storage tier.

Lifecycle policies make it possible to move data automatically between storage tiers as usage patterns change. This is particularly important where data volumes grow steadily but a large proportion of older information remains actively stored only for regulatory or analytical reasons.

Automation prevents the problem before it appears on the bill

Infrastructure as Code makes it possible to define infrastructure in a repeatable and controlled way. Combining IaC with policies, CI/CD automation and platform standards can reduce accidental configurations and infrastructure operating outside the agreed model.

This is where cost management connects with the broader approach of Platform Excellence. Standardising the way environments are created and maintained gives teams greater autonomy while preserving control over stability, security and infrastructure usage.

Kubernetes, observability, data transfer and AI – new sources of cloud costs

A traditional virtual machine audit is no longer sufficient in every environment. Containers, extensive observability and AI workloads create additional layers of cost whose underlying mechanisms may be less obvious.

Kubernetes and containers

Containers make applications easier to scale, but they do not automatically solve the problem of efficient infrastructure utilisation.

Inappropriate requests and limits, oversized worker node pools or poorly configured autoscaling can leave a cluster with significant unused capacity. Kubernetes optimisation therefore requires looking at application behaviour, cluster configuration and actual utilisation of the underlying compute infrastructure at the same time.

AI, LLMs and GPUs

In AI solutions, the cost structure can be even more distributed. Costs are generated not only by model training or GPU infrastructure. Solutions based on language models also involve inference, API token usage, embeddings, data storage, vector databases, observability and integrations.

AI therefore requires its own layer of cost monitoring. The economics of an experimental PoC are very different from those of a production system processing thousands of requests every day.

Observability and log retention

Monitoring helps organisations control their environments, but monitoring itself also generates costs.

If an organisation collects every possible metric and retains every log for long periods in the most expensive tier, telemetry costs can increase faster than the value generated by the data.

It is therefore worth defining which information is operationally necessary, how long it needs to remain immediately accessible and what can be moved to lower-cost archival storage.

Data egress and traffic architecture

Data transfer can also become a significant budget item, particularly when communication takes place between regions, availability zones or different clouds.

In these cases, changing the pricing model alone will have limited impact. The application architecture needs to be analysed: component placement, communication patterns, CDN usage and where data is processed.

This is one example of a situation in which a cost problem becomes an architectural problem.

When does cloud cost optimisation require a change in architecture or operating model?

If an organisation carries out the same audit every few months and repeatedly identifies similar problems, it is worth looking beyond the configuration of individual services.

Recurring overprovisioning may indicate a lack of common standards. Increasing egress costs may result from the system architecture. Ownership issues may be caused by the way responsibilities are structured. Inefficient commitments may show that purchasing decisions are disconnected from the plans of technical teams.

In these situations, the way the environment is managed needs to change.

This is particularly relevant for multi-cloud organisations. Multi-cloud can increase flexibility and reduce dependency on a single provider, but it also increases the number of technology decisions, pricing models and competencies that need to be maintained. Simply adding another cloud is therefore not a cost optimisation strategy.

MINDBOX also takes a broader view of this challenge when addressing how to design an operating model for a multi-cloud environment. The starting point is to adapt the model to the organisation’s actual structure rather than mechanically applying a generic framework.

In practice, a well-designed cloud environment should combine cost efficiency with requirements for security, scalability, reliability and application development. MINDBOX does not treat cost control as a separate add-on to cloud services – it is part of a broader strategy, governance framework and IT operating model.

How to build a cloud cost optimisation plan

The best place to start is visibility. Without data, it is impossible to determine whether the problem lies in infrastructure utilisation, the purchasing model or the architecture itself.

The next step is to assign costs to applications and owners. Only then should the organisation look for quick improvements: unused resources, oversized instances or poorly matched purchasing models.

The next stage involves technical decisions around scaling, storage, automation and workload structure. If the same problems continue to return despite these actions, the architecture and operating model need to be reviewed.

The final element is making the process sustainable. Budgets, KPIs, ownership and regular reviews ensure that optimisation does not end with the first reduction in the cloud bill.

Frequently asked questions about cloud cost optimisation

Cloud cost optimisation starts with understanding the environment

The lowest possible bill is not a goal in itself. The infrastructure still needs to provide the required performance, security and operational continuity.

Cost should therefore be treated as one of the parameters of the architecture and the way the organisation operates. Sometimes switching off an unused environment is enough. In other cases, the purchasing model needs to change. In more complex situations, the organisation may need to clarify accountability, redesign the architecture or introduce a consistent FinOps model.

At that point, optimisation stops being a recurring exercise in “cleaning up the cloud bill” and becomes part of deliberate technology management.