How To Optimize Cloud Costs In 2026

After years of rapid scaling fueled by digital transformation and AI-driven workloads, organizations in 2026 are under pressure to become more financially disciplined. Cloud spending continues to increase - especially with the surge in GPU-heavy AI training and inference workloads - yet financial leaders are demanding more predictable budgets and stronger ROI.

This doesn’t imply a slowdown in innovation. Instead, companies need a smarter, more data-driven model to align engineering velocity with financial outcomes. Below, we unpack five practical cloud cost challenges most companies face and explain how modern approaches can mitigate them without compromising performance or customer experience.

1. Cloud Waste 

Cloud waste remains one of the biggest contributors to overspending, largely due to idle resources, oversized infrastructure, zombie assets, and insufficient provisioning strategies. According to 2025 industry estimates from ByteIota, organizations waste nearly 30 - 32% of their total cloud spend, amounting to $200–230 billion annually - largely due to idle resources, over-provisioning, and inefficient pricing decisions.

Modern causes of cloud waste include oversized compute and GPU instances, unused or duplicate resources left running across environments, missing shutdown policies, aggressive autoscaling settings, forgotten storage volumes or snapshots, and commitments like Savings Plans or RIs that remain underutilized.

Solution

Organizations must rely on real-time cost visibility and automated remediation to control waste. Advanced AI-powered cost analytics platforms can provide resource-level insights, predict right-sizing opportunities, detect anomalies instantly, and automatically shut down idle workloads. Combined with consistent tagging standards and clear team ownership models, these capabilities enable engineering teams to proactively eliminate cloud waste and maintain lean cloud environments.

2. Difficulty in Predicting Long-Term Cloud Requirements

Forecasting cloud requirements has become even more challenging in 2026 as workloads grow more dynamic, AI models introduce unpredictable GPU needs, and architectures evolve across multi-cloud, hybrid, and edge environments. Even sophisticated forecasting models cannot perfectly predict consumption patterns.

This uncertainty often leads to over-buying commitments, under-utilizing Savings Plans, relying on expensive on-demand pricing, or making architecture decisions based on assumptions rather than data.

Solution

The better approach is to shift from long-term forecasting to smarter and more controlled provisioning. Organizations should begin by establishing a strong governance framework with clear ownership of cloud resources. Workloads must be classified based on whether they require static, predictable, or dynamic provisioning. Teams should rely on platform-native optimization tools such as AWS Savings Plans and Compute Optimizer, GCP Active Assist, and Azure Advisor with AI Ops for more accurate recommendations. Infrastructure automation tools - combined with auto-provisioning for dynamic workloads - ensure that resources scale efficiently and only when needed. For GPU clusters, autoscaling, job batching, and shared pool designs further reduce unnecessary costs.

3. Choosing the Right Cloud Services

With hundreds of different options, even experienced cloud practitioners often find it difficult to choose the right set of cloud-service capabilities for their needs. For example, Amazon EC2 now offers over 600 instance types across multiple instance families. 

Choosing the wrong combination of instance types and sizes can easily introduce persistent cloud cost inefficiencies and business risks.

Solution

Organizations need a balance of business context and technical intelligence when selecting services. Decisions should take into account deployment goals around latency, reliability, and performance; cost-to-value metrics such as cost per transaction, inference, or customer; the operational capability needed to manage the chosen service; and the long-term implications of pricing models and vendor lock-in. A robust FinOps analytics platform helps simulate cost impact before committing to any resource, while expert partners can offer guidance in navigating complex pricing models and avoiding underutilized commitments.

4. Lack of Performance Tracking and Benchmarking

Without performance benchmarking, it becomes nearly impossible to understand how resources are being consumed or whether the associated costs are justified. Tracking only CPU and memory is no longer sufficient in 2026. Modern cloud environments demand broader visibility that includes GPU utilization, cost per workload unit, storage access patterns, Kubernetes cost allocation, egress consumption, and even carbon impact.

Solution

Organizations must adopt real-time monitoring and centralized dashboards that consolidate performance, cost, and utilization metrics. Native tools like AWS Cost Explorer, Azure Cost Management, or GCP Billing offer foundational insights, but they should be supplemented with continuous logging, audit trails, and Kubernetes cost monitoring tools such as Kubecost or CloudKeeper Lens. Clearly defined KPIs and OKRs linked to business outcomes help teams understand the trade-offs between cost, reliability, and performance. SLO-based autoscaling further ensures that workloads scale only when required, maintaining both efficiency and performance.

5. Lack of Organizational Alignment 

FinOps is a collaborative discipline, yet many organizations continue to operate in silos. When engineering, finance, and procurement teams work independently, communication gaps widen, cost decisions become reactive, and architectural choices often conflict with budget expectations. The FinOps Foundation continues to identify this cultural misalignment as one of the most significant challenges in cloud financial management.

Solution

To overcome this, organizations must cultivate shared responsibility for cloud spend. This begins with bringing finance, engineering, procurement, and leadership together under a unified FinOps framework based on the Inform, Optimize, and Operate phases. Teams should follow consistent reporting practices, rely on showback dashboards, and participate in regular cross-functional FinOps reviews. Policy-as-code guardrails ensure governance remains consistent and automated. Upskilling programs and FinOps certification tracks motivate cloud and DevOps professionals to build stronger financial awareness and create a culture where cost is considered an essential part of application design.

Harnessing the Power of Collaboration

Cloud cost optimization is not a one-time project but an ongoing discipline that evolves with workloads, business needs, and industry innovation. Every organization has unique priorities, making it essential to adopt an approach that combines real-time visibility, automation, governance, and cross-team collaboration.

A well-structured FinOps function - whether built internally or supported by an experienced external partner - can help organizations navigate complex pricing models, manage commitments effectively, optimize AI and GPU workloads, and maintain predictable, efficient cloud environments. As cloud adoption accelerates and AI becomes more central to business operations, a strong FinOps practice ensures that organizations can continue to innovate while making the most of their cloud opportunities.

This article was originally published on Forbes Technology Council.