Choosing the Right AI Deployment Model for Your Business
In Part One, we explored the different AI deployment options, cloud, on-premise, and edge computing, along with their advantages and trade-offs. Now, in Part Two, we dive into the key considerations for making an informed decision that balances performance, cost, and compliance.
Key Considerations for AI Deployment
Cost vs. Long-Term Investment
Cloud AI operates on a subscription or pay-as-you-go model, which can be cost-effective for short-term projects or businesses with fluctuating AI workloads.
On-premise AI requires a higher initial investment but may reduce costs in the long run for businesses with steady AI needs.
Using an AI service provider can be a significant variable cost for an SME and it will be difficult to create USPs for your business or generate good EBITDA metrics.
Key Takeaway:
If cost predictability is a priority, on-premise AI may be more economical over time. For businesses needing flexibility, cloud AI is the better choice.
Performance and Scalability
Cloud AI offers near-instant scalability, making it ideal for businesses with unpredictable workloads.
On-premise AI provides dedicated resources, ensuring consistent performance for mission-critical applications. May require significant investment in infrastructure and IT expertise.
Edge AI, meanwhile, minimises latency for real-time decision-making. May requires access, creation and maintenance of training sets.
Key Takeaway:
Cloud AI is best for scalability, on-premise AI ensures dedicated performance, and edge AI is crucial for real-time processing needs.
Compliance and Data Security
Businesses operating in regulated industries (e.g., healthcare, finance, defence) must ensure compliance with data protection laws like GDPR or HIPAA. On-premise AI provides complete control over data security, while cloud providers offer compliance-ready environments with encryption and access controls.
Key Takeaway:
Businesses with strict compliance requirements should consider on-premise AI, while cloud AI is a viable option if security concerns are addressed.
IT Expertise and Maintenance
Cloud AI reduces the need for in-house IT teams by outsourcing maintenance and updates to service providers. On-premise AI requires dedicated IT staff to manage infrastructure, security patches, and system updates.
AI processing that is available within the cost constraints of IoT devices will be severely restricted. The business case for adding the complexity of AI must be judged against the greater utility than can be offered when compared with simpler solutions, such as Support Vector Machines.
Key Takeaway:
If IT resources are limited, cloud AI simplifies deployment and management. For companies with IT expertise, on-premise AI offers greater control.
Hybrid AI: A Balanced Approach
Many businesses are adopting a hybrid AI model, using cloud services for flexible workloads while keeping sensitive data and mission-critical applications on-premise. This approach balances cost, security, and scalability.
When to Consider a Hybrid AI Model:
Sensitive data stays on-premise, while cloud AI handles less critical workloads.
AI training is done on-premise, while inference and processing are outsourced to the cloud.
A combination of on-premise and edge AI ensures real-time processing while leveraging cloud resources when needed.
Final Thoughts
Choosing the right AI deployment strategy depends on business goals, operational constraints, and regulatory requirements.
Cloud AI is best for businesses needing rapid deployment, scalability, and minimal maintenance.
On-Premise AI is ideal for organisations that require full control over security and compliance.
Edge AI works well for real-time processing applications.
Hybrid AI offers the flexibility to combine different approaches for optimal performance and cost-efficiency.
What’s Next?
If you're evaluating AI deployment options and need guidance, get in touch with our team. We can help you navigate the decision-making process and find the best AI strategy for your business.
