Public clouds burnish their on-premises options

One of the more notable developments in enterprise IT was public cloud providers offering on-premises services. These appear to be regaining steam, as Amazon Web Services recently unveiled second-generation AWS Outposts racks packed with cutting-edge hardware, such as fourth-generation Intel Xeon Scalable processors, to boost performance for on-premises workloads.

AWS has also introduced specialized instance types, such as bmn-sf2e, for latency-sensitive, compute-intensive, and throughput-oriented applications such as real-time data distribution, risk analytics, and 5G core networks. These upgrades reflect a growing reality: The public cloud can’t meet every need, and enterprises are shifting resources toward hybrid cloud and on-prem models to address evolving requirements.

Is it likely that more public cloud providers will follow suit? At the core, this is about staying in step with multiplatform enterprise strategies and preparing for the next decade of AI-accelerated workloads. The world of enterprise IT is changing quickly, and cloud providers want to ensure their relevance in this new era.

The need for diverse platforms

Enterprises’ multiplatform workload strategies will continue to drive the rise of on-prem offerings by public cloud players. Increasingly, organizations are choosing to deploy workloads across various platforms—public cloud, private cloud, and on-premises. This diversification reflects the need for flexibility, cost-efficiency, and control.

On-prem solutions can help enterprises prioritize cost predictability and performance optimization. With pay-as-you-go pricing models and premium costs for cutting-edge hardware, public clouds are proving too expensive for many organizations. This has opened the door for other models, such as on-premises or hybrid cloud, which give enterprises more predictable and, in many cases, lower operating costs.

AWS’s new Outposts racks are a good example of cloud providers aligning with these trends. Organizations can leverage AWS’s infrastructure and tools in their data centers while benefiting from improved compute, memory, and network capacity tailored to mission-critical workloads. The flexibility to independently scale computing and networking resources is another key advantage, reducing the need to buy entire racks and allowing for more strategic scaling without unnecessary overhead.

Hybrid and on-prem suit an AI-driven future

One of the most significant reasons for the growth of hybrid and on-prem solutions is the looming challenge of generative AI workloads. Within the next decade, enterprises are expected to heavily invest in AI systems, both for training models and for deploying real-time inferencing applications. These workloads are resource-hungry, and their cost has proven to be a substantial barrier to growth. Public clouds aren’t cost-competitive for enterprises needing consistent, long-term access to large amounts of compute and storage power.

By bringing AI operations to on-premises environments or hybrid models, enterprises can better predict and manage expenses, minimize latency, and remain in full control of their data. These models also allow organizations to focus on the long term instead of being at the mercy of cloud pricing dynamics, which can change unpredictably. Additionally, hybrid cloud models position enterprises for the next wave of AI innovation. AI is a rapidly evolving space where agility is a must. Enterprises that invest in versatile architectures across cloud and on-prem data centers will have the flexibility to scale and pivot their operations as the technology matures.

Are public clouds pricing themselves out?

It’s easy to see why the shift to hybrid and on-prem models is happening, but it’s equally important to consider why public clouds are losing ground. Much of this can be attributed to pricing for the most resource-intensive workloads. Massive parallel processing and specialized accelerators quickly become cost-prohibitive in many public cloud environments.

The new AWS instance types indicate that public clouds recognize this problem and are attempting to address it by offering tailored solutions for on-prem environments. However, this is more of an exception than a rule, so far. Public clouds remain heavily invested in premium pricing models for their centralized services, which won’t work for enterprises looking to scale AI operations at a reasonable price point.

The path forward

Strategic decisions must support scalability, cost-efficiency, and innovation in an AI-driven future. Simply put, enterprises need to take control of their infrastructure decisions rather than being wholly reliant on public cloud services.

First, organizations must deeply analyze their workloads. Developing a clear understanding of where to place each workload—public cloud, on-prem, or hybrid cloud model—is essential. AI workloads, in particular, should be examined through the lens of performance needs, latency requirements, and long-term cost implications. The public cloud might be ideal for development and initial testing, but long-term operations will often benefit from more cost-effective on-premises solutions.

Second, enterprises need to build flexibility into their architectures. By investing in hybrid cloud infrastructure, businesses can leverage the best of both worlds: the scalability of public cloud and the control of on-prem environments. This means considering vendors such as AWS, which offer compelling hybrid and on-prem options, but also keeping an eye on emerging players and alternatives that may offer better pricing or specialized solutions.

Finally, sustainability and future-proofing must be core parts of the equation. AI workloads will become essential to business strategy, and the cost will continue to climb. Enterprises must adopt approaches that are not just cost-efficient now but that help set the stage for AI-driven growth in the next 10 years. Choosing platforms that balance innovation and cost will ensure stability in a market that is constantly changing.