IT cloud spending is constantly increasing, posing one of the most pressing challenges for financial institutions today. After migrating to cloud environments, particularly in multi-cloud models, many banks face unpredictable spikes in their monthly bills. This occurs due to several factors: from orphaned resources (forgotten virtual machines or databases that continue to incur costs) to overprovisioning of Kubernetes clusters, which are reserved with excess capacity and not fully utilized. Dormant testing and development environments, a lack of strategy for using reserved instances (discounted capacity) and spot instances (temporary, cheaper capacity) only exacerbate the problem. Consequently, cloud bills grow, but transparency regarding exactly where the money is going is lacking, complicating budget planning and control.
Why cloud costs spiral out of control
The primary reason for uncontrolled cloud cost escalation lies in the shift in resource consumption models. In traditional on-premises infrastructure, capital expenditures (CAPEX) were high but predictable. In contrast, operational expenditures (OPEX) in the cloud are flexible but require constant management. Without clear policies and processes, developers and DevOps teams gain easy access to unlimited resources, leading to their overconsumption. The absence of integration between finance and IT departments, unclear definition of responsibilities for cloud budgets, and a lack of real-time cost monitoring and analysis tools create financial planning black holes. This is particularly critical for banks, where every dollar spent must be justified to regulators and shareholders.
Solution options: From reactive to proactive management
Cloud cost management has evolved from simple monitoring to comprehensive FinOps practices. The first option is reactive cost monitoring. This involves using native cloud provider tools (e.g., AWS Cost Explorer, Azure Cost Management) to analyze bills after they are received. This allows for the identification of anomalies and major cost drivers but does not enable real-time influence. This approach addresses basic visibility but doesn’t provide proactive control. The second, more effective option is implementing FinOps practices. FinOps (Financial Operations) is an operational model that aligns IT teams’ financial accountability with business objectives. It includes resource tagging, setting budgets for each team or project, regular reviews of reserved instances and spot capacity usage, and automation for detecting and deactivating orphaned resources. This approach ensures proactive cost management, allowing banks not only to see where money is going but also to influence its consumption before the bill is issued. This eliminates unpredictable expenses and provides the transparency necessary for regulatory reporting and strategic planning.
Common Pitfall
One of the most common mistakes banks make after migrating to the cloud is viewing cloud costs as unavoidable operational expenses that require no active management. Instead of implementing FinOps practices from day one, many organizations wait until their cloud bill reaches significant proportions. This leads to teams continuing to create resources without proper control, failing to optimize their usage, and not considering the financial implications of their architectural decisions. As a result, the bank incurs costs due to overconsumption, and attempts to rectify the situation post-factum require significantly more effort and time.
The correct path is to implement FinOps practices from day one. This means mandatory tagging of all resources (by project, team, environment), setting clear budgets for each team, and regularly reviewing the usage of reserved instances and identifying orphan resources. This approach integrates financial responsibility into the daily operations of IT teams, ensuring transparency, control, and continuous cost optimization.
Using the example of a typical scenario in a national-scale bank with several million customers, where customer profiles are distributed across an ABS (Automated Banking System), CRM, mobile application, and loyalty program, FinOps implementation becomes particularly crucial. A bank with an extensive branch network and active online service channels constantly handles large volumes of data and high loads. Regulatory requirements from the NBU and GDPR (for EU clients) regarding data quality and audit trail necessitate uninterrupted operation and high system availability. Without FinOps, maintaining Oracle DB, SAP, IBM ABS, CRM (Salesforce/Dynamics), API Gateway, IAM, and SIEM in a multi-cloud environment would lead to unpredictable expenses. This would hinder achieving business results such as reducing customer duplicates in reports, shortening service times in branches through a unified customer profile, and faster readiness for external audits.
Technological FinOps Tools in Multi-Cloud
Effective FinOps implementation in a multi-cloud environment requires the use of specific technological solutions:
- Cloud Cost Management Platforms (CCMP): These platforms, such as CloudHealth, Flexera One, or native provider tools, aggregate cost data from various cloud providers (AWS, Azure, Google Cloud) into a unified interface. They are used for centralized monitoring, analysis, and optimization of cloud expenses. This resolves the issue of fragmented cost visibility, allowing finance and IT teams to gain a complete picture of resource consumption and quickly identify sources of overspending.
- Infrastructure as Code (IaC) (e.g., Terraform): IaC enables the management and provisioning of cloud infrastructure through code, rather than manual console operations. It is used to standardize resource deployment, ensure compliance with tagging policies, and automatically control instance sizes. This approach addresses the problem of resource non-compliance with standards and manual errors during creation, which led to suboptimal usage and difficulty in identifying cost owners.
- Kubernetes Cost Optimization Tools (e.g., Kubecost): These tools specialize in monitoring and optimizing costs within Kubernetes clusters used for deploying the bank’s microservices. They allow for the analysis of resource consumption at the level of individual pods and namespaces, identifying overprovisioning, and recommending optimal cluster sizes. This eliminates excessive spending on containerized environments, which often arises from the complexity of accurately forecasting microservice loads.
- AI Analytics for Cost Forecasting: AI models analyze historical cloud resource consumption data to predict future expenses. This enables banks to plan budgets more accurately and make informed decisions regarding the purchase of reserved instances or the utilization of spot markets. For instance, Softengi teams, specializing in AI system development, employ such approaches for their clients. This resolves the issue of unpredictability in future costs, which complicates financial planning and strategic decisions regarding infrastructure development.
Risks and Limitations
Despite significant advantages, FinOps implementation carries its own risks. Firstly, organizational resistance: changing culture and integrating financial responsibility into IT processes may face opposition from developers accustomed to unlimited resource access. Secondly, tagging complexity: in large enterprise environments with hundreds or thousands of resources, ensuring consistent and correct tagging can be challenging without proper automation and discipline. Thirdly, insufficient expertise: FinOps requires deep knowledge in both cloud technologies and financial management, and a lack of such specialists can slow down implementation. Finally, cost vs. performance trade-off: excessive cost optimization can lead to decreased system performance or availability, which is an unacceptable risk for a bank. In such cases, banks often engage external partners, such as SL Global Service, to provide cloud infrastructure management and FinOps services, allowing them to delegate complex optimization and infrastructure support tasks.
Effective FinOps management in a multi-cloud environment allows banks to achieve predictable cloud costs and significantly reduce IT expenses – industry trends show savings of 15–25% through architecture optimization and continuous control. This is not just about saving money, but about ensuring financial stability and transparency, which is crucial for financial institutions in today’s regulatory landscape. The comprehensive approach of Intecracy Group, combining the expertise of its member companies in cloud solutions, AI analytics, and system integration, helps clients not only control but also optimize their cloud investments, transforming them into a strategic advantage.