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Remember when capacity planning was the bane of every IT professional’s existence? Those spreadsheets predicting server needs months in advance, the painful procurement cycles, and the constant fear of under-provisioning during Black Friday? Public cloud promised to make all that go away with its infinite shelf space and pay-as-you-go pricing. For many organizations, it did—until the bills started arriving.
Now, as enterprises grapple with cloud spending that Gartner forecasts to grow 20.7% annually to reach nearly $600 billion and a CNCF survey showing 49% of organizations report spending more with Kubernetes, a surprising truth emerges: the discipline of capacity planning hasn’t become obsolete. Instead, its absence has created new problems that mature organizations can no longer ignore.
TL;DR: Key Takeaways
- Public cloud’s “infinite shelf” illusion eliminated capacity planning discipline, creating unpredictable costs and chronic over-provisioning—with average Kubernetes clusters running at only 13-25% utilization
- Reactive scaling replaced proactive planning, leading to overage fear, defensive under-provisioning, and constant firefighting instead of strategic infrastructure decisions
- Predictable infrastructure models transform capacity planning from burden to competitive advantage through fixed monthly pricing, dedicated resources, and transparent performance characteristics
- Organizations with capacity planning discipline gain cost clarity, performance predictability, and architectural flexibility—enabling alignment between infrastructure growth and business objectives
- The future belongs to teams that evolve capacity planning for modern infrastructure realities, not those that abandoned it entirely
How Public Cloud Made Capacity Planning “Disappear”
The public cloud revolution seemed to solve capacity planning overnight. Why predict resource needs when you could provision instantly? Why plan when you could scale automatically? This “infinite shelf” mindset fundamentally changed how organizations approached infrastructure.
The consumption model encouraged reactive thinking. Research from Gartner finds that without an effective cloud cost optimization strategy, organizations can overspend by as much as 70%. Teams spun up resources without considering long-term implications, creating what experts call “cloud sprawl”—the digital equivalent of leaving the lights on in empty rooms.
Auto-scaling became both savior and saboteur. While it prevented outages during traffic spikes, it also masked underlying capacity decisions. Teams set resource requests based on worst-case scenarios, leading to chronic over-provisioning. The average Kubernetes cluster runs at only 13-25% CPU utilization and 18-35% memory utilization, representing massive waste.
The “overage fear” phenomenon emerged as teams encountered surprise bills. Rather than plan appropriately, many organizations switched to defensive postures—under-provisioning resources to avoid costs, then scrambling when performance suffered. This reactive cycle replaced the proactive discipline of capacity planning with constant firefighting.
Why Capacity Planning Still Matters
Performance remains king, regardless of cloud models. Applications don’t care whether they’re running on-premises or in AWS—they need adequate resources to serve users effectively. Seventy percent of respondents cited over-provisioning as a top issue leading to overspending, but the solution isn’t less planning—it’s better planning.
Cost discipline requires intentionality. Organizations are billed continuously as consumption occurs, instead of once-off as it happens when they procure their data center capacity. Without planning frameworks, teams lose visibility into spending patterns and struggle to correlate costs with business outcomes.
Environment separation becomes critical as organizations mature. Development, staging, and production environments each have different capacity profiles. Proper planning ensures that development environments don’t consume production-level resources while maintaining adequate performance for testing and validation.
Long-term architectural decisions depend on capacity understanding. Whether choosing between microservices and monoliths, selecting database architectures, or planning disaster recovery, capacity planning provides the foundation for sustainable technical decisions.
Predictable Infrastructure Brings Planning Back
The return of capacity planning requires predictable infrastructure models that make planning both possible and valuable. OpenMetal’s approach transforms capacity planning from a burden into a strategic advantage through fixed monthly pricing and dedicated resources.
Fixed Pricing Eliminates Consumption Anxiety
Unlike hyperscale providers that charge per instance, per gigabyte, or per operation, OpenMetal uses hardware-based pricing. Teams can fully utilize allocated resources without fear of surprise bills. This predictability allows for proper capacity modeling—you can plan infrastructure growth against business milestones rather than reacting to billing shocks.
Ramp pricing and migration assistance eliminate the double-cost exposure that traditional capacity planning aimed to avoid. Organizations can transition smoothly without maintaining parallel infrastructure investments.
Dedicated Resources Enable True Planning
OpenMetal’s hyperconverged Cloud Cores provide dedicated compute, storage, and networking resources. Each deployment includes enterprise-grade bare metal servers with high-performance NVMe storage, DDR4/DDR5 ECC RAM, and Intel Xeon processors. This dedicated approach means capacity planning becomes about optimizing known resources rather than guessing at shared infrastructure performance.
Elastic scaling happens through additional servers rather than mysterious auto-scaling policies. Teams can expand Cloud Cores in approximately 20 minutes, making capacity adjustments predictable and controlled.
Networking Architecture Supports Growth
OpenMetal’s networking approach supports sophisticated capacity planning. Public networking includes dual 10 Gbps NICs with DDoS protection, while private networking provides unmetered 20 Gbps per server connectivity. This architecture allows teams to plan network capacity alongside compute and storage, creating comprehensive infrastructure models.
VLANs and VXLAN overlays enable proper environment isolation without complex networking gymnastics. Teams can model capacity for development, staging, and production environments within the same infrastructure, maintaining separation while sharing underlying hardware efficiently.
Storage and Performance Predictability
Ceph-powered storage provides unified block and object storage with predictable performance characteristics. Unlike public cloud storage with variable performance tiers and complex pricing models, OpenMetal’s storage delivers consistent performance that teams can plan around.
GPU clusters and specialized hardware configurations support demanding workloads from AI/ML training to high-performance computing, all within the same predictable pricing model.
Capacity Planning as a Strategic Advantage
Modern capacity planning becomes a competitive advantage when built on predictable infrastructure. CTOs can align infrastructure growth with business objectives rather than reacting to consumption surprises.
Budget clarity transforms infrastructure from a cost center to a strategic investment. Fixed monthly pricing enables accurate forecasting, allowing finance teams to model infrastructure costs alongside revenue projections. This clarity supports better business decision-making and eliminates the budget anxiety that consumption-based pricing creates.
Team morale improves when infrastructure decisions become proactive rather than reactive. Developers can focus on building features rather than optimizing around unpredictable resource costs. Operations teams shift from firefighting billing surprises to planning infrastructure evolution.
Architectural agility emerges from resource predictability. Teams can experiment with different approaches—monoliths versus microservices, different database architectures, various deployment patterns—without worrying about consumption cost implications. This freedom supports innovation while maintaining cost discipline.
Performance optimization becomes systematic. With dedicated resources and predictable performance, teams can establish baseline metrics and optimize incrementally. This approach produces better long-term results than the constant resource juggling that consumption-based models encourage.
The Renaissance of Infrastructure Discipline
The public cloud taught us that infinite resources don’t eliminate the need for resource discipline—they change its character. Organizations that embrace capacity planning on predictable infrastructure gain competitive advantages: cost clarity, performance predictability, and architectural flexibility.
OpenStack adoption is surging as organizations seek virtualization alternatives, driven partly by the realization that infrastructure predictability enables better business outcomes. The companies thriving in today’s cloud landscape aren’t those that abandoned capacity planning—they’re those that evolved it for modern infrastructure realities.
Capacity planning isn’t dead; it’s been reborn. The organizations that recognize this shift and adapt their infrastructure strategies accordingly will find themselves with a sustainable competitive advantage in an increasingly cloud-centric world.
Want to see how predictable infrastructure transforms capacity planning? Contact OpenMetal to learn about our fixed-price private cloud solutions and migration support.
Works Cited
- Gartner, Inc. “Gartner Forecasts Worldwide Public Cloud End-User Spending to Reach Nearly $600 Billion in 2023.” Gartner Newsroom, 31 Oct. 2022, www.gartner.com/en/newsroom/press-releases/2022-10-31-gartner-forecasts-worldwide-public-cloud-end-user-spending-to-reach-nearly-600-billion-in-2023.
- “How to Manage and Optimize Costs of Public Cloud IaaS and PaaS.” Gartner Research, www.gartner.com/en/documents/3982411.
- InfoQ. “CNCF Survey: Half of Organizations Spend More with Kubernetes, Mostly Due to Overprovisioning.” InfoQ, 4 Mar. 2024, www.infoq.com/news/2024/03/cncf-finops-kubernetes-overspend/.
- OpenInfra Foundation. “OpenInfra Marks 15 Years of OpenStack as Adoption Surges and Epoxy Release Lands.” HPCwire, 2 Apr. 2025, www.hpcwire.com/off-the-wire/openinfra-marks-15-years-of-openstack-as-adoption-surges-and-epoxy-release-lands/.