Platform engineering teams face growing complexity and cost challenges
There’s a clear reality in front of us, cloud, Kubernetes, and AI are not slowing down. They’re accelerating. And platform engineering teams are expected to lead this velocity shift. These teams are the engine room behind enterprise innovation. They’re integrating AI, building and scaling infrastructure, optimizing for performance, and, at the same time, fighting to keep costs under control. That’s a hard combination, even for experienced teams.
In our latest research at Rafay Systems, 93% of platform teams said they’re facing serious obstacles in managing Kubernetes infrastructure. That number speaks for itself. Without proper visibility into their Kubernetes environments, most organizations aren’t fully aware of what they’re spending, where resources are going, or how systems could be improved. At the same time, new AI workloads only stack more demand on top of existing complexity.
Platform teams are typically small, yet the scope of their responsibility is gigantic. They’re juggling fragmented toolchains, legacy systems, cloud sprawl, compliance pressures, and an increasing expectation to deliver fast and reliably. If you’re in the C-suite, overlooking these teams, or under-investing in them, is a fast path to stalled innovation and rising infrastructure costs.
To move ahead, executive teams need to support platform engineering functions not just in name, but with budgets, team structure, and tools that match their scope. Doing so creates a foundation for scalable, efficient innovation across the organization.
Traditional cost management tools are outdated
Most enterprises still rely on old cost management tools to track modern systems. That’s a problem. These tools weren’t built for today’s containerized, multi-cloud, or AI-powered environments. They don’t see what’s happening at the Kubernetes layer. They don’t offer real-time insights. And they definitely don’t handle cost visibility across multiple environments working in parallel.
This is a fundamental mismatch. When your infrastructure evolves, your monitoring stack has to evolve with it. What we’re seeing in the market is that almost one-third of organizations underestimate the total cost of Kubernetes ownership. Another 44% are now prioritizing cost visibility as a top focus, according to Rafay Systems research.
Legacy tools typically miss two key things: granularity and adaptability. Granularity means seeing the real-time cost of individual workloads or containers, and knowing exactly how resources are being used across teams. Adaptability means responding to infrastructure that changes constantly, across clouds, across clusters, across use cases. Without that visibility and control, teams operate blind, and budgets suffer.
This isn’t something to delegate endlessly down the chain. If you’re leading technology or finance for a modern organization, you need to know whether your teams are optimizing for efficiency, or just reacting to cost overruns. And most legacy tools won’t tell you that.
To fix this, organizations need to shift into cost-aware design. That means building infrastructure not just for scalability, but for transparency and control. It means putting tools in place that let you forecast cost, understand cost, and minimize waste, at scale. That’s how you protect margin while continuing to innovate.
Integrating AI and generative AI workloads intensifies the demands
We’re in the early stages of a seismic shift in enterprise infrastructure. AI and generative AI are no longer speculative projects, they’re operational priorities. But the transition is straining existing systems. As organizations double down on deploying large models and enabling AI application development, they’re exposing a major gap in readiness, especially at the infrastructure level.
Platforms are being stretched. GPU capacity is limited and expensive. AI training workloads can quickly burn through compute resources. Without efficient methods for allocating and managing those resources, teams are forced into trade-offs between speed, cost, and availability. And if those decisions aren’t automated or properly structured, they slow down progress.
According to Rafay Systems research, 95% of organizations plan to increase their usage of Kubernetes in the next year. In parallel, 96% say they need efficient ways to build and deploy AI-powered applications, and 94% say the same for generative AI systems. That convergence is creating unsustainable pressure on platform teams, unless they adjust fast.
What’s required now is a shift toward GPU-aware infrastructure management. This means developing capabilities like GPU sharing, intelligent workload scheduling, and automated cost-performance optimization. Without these, it’s easy to overpay or underdeliver.
For C-suite leaders, this is about direction-setting. AI capability isn’t just a technology race, it’s a resource race. If your platform teams don’t have the right capabilities in place, you’ll feel it in timelines, costs, and talent retention. The faster AI scales inside your business, the more essential it becomes to treat infrastructure management as a business-critical function, and fund it accordingly.
Automation and self-service are key strategies for modern platform engineering
Manual operations don’t scale. That’s not up for debate anymore. The most effective teams are reducing their dependency on manual provisioning, scripting, scaling, and monitoring, and moving toward automation and self-service.
Platform teams that can automate Kubernetes cluster provisioning, standardize infrastructure templates, and enable developers to self-serve resources are consistently outperforming those that can’t. They move faster. They introduce fewer errors. And they gain real control over usage, without sacrificing agility.
The business benefit here is clear. Self-service reduces delays, and automated guardrails protect budgets. Teams still get what they need, but within defined parameters. This creates a more sustainable model, where innovation can scale without dragging infrastructure costs along with it.
Rafay Systems’ research points to a clear trend: organizations are prioritizing cost optimization around Kubernetes, visibility and showback for infrastructure expenses, and chargeback models for internal teams. These aren’t fringe ideas, they’re becoming standard practice for organizations that want proper financial control over their technical environments.
For executives, this is the time to act. Investing in automation and self-service is a way to free up your top engineering talent to solve harder problems, and to gain clarity over where your real infrastructure costs live. It’s how you stay competitive without bloating budgets or slowing execution. And it’s how your teams stay focused on what actually moves the business forward.
Empowering platform teams with advanced tools and strategic frameworks is essential for sustainable innovation and competitive advantage
Recognizing the value of your platform team isn’t enough. You have to act on it. These teams sit at the core of everything your developers, data scientists, and AI engineers rely on. If they’re not equipped with the right tools and frameworks, everything downstream moves slower, costs more, and becomes harder to scale.
The objective is clear, platform teams need access to unified, automation-driven environments with full visibility and control over resources. They also need the ability to enforce standardization without adding bureaucracy. When this balance is achieved, operational complexity decreases while delivery speed increases. That creates real leverage across the business.
What we’re seeing in the market is that organizations that consistently scale innovation without blowing past infrastructure budgets have something in common: they empower their platform teams. These organizations prioritize cost visibility, invest in automation, and insist on maintaining consistent deployment patterns across environments. They don’t overengineer, but they don’t underinvest either.
The research supports it. Rafay Systems found that platform teams consistently struggle with Kubernetes cost visibility, challenges in maintaining standardization, and increasing AI demands. These aren’t separate problems, they’re linked. Disconnected systems and low visibility don’t just cause budget overruns, they slow delivery and drain your best talent.
If you’re in an executive role, your job is to clear the path for strategic execution. That means putting systems in place, not just people. Enable your platform teams with tools that automate, standardize, and monitor at scale. Create frameworks that reinforce accountability without slowing innovation. When you do, you’re not just solving current problems, you’re building a foundation to handle future complexity without compromise. This isn’t overhead. It’s strategy.
Main highlights
- Platform teams are hitting critical complexity limits: Leaders should recognize that platform engineering teams are under pressure from growing cloud, Kubernetes, and AI demands. Supporting them with strong tools and clear mandates is essential to avoid stalled innovation and uncontrolled infrastructure costs.
- Legacy tools can’t keep up with modern environments: Traditional cost-tracking systems lack visibility and flexibility for containerized, multi-cloud setups. Executives should invest in purpose-built platforms with granular insights to accurately manage spend and resource allocation.
- AI and generative AI are stressing infrastructure hard: As 95% of organizations ramp up Kubernetes while pushing AI development, GPU management and workload orchestration must become core capabilities. Leaders must act now to avoid performance bottlenecks and rising training costs.
- Automation and self-service are now strategic priorities: Manual operations are a drain on speed and efficiency. Executives should prioritize platform investments that enable automation, standardization, and self-service to keep teams productive and costs predictable.
- Empowered platform teams drive competitive advantage: Success requires more than just acknowledging platform teams, it demands action. Decision-makers should ensure these teams have strategic frameworks and scalable tools that align with long-term innovation goals while maintaining cost control.
