The use of generative AI (GenAI) is growing at an unprecedented rate across various industries. Significant technical advances in AI models, computing power, and data, along with the benefits in productivity, cost, and ease of use, have fueled this expansion. GenAI’s global market size is expected to reach $62.72 billion this year and reach $356 billion by 2030.
With this adoption has also come tremendous demands for resources. As a result, organizations are now focused on how to use GenAI efficiently, including reducing electricity usage, lowering GPU consumption, and, overall, using less carbon-intensive and more sustainable infrastructure. The rise of DeepSeek has only heightened the interest in AI infrastructure optimization.
This demand has also driven interest in flexible consumption models and scalable infrastructure. To avoid large upfront costs, companies are adopting flexible subscription and pay-as-you-go models, allowing them to start small and scale quickly. As prices fall and flexible models proliferate, GenAI will become increasingly pervasive, driving higher demand. In the long term, flexible models will make GenAI more efficient, cost-effective, and sustainable. However, implementing Gen AI in a hybrid, flexible model requires several steps and considerations.
Assessing AI Needs
Developers should start by clearly understanding the workload and performance attributes of their Gen AI projects. Define the use case — LLMs, copilots, image generators, chatbots or multimodal AI — and map the AI pipeline from data preprocessing, model training, model evaluation, inference, and fine-tuning. Profiling tools help scope areas including throughput, latency, concurrency, and burstiness. Each part of the AI pipeline has different storage requirements, so it is often necessary to align the infrastructure to the workload on a bespoke basis. Using solutions that provide great performance may be wasteful if the usage is less than expected. Flexible consumption models can help in adapting for quickly changing needs.
In addition, consider the unique characteristics of your company size, geography and resources. For example, some regions offer lower energy costs, while others face higher energy expenses. Technical factors — such as data center cost structure, cloud computing costs, data transfer costs, and network-related expenses — are critical.
Latency and Response
Performance is susceptible to latency, as even 100- to 200-millisecond delays can impact the user experience. Infrastructure should ensure that compute, network, and storage layers are all built to handle the load generated by Gen AI applications. Minimize data movement by co-locating compute and storage. Smart caching and pre-loading often-used prompts, models, or embeddings in memory can also reduce lag.
Another way to address latency is by considering data gravity — the concept that as a project grows larger, the more difficult and expensive it becomes to move data around. One misconception is that all data is the same and it does not matter where it is stored. But data is not the same and pulling it across the internet may slow latency and impact performance. So, avoid moving large datasets and increase proximity to data. For example, train models using existing data.
Workload Changes
One pitfall to avoid is in not understanding how workload requirements change over time. Gen AI workload demands can change significantly between development, testing and production. As a result, developers should design for modular, scalable growth to accommodate rapid growth in usage, customer demand, or product expansion.
Bottlenecks at the storage, network, or compute layers are a common issue with Gen AI. Underscoped components can harm the overall experience. A unified solution that addresses all three layers as a single entity will keep system reliability and meet performance needs even under heavy loads.
Hybrid Solutions
Many organizations now utilize hybrid solutions to address the rising costs of data storage. Still, there is also the question of which data to store in the cloud or on-premises in a hybrid architecture. Key factors in this decision include security, cost, performance, and sustainability. Data security and privacy are critical to ensure an AI infrastructure that is safe and compliant. Data that is private, confidential or has intellectual property is typically best managed on premises.
GenAI infrastructure is expensive to build and maintain due to the hardware, compute, energy and security costs. Many enterprises may consider on-premises storage solutions that support AI if they can reach a cost per GB that is attractive. This can be done by consuming infrastructure as a service from a service catalog that is aligned to AI storage requirements. Otherwise, cloud options can make sense because of the scale of cloud providers.
Performance Architecture
Organizations typically choose on-premises solutions because they allow for purpose-built architectures that support consistent performance, ranging from object storage to extreme multiprocessing. These storage solutions, which can be referenced in the service catalog, can then be aligned to the upstream components such as compute and networking to ensure key service metrics are met.
Sustainability is also key because of the massive electricity needs of data centers — as well as concerns for long-term energy savings and regulatory compliance. On-premises solutions provide more control and efficiency gains. While they are typically remote, cloud providers benefit from scale, which can offset some sustainability concerns.
Flexible Wins
Flexible consumption models provide organizations with more options to optimize their spending according to their specific needs. The high cost and energy demands of AI, in particular, make flexible consumption models an attractive option. The recent introduction of more cost-effective options, such as DeepSeek, has increased interest in starting small and scaling quickly.
Hybrid models are well-suited to this approach, supporting pay-as-you-go subscription models that allow for quick scaling up and down as needed. Tasks that require real-time processing can be kept on-premises, while less computationally intensive workloads can be moved to the cloud. Additionally, businesses want the flexibility of cloud subscriptions applied to their on-premises infrastructure. The goal is to create a hybrid world where both cloud and on-prem operate on subscription models.
Finally, service catalogs that are part of consumption-based usage help mitigate overspending or underperformance. This enables customers to consume only what they require from specific classes for specific durations of time.
In a new world of generative AI, infrastructure will rely on a strategic mix of on-premises and cloud environments. Due to its significant resource demands and requirement for rapid scalability, AI is the ultimate hybrid application. Flexible consumption models are key to this transformation because they enable enterprises to minimize upfront costs while maintaining maximum flexibility to expand in either on-premises or cloud settings.
The post GenAI and Flexible Consumption Models Reshape Hybrid Storage Infrastructure appeared first on The New Stack.
Due to its significant resource demands and requirement for rapid scalability, AI is the ultimate hybrid application.