Jitendra Nandwani
SVP and Head of CIS
Cloud is expensive – waste is exorbitant.
Burgeoning cloud bills, which is an industry wide concern, isn’t because of higher usage alone — rather it is a fallout of nurturing too many inefficiencies across management and operations.
Per industry estimates, data centers are projected to require $6.7 trillion by 2030 to keep pace with the demand for computing power. 75% of this projected demand is attributed to AI processing loads. Tech leaders are tasked with driving digital momentum and delivering what was earlier done in a decade in a matter of months whilst managing economic volatility, recessionary pressures, and keeping budgets under control. In this environment, going after business growth singularly is not practical rather pursing strategic goals and doing more with less will separate the men from the boys.
Key cost drivers and how to address them:
The discipline around consumption has not kept pace with the unprecedented adoption of cloud infrastructure. In other words, it’s not the cloud that’s expensive — it’s the unstructured consumption. Hidden costs compound with data egress, underutilized licenses, and long-term enterprise level commitments made without cost benchmarking and in some cases, shadow IT. As a thumb rule, when financial accountability meets optimized technical efficiencies, such as, true rightsizing, eliminating sprawl, defined governance with FinOps, it brings overall discipline to cloud consumption.
1. Overprovisioning, unused or idle resources
Organizations can overestimate peak demand, leaving “zombie” resources to sit idle and incur charges or overprovision and end up paying for capacity that is never used. In either scenarios, inefficient resource management, idle workloads, missed or fragmented FinOps lead to cost overruns. No one can right size what they don’t measure so invest in end-to-end observability and forecasting tools and gain visibility into resource utilization; move from a reactive, bill-shock model to a proactive, data-driven approach. Treat capacity like electricity — pay for what you use, not what you think you’ll need.
How to address it: Leverage hyper-scaler tools to run workload assessments, analyze data and optimize instance sizes. Adopt an elastic architecture and auto-scaling, spot instances, and serverless compute for on-demand efficiency. Hyperscalers offer tools and programs that ensure the system auto-adjusts and meets demand seamlessly. Use automated scripts to shut down unused resources during non-use hours – seems like a no brainer, but it’s a discipline that is often missing.
2. Cloud Sprawl Across Hybrid and Multi Cloud Environments
In the absence of a centralized view of resources across all clouds, it’s impossible to track spending effectively.Proliferation of accounts, regions, and services across hyperscalers and on prem environments leads to visibility gaps and duplication – think shadow IT. The result? Fragmented silos, complex environments, security vulnerabilities and poor governance. Multi-cloud shouldn’t mean the chaos of “multiple” clouds rather a single, cohesive, integrated and efficient environment.
How to address it: Use a platform to get a single pane of glass view of all cloud spending, usage, and governance. Zensar’s The Vinci brings multiple tools to a single pane of glass and integrates the likes of CloudWatch, Datadog, Splunk etc. with noise reduction – using AIOPs. These tools can aggregate data, pinpoint waste and allocate costs appropriately. Consolidate redundant services, standardize tooling, and use cross-cloud benchmarks for workloads.
3. Lack of FinOps & Cost Governance
Many enterprises treat cost optimization as an IT-only activity rather than a shared responsibility. Cost is not just a back-office concern — it needs to be established as a shared responsibility across business and IT. Lack of accountability, siloed structure, opaque bills and no policy enforcement or standardized framework leads to misconfiguration and mounting bills.
How to address it: Reiterate FinOps as a culture across engineering, business and finance teams. Set budget alerts, usage quotas, and resource tagging policies, using best practices. Continuously track metrics like “cost per transaction” and connect to business outcomes. It’s not about saving money but about maximizing the business value of every dollar spent on the cloud.
4. AI & Compute-Heavy Workloads
AI adoption race is driving unpredictable compute and storage costs. Quick comparison – AI workloads are 16x heavier than the others on an average. With many AI workload experimentations, training and questionable business value, this can quickly become a cost nightmare. Treat AI cost as a measurable unit tied to ROI.
How to address it: Choose the right model size to avoid waste. Establish architecture matching the workloads. Run training in the cloud for elasticity, inference at the edge/on-prem for cost control. Actively manage environments – management cost is significantly lower than the overruns.
Challenge the impulse of delaying tech investments to save cost.
The cost pressure is real and it’s tempting to pause investments and manage costs — but postponing modernization is like burning your house to stay warm for the moment. Delaying tech investments takes enterprises behind faster than people believe – think a day of delay accounts for over 7 days of setback. Emerging tech isn’t risky — unmanaged tech is. So, anchor innovation in value, governance, and control, link investments to support three key parameters – growth, experience and productivity.
Use the PRISM Framework to evaluate responsible tech adoption
P – Performance: Is the workload delivering measurable ROI? R – Right-Sizing: Are we provisioning right to meet demand or plan for growth? I – Impact: Does the technology adoption reduce friction across functions? S – Sustainability: Are we building with long-term architectural and energy efficiency in mind? M – Monitoring: Are there feedback loops to measure value, utilization, and cost over time?
Changing gears from hyper acceleration to conscious innovation
If the post-pandemic years (2020-2023) were all about hyper acceleration, i.e. responding to crisis with speed and technical prowess, now is the season for a far more outcome-oriented, conscious and strategic approach. This means building systems that are resilient and scalable, investing in initiatives that have measurable ROIs, and embedding governance into design decisions. The proliferation of ChatGPT has brought on spotlights innovation but it must be thoughtful, pragmatic, democratized, defensible, sustainable.