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Cloud AI has AI models hosted on remote cloud platforms, accessed via internet, offering scalability and low upfront cost. On the other hand, On-Prem AI involves AI deployed on local infrastructure within an organization, providing greater control, security, and customization.
Artificial intelligence is the core driver for businesses in digital transformation. As this phenomenon is becoming prevalent making right infrastructure decisions whether to choose between on-premises, cloud or mixed is becoming crucial. The decision has significant impact on cost, scalability, compliance and off-course the security.
Whether to choose between cloud AI or On-Prem AI is an individual business choice driven by organization needs, long term goals, financials and nature of workloads. While adopting AI infrastructure organizations need to decide on deployment approach and evaluate specific business needs they have with specific deployment strategies they will adopt.
In today’s article we will understand the difference between the AI infrastructure landscape, choosing between Cloud AI or On-premises AI, their key differences, challenges and use cases.
What is Cloud AI
The cloud based AI model leverages existing cloud providers (AWS, AZURE, GCP etc.) already available distributed computing resources. GPU based clusters, AI specialized accelerators which support unlimited scalability and on demand provisioning for managed AI services help organizations to get away with huge capex budgets to incur in setting up on premises AI infrastructure. Enterprises can perform AI experiments without much investments and with minimum upfront cost. Cloud AI can be deployed as shared, public and dedicated.
Use Cases for Cloud AI
- Start-ups and scale-ups companies having limited financial and technical resources. Need speed and agility in business to focus on product development over management of underlying infrastructure.
- Large scale model trainings for deep learning etc which require huge GPU resources
- Intermittent or seasonal workloads which require flexibility of pay-as-you-go
What is On-Prem AI
On premises AI infrastructure requires dedicated hardware and other tools to setup inside the data center. This includes high performance GPU servers, AI specific accelerators and other associated infrastructure such as power, cooling, cabling, networking etc similar to traditional data centres but of much higher capacity to handle AI workloads. Having an on premises AI infrastructure means full control of all resources and ideal for organizations subjected to regulatory requirements such as finance, healthcare, national security etc.
Use Cases for On-Prem AI
- Meant for government or highly regulated institutions which require data sovereignty and compliance to regulations such as FedRAMP, ITAR etc.
- Healthcare and finance sector organizations which need to have specific privacy requirements such as HIPAA, PCI-DSS.
Hybrid Approach
Hybrid approach is a mix of both cloud AI and On premises AI to enable organizations to optimize different types of workloads across hybrid environments. In a typical hybrid setup models are trained on cloud and interfaces are deployed on premises.
Comparison: Cloud AI vs On-Prem AI
| Features | Cloud AI | On-Prem AI |
|---|---|---|
| Costs | Cloud AI works on Pay-as-you-go model and having low upfront costs | This is high upfront cost with low variable cost model |
| Scalability | Cloud AI setups provide scalability on demand with instant provisioning capabilities | On prem AI setups scalability is decided basis of hardware deployed. Also scaling involves physical installations, setting up new hardware which is a time-consuming process |
| Data security | In cloud AI organizations need to rely on cloud provider security measures | In On premises AI setups, the organizations have complete control over their data and its security |
| Speed of setup | Instant provisioning – few minutes to few days | This is slow depends on efforts required for in-house team to deploy new severs |
| Maintenance | Maintenance is cloud provider scope | Maintenance is organization in-house team scope |
| Infrastructure | Cloud provider infrastructure is shared which could lead to performance variances and not ideal for latency sensitive business applications or uses | AI hardware evolves rapidly which means existing hardware will become obsolete or not able to handle complex AI workloads |
| Costs of training AI models | Training large AI models on cloud could be expensive as there are hidden costs such as data egress, Inter-region transfers, idle compute etc. | Training large AI models in On premises setup could be highly expensive as this would need additional capacity which means more investment in capex – Hardware, software etc. |
Download the comparison table: cloud AI vs on-prem AI