The cloud once promised boundless scalability, flexibility, and efficiency. But with the rise of generative AI, many organizations are experiencing a rude awakening in the form of unprecedented cloud bills. According to Tangoe’s recent report, nearly three-quarters of enterprises find their cloud bills "unmanageable" due to the compute demands of AI and the rising cost of GPU and TPU usage.

There is a solution for businesses struggling to justify cloud expenses in an AI-driven world: repatriating select AI workloads to on-prem infrastructure.

The Case for Repatriation

With cloud providers charging a premium for high-performance compute resources, running advanced workloads in the cloud can quickly lead to skyrocketing expenses. According to our research, which we will preview shortly, 39% of respondents were either very concerned or extremely concerned about the costs associated with running artificial intelligence and/or machine learning workloads in the cloud.

Repatriation—moving select workloads from the cloud back to on-premises infrastructure—might be the solution to this rising anxiety, especially for AI workloads with variable and unpredictable usage patterns. By bringing these workloads on-prem, companies with sufficient infrastructure can sidestep hidden cloud costs like egress fees, storage retrieval charges, and idle resource expenses that often catch organizations off guard.

This strategy can be particularly effective for software that runs efficiently on commercial or off-the-shelf hardware. Most AI applications, including machine learning and data processing workflows, do not require custom, high-end servers or specialized hardware, making them ideal candidates for repatriation. Utilizing commodity hardware allows companies to handle demanding AI workloads affordably, reducing dependency on cloud providers’ premium-priced infrastructure while retaining flexibility and control over their environments.

Key Benefits of Repatriating AI Workloads

1. Cost Predictability and Control In the cloud, consumption-based billing models make it difficult to predict costs. By contrast, on-prem infrastructure provides a fixed-cost model, with the possibility of amortizing the cost of hardware over time, which in turn allows for greater predictability. We’ve found that customers can save 50% of their cloud costs by repatriation.

2. Enhanced Performance High-performance GPUs and low-latency networking are essential for AI. By hosting workloads on-prem, companies gain more control over these factors, ensuring that AI models can run as efficiently as possible. With fewer dependencies on external cloud networks, organizations can potentially reduce latency and boost performance—key elements for real-time AI applications.

3. Improved Data Security and Governance AI and machine learning applications rely on vast quantities of data, often of a sensitive nature. While cloud providers offer robust security measures, repatriating data to an on-prem environment can provide more granular control over data governance. Organizations can enforce tighter access controls and reduce the risk of data exposure by keeping information within their own firewalls.

4. Reducing Shadow IT and Increasing Accountability Repatriating workloads can help combat the problem of shadow IT, where departments independently spin up cloud resources, adding unexpected costs. On-prem infrastructure requires centralized approval and oversight, making it easier for organizations to ensure that resources are being used effectively and transparently.

When to Repatriate: Identifying Optimal AI Workloads for On-Prem

Not all AI workloads are ideal candidates for repatriation. High-priority, real-time, or mission-critical applications that demand extensive compute power are generally good fits for on-prem infrastructure. Batch processing and model training tasks, which often require high-intensity compute resources for set durations, also tend to perform well in a dedicated on-prem environment.

Conversely, dynamic or temporary workloads may still benefit from the cloud’s flexibility. Identifying which workloads best suit on-premises deployment can save organizations substantial time and money without sacrificing performance or scalability.

When considering your infrastructure, it’s best to bake in the flexibility to choose which environment to run your workloads in by choosing software that can run anywhere. 

Taking a Hybrid Approach to AI Workloads

Repatriating AI workloads isn’t an “all-or-nothing” solution. Hybrid strategies that blend the benefits of cloud and on-prem environments are ideal, allowing companies to leverage each option’s strengths where they’re most valuable. By running predictable, high-intensity AI workloads on-premises and using the cloud for more flexible tasks, organizations can optimize both cost and performance. Let us know how you’ve built out your architecture or if you have any questions at hello@min.io or on our Slack channel.