AI workloads have been on the rise in terms of both their number and complexity, driven by the development of complex recommendation engines and autonomous systems, and now generative AI models.
Most organizations expect to incur increased IT costs with the adoption of AI. New AI functions emerging in the software systems are a crucial infrastructure challenge, as present-day clouds are displaying symptoms of strain.
Cloud platforms were primarily designed to host web applications and storage-intensive enterprise software—not to support the massive computational requirements, high-throughput data pipelines, and accelerated processing that modern AI workloads demand.
As a result, traditional cloud architectures are increasingly misaligned with the needs of AI at scale. This raises an important question: do we need a new generation of infrastructure—purpose-built cloud architectures designed specifically for the AI era? The answer may well define the next phase of technological innovation.
Common Challenges in Managing AI Workloads
Effectively running AI workloads goes far beyond simply allocating compute power. Organizations often face a set of operational and financial challenges that can impact performance and efficiency:
• Underutilized Infrastructure: Resources frequently sit idle between training cycles or inference jobs, leading to wasted spend and reduced overall value from investments.
• Limited Scalability: Rigid, fixed-capacity infrastructure struggles to respond to sudden changes in workload demand, which can result in performance bottlenecks or service disruptions.
• Operational Overhead: Supporting multiple AI tools and platforms across teams increases management complexity and often requires highly specialized expertise.
• Unpredictable Costs: Large upfront investments in hardware may not align with real usage patterns, while sudden spikes in demand can drive unexpected expenses or constrain growth.
Together, these challenges underscore the need for a more adaptive and efficient infrastructure approach. One that can handle diverse AI workloads while minimizing cost, complexity, and waste.
Key Characteristics of Unpredictable AI Workloads
AI workloads behave differently from traditional applications, which makes them harder to plan and manage.
• Some AI tasks, such as model training or large prediction runs, need intense processing power for a short period. Once the task is complete, that power is no longer needed, yet traditional systems often keep running and continue to cost money.
• An AI system is made up of different stages, and each stage places different demands on infrastructure. Data preparation runs well on standard machines, while model training needs far more power. This is where Graphics Processing Units (GPUs) are commonly used.
GPUs are specialized processors that can handle many calculations at the same time, making them well-suited for AI training and inference. Prediction systems, on the other hand, focus on speed and fast response times.
• Data plays a central role in AI. Large datasets and model files are expensive to move and slow to process when systems are not located close to each other. Poor data placement quickly becomes a performance and cost problem.
• Much of AI work involves trial and error. Teams frequently test new models, change settings, and discard results. This leads to temporary environments that appear and disappear, making usage patterns hard to predict.
• AI technology evolves quickly. Models grow larger, tools change, and new hardware becomes available. Infrastructure that is tightly fixed to one setup struggles to keep up.
Design Principles for AI-Ready Cloud Architectures
Cloud systems that support AI must be designed to adjust easily as workloads change.
• Processing resources should turn on and off automatically
Systems should be available when a job starts and shut down when it ends, so you don’t pay for unused machines.
• Data storage and processing should scale separately
Data must remain available even when processing systems are not running. This reduces wasted spending.
• Specialized machines should be used only when required
High-performance machines such as GPUs are expensive and should be assigned only to tasks that truly need them.
• Most setup and management should be automated
Manual setup slows teams down and leads to mistakes. Automation makes systems faster and more reliable.
• Processing should happen close to where data is stored
Running jobs near the data improves speed and avoids high data transfer costs.
• Usage and cost should be easy to understand
Teams should clearly see which jobs or models are using system resources so they can control costs without slowing work.
Architectural Patterns That Fit Unpredictable AI Workloads
Once AI workload behavior is understood, the next step is choosing patterns that reduce waste and simplify operations.
Short-lived infrastructure works better than long-running systems. Training jobs and large processing tasks can run on temporary environments that are created for a specific job and removed once it finishes.
Event-driven designs are useful for prediction systems. When new data arrives, the system starts, processes the request, and stops when the work is done.
Inference refers to the process of using a trained AI model to make predictions or decisions on new data.
Batch processing is used when large amounts of data are processed together at scheduled intervals, such as running predictions on millions of records at once instead of one request at a time.
Separating experimental environments from production systems helps teams test new models without affecting live applications or unnecessarily increasing costs.
Cost Control and Reliability Considerations
AI systems can become expensive and fragile if not designed carefully.
Costs should be tracked at the level of jobs or models so teams understand exactly what they are spending on. Idle systems should shut down automatically to avoid waste.
Failures are common in AI workloads, especially during long training runs. Saving progress at regular intervals and restarting only the failed portion reduces delays and wasted effort.
AI workloads are unpredictable by nature. Cloud architectures that can scale on demand, recover quickly from failure, and align costs with actual usage give organizations a practical way to run AI without losing control over infrastructure complexity or spending.
Conclusion: Building Cloud Architectures That Can Keep Up with AI
AI workloads are unpredictable by design. They scale unevenly, rely on large volumes of data, and place heavy demands on infrastructure. Cloud architectures built for steady, traditional workloads struggle to meet these needs, leading to higher costs, operational complexity, and performance limitations.
Designing cloud environments that can adapt to changing AI demands is no longer optional. Organizations need architectures that scale on demand, use specialized resources efficiently, keep data close to processing, and provide clear visibility into cost and usage.
This is where Aretove can help. Aretove works with organizations to design and optimize cloud architectures that are well suited for AI workloads—focusing on flexibility, cost control, and operational simplicity. By aligning infrastructure design with how AI workloads actually behave, Aretove helps teams run AI initiatives at scale without unnecessary waste or complexity.
The result is an AI-ready cloud foundation that supports innovation today and remains adaptable as AI technologies continue to evolve.