A 6-Minute Guide To On-Premises AI

Quick Summary

• On-premises AI runs on an organisation’s own hardware instead of cloud-based infrastructure.
• Businesses are considering on-prem AI due to data privacy, security, compliance, and sovereignty concerns.
• It provides greater control over AI models and lower long-term costs but requires an upfront investment.
• Cloud AI remains more scalable and easier to deploy, but costs can spiral over time.
• Many companies, especially in finance, government, and retail, are moving workloads from the cloud back on-prem for better control.
• Performance advantages make on-prem AI ideal for real-time applications, reducing network latency.
• Hybrid AI models that blend on-prem and cloud are becoming a preferred approach for cost and flexibility.

Introduction

As AI adoption grows, organisations are re-evaluating where they run their AI models. While cloud-based AI services offer flexibility and scalability, they also introduce concerns around data privacy, security, and compliance – this is becoming an ever increasing concern in the current geopolitical climate – with many organisations desiring data sovereignty within their nation state.

On-premises AI, where AI workloads run on an organisation’s own servers, is gaining traction as a solution. This approach ensures full control over data, compliance with regulations like GDPR, and predictable long-term costs. However, it requires investment in infrastructure and expertise.

Many businesses that initially relied on cloud AI are now repatriating workloads to on-premises infrastructure. This shift is driven by cost concerns, security risks, and performance needs. The ability to run AI locally, without reliance on external providers, ensures that businesses retain autonomy over their critical data and processing power.

On-Premises AI vs. Cloud AI: A Comparison

Factor	On-Premises AI	Cloud AI
Data Privacy & Security	Full control, no external access risks	Relies on provider security measures
Compliance & Data Sovereignty	Keeps data within company / country	Data may be stored in multiple regions
Control over AI Models	Customisable, no vendor lock-in	Limited customisation, vendor-dependent
Performance & Latency	Low latency, optimised for local use	May suffer from network latency
Scalability	Limited by hardware capacity	Instantly scalable on demand
Upfront Cost	Higher with hardware purchase. Consumption models available.	Lower with pay-as-you-go model
Total Cost of Ownership (TCO)	Lower with time or payment model	Expensive once scaled
Maintenance	On-prem service providers handle maintenance	Provider handles updates & upkeep, reducing control
Flexibility	Fully customisable	Limited by vendor service offerings
Cost Predictability	Fixed costs with planned depreciation	Costs can fluctuate based on usage

What’s Possible with On-Prem AI?

On-prem AI can now match cloud AI in capability, thanks to:

• Advanced AI hardware: GPUs (e.g., NVIDIA A100, H100), TPUs, and AI appliances.
• Open-source AI models: Large models like Meta’s Llama 2 can run locally.
• Enterprise AI tools: Platforms like Kubeflow enable on-prem model management.
• Optimised performance: AI inference is faster when processed locally, reducing dependency on internet speeds.
• Custom AI deployment: Organisations can fine-tune AI models specifically for their data and use cases without relying on a third party.

Many industries are adopting on-prem AI for fraud detection, customer analytics, manufacturing automation, and defence applications. Performance is optimised for low-latency AI processing, making it ideal for real-time decision-making. Companies also leverage edge computing AI, where AI runs closer to the source of data (such as in a factory or hospital), ensuring privacy and immediate insights.

Costs: Upfront vs. Total Cost of Ownership (TCO)

On-premises AI has more pricing options, but can come with a higher upfront cost. Truly in-house AI, managed your your IT team, using your owned hardware, can be expensive. However, using services such as HPE Consumer Pricing Model, you can obtain fully optimised AI servers, pre-trained AI models and a pay-as-you-grow pricing. Simply activating more of your AI systems when you need the capacity, and winding it down when you require less AI computing power.

In a nutshell:

• On-prem AI requires some initial investment in servers, storage, and infrastructure. But options for financing are available.
• Cloud AI offers lower upfront costs but can lead to expensive long-term operational expenses.
• Studies suggest on-prem AI can cost 3-5× less than cloud in the long run if workloads are steady.
• Hybrid models (cloud for development, on-prem for production) can offer a cost-effective balance.
• Hidden cloud costs such as data egress fees and pay-per-use AI API pricing can drive up expenses unpredictably.
• On-prem AI gives financial predictability as companies own their infrastructure outright.

Migration Considerations

Organisations shifting from cloud to on-prem AI will need a thorough plan of action that will include your in-house IT team, and an IT project management specialist, like Trustco. Some key considerations before you begin are:

• Data Transfer & Compliance: Ensuring your data is secure and meeting regulatory requirements when migrating personal or corporate information.
• AI Model Portability: Migrating your custom trained models from cloud to on-prem, where possible.
• Infrastructure Planning: Right-sizing on-prem systems to match previous cloud requirements.
• IT Skills & Maintenance: Training staff or outsourcing AI infrastructure management.
• Gradual Hybrid Deployment: Running workloads in both cloud and on-prem environments during the transition phase, to maintain consistency and iron out bugs.
• Disaster Recovery Planning: Ensuring redundancy and backup strategies when moving away from cloud-managed disaster recovery systems.

Real World Solutions: Who Benefits from On-Premises AI?

• Banking Institutions are considering moves from US Big Tech’s cloud AI to part or full in-house models to ensure data sovereignty.
• Government & Defence Agencies should look to migrate AI workloads for national security and data sovereignty.
• Healthcare Organisations would benefit from moving their AI models on-prem to process patient data securely without exposing sensitive information to external networks.
• Retail and Manufacturing companies can use on-prem AI for real-time supply chain and quality control applications.

Conclusion

On-premises AI is a compelling alternative to cloud AI for organisations prioritising data control, compliance, and long-term cost savings. While cloud AI remains beneficial for rapid scaling and experimentation, businesses running steady AI workloads may find owning their infrastructure more secure and cost-efficient.

For IT managers considering a move to on-prem AI, the key takeaways are:

• Assess workloads and regulatory needs before migrating.
• Compare long-term cloud vs. on-prem costs to find the break-even point.
• Consider hybrid AI models to balance flexibility and control.
• Evaluate total cost beyond just initial expenses, factoring in operational stability.

Ultimately, on-premises AI empowers organisations to own their AI strategy, ensuring security, compliance, and cost predictability in a rapidly evolving digital landscape. Businesses that make the transition successfully can expect greater autonomy, stronger regulatory alignment, and long-term financial benefits.

The world of AI can be a fast moving and confusing place to be. If you are looking to explore the opportunities AI has to offer your organisation, speak to Trustco for some no obligation advice and an introduction to what we do.