Cloud AI vs. on-premises AI: Where should my organization run workloads?

Artificial intelligence (AI) has become an indispensable part of modern business operations. But for leaders, one of their most crucial decisions is determining where to deploy these workloads: on-premises, in the cloud, or in a hybrid environment. 

Each approach offers unique benefits and challenges, and the right choice depends on your organization’s goals, resources, and constraints. In this article, I share actionable frameworks, real-world case studies, and clear decision-making tools to help you navigate this critical decision.

AI workloads are not one-size-fits-all. They vary widely in computational demands, latency requirements, and data sensitivity. Before deciding on a deployment strategy, it’s essential to categorize your workloads. There are four types:

1. Training workloads: These involve building AI models by processing vast datasets. Training often requires intensive compute resources (e.g. GPUs or TPUs) and benefits from scalable environments like the cloud.
2. Inference workloads: These apply trained models to real-time data. For instance, AI chatbots or fraud detection systems often require low-latency environments, making on-premises or hybrid solutions attractive.
3. Data preprocessing: Cleaning and organizing data before training or inference is critical. Sensitive data preprocessing may need to remain on-premises for compliance, while non-sensitive preprocessing can leverage the cloud.
4. Edge AI: AI models deployed on IoT devices or edge servers require minimal latency and continuous uptime. This workload often necessitates hybrid solutions to combine local processing with cloud-based updates.

Both on-premises and cloud deployments have distinct advantages and trade-offs. Understand these pros and cons to align your infrastructure decision with your business priorities.

• Data control: Organizations have full control over sensitive data, ensuring compliance with regulations such as HIPAA or GDPR
• Customization: You can tailor infrastructure to specific AI workloads for maximum efficiency and performance
• Predictable costs: Upfront capital investment (CAPEX) eliminates long-term variable costs associated with cloud usage

• High upfront costs: On-premises infrastructure requires significant initial investments in hardware, facilities, and skilled personnel
• Scalability limitations: Adding capacity is slow and expensive, making it difficult to handle sudden workload spikes
• Maintenance: Organizations must manage upgrades, security patches, and repairs

Case study: Bank of America opted for an on-premises solution for their AI workloads to ensure compliance with stringent financial regulations. While this guaranteed data security, the bank faced challenges scaling its operations as AI demands grew. To address this, they began integrating hybrid strategies for less sensitive tasks like customer behavior analysis.

Running AI in the cloud comes with its own share of benefits and challenges.

• Scalability: The cloud offers near-infinite scalability, making it ideal for computationally intensive workloads like model training
• Cost efficiency: Pay-as-you-go pricing aligns cost with usage, making it suitable for startups and organizations with variable workloads
• Managed services: Providers like AWS, Google Cloud, and Azure handle maintenance, updates, and infrastructure management

• Data security and compliance risks: Transferring data to third-party providers can create vulnerabilities and compliance challenges
• Vendor lock-in: Organizations risk becoming dependent on a single provider, limiting flexibility and negotiating power
• Latency issues: Real-time workloads may suffer from higher latencies when deployed in the cloud

Case study: Spotify migrated its AI workloads to Google Cloud to enhance scalability and accelerate feature development. The cloud allowed Spotify to handle billions of user interactions daily. As costs began to rise with scaling, Spotify had to invest more in optimizing its workloads and leveraging multi-cloud strategies.

Hybrid deployment combines on-premises infrastructure with cloud resources, offering a middle ground for organizations that need both control and flexibility. Sensitive data can be processed on-premises, while computationally intensive tasks like training are handled in the cloud.

• Data sovereignty: Organizations can meet compliance requirements by keeping sensitive data on-premises
• Scalability: Hybrid deployments provide the flexibility to leverage cloud resources when needed
• Cost optimization: Organizations can distribute workloads strategically, reducing overall costs

Case study: Volkswagen adopted a hybrid strategy to accelerate its autonomous vehicle development. The company used on-premises infrastructure for sensitive data processing while leveraging the cloud for large-scale simulations. This approach enabled rapid innovation while maintaining compliance with data protection regulations.

Not sure where to start? This decision-making framework can help you choose where to deploy your AI workloads.

AI workloads vary significantly in terms of computational intensity, latency requirements, and predictability. Training-heavy workloads that require massive computational power over short periods, such as building large language models, benefit from the cloud’s elasticity. 

Conversely, latency-sensitive workloads, like real-time inference in high-frequency trading, are better suited for on-premises environments. Hybrid setups cater to mixed needs, like processing low-latency edge AI locally while leveraging the cloud for burst computational tasks.

Whichever you choose, align your deployment choices with long-term organizational priorities, such as innovation, risk management, and global expansion.

Example: A retail company processes demand forecasting and recommendation systems in the cloud, using its scalability for periodic model training. However, for low-latency in-store inventory management, they rely on on-premises infrastructure.

Data sensitivity and regulatory compliance often dictate deployment choices. Industries with strict regulations, such as healthcare (with HIPAA) or finance (with GDPR), benefit from on-premises setups to ensure data sovereignty. 

Less sensitive data workloads can run in cloud environments that offer certifications like ISO 27001 for secure data handling. Meanwhile a hybrid strategy balances compliance needs by processing sensitive data locally while training models in the cloud.

Example: A financial services provider handles sensitive transaction data on-premises to meet data sovereignty laws but uses AWS for large-scale fraud detection model training, combining security with computational power.

When budgeting for AI, consider capital expenditure (CAPEX) versus operational expenditure (OPEX). On-premises deployment involves high upfront costs (CAPEX) but predictable long-term expenses, making it suitable for stable workloads. 

Cloud solutions reduce entry costs with pay-as-you-go pricing but can become expensive at scale due to data transfer and usage fees (OPEX). 

Hybrid models, on the other hand, can optimize costs by distributing workloads strategically between both environments.

Example: Dropbox initially relied on cloud storage but found long-term costs unsustainable. By shifting core workloads to on-premises infrastructure, the company saved $75 million over two years while keeping some cloud flexibility for non-critical operations.

How much scalability you need depends on your workload’s stability and growth.

Scalability needs depend on workload stability and growth expectations. Cloud environments provide unmatched scalability, ideal for organizations with fluctuating demand or seasonal workloads, such as e-commerce platforms experiencing holiday spikes. 

On-premises solutions work well for stable, predictable workloads with their cost control and tailored performance. Hybrid setups combine the cloud’s elasticity with on-premises stability to handle computational bursts.

Example: Netflix uses the cloud to manage its recommendation engine during peak viewing hours. This ensures seamless performance during demand spikes while maintaining cost efficiency through dynamic scaling.

The complexity of managing AI infrastructure depends on your organization’s IT capabilities. On-premises setups require skilled teams to maintain, secure, and optimize hardware. 

Organizations without specialized expertise benefit from cloud providers’ managed services. Hybrid models balance in-house IT management with the convenience of vendor-supported services for specific tasks.

Example: A mid-sized manufacturing firm with limited IT resources adopted Google Cloud’s managed AI services. This enabled them to deploy predictive maintenance models and focus on operational improvements instead of infrastructure management.

Ensuring uninterrupted AI operations in the face of failures or disasters is crucial. In on-premises setups, this requires investing in redundant infrastructure and off-site backups, which can be complex and costly.

Cloud solutions, on the other hand, simplify disaster recovery with built-in high availability and failover services. Hybrid models split workloads, keeping critical operations local while leveraging cloud redundancy.

Example: A global retail company kept critical operational data on-premises while relying on Microsoft Azure for disaster recovery. When a regional data center outage occurred, cloud redundancy ensured uninterrupted services.

Want a quick framework to help you decide where to run your AI workloads? This structured decision tree can guide you through the process of selecting the optimal deployment strategy for your organization:

1. What are your workload requirements?
   • Training-heavy or dynamic workloads → Cloud
   • Latency-sensitive workloads → On-premises or hybrid
2. How sensitive is your data?
   • High sensitivity (e.g. regulated industries) → On-premises or hybrid
   • Moderate to low sensitivity → Cloud
3. What is your budget model?
   • CAPEX-focused → On-premises
   • OPEX-focused → Cloud or hybrid
4. What scalability do you need?
   • Predictable workloads → On-premises
   • Fluctuating or unpredictable workloads → Cloud
5. Do you have in-house expertise?
   • Yes → On-premises or hybrid
   • No → Cloud

The choice between on-premises, cloud, or hybrid deployment is not a one-size-fits-all decision. It requires a nuanced understanding of workload requirements, cost structures, scalability needs, and data sensitivity. By leveraging the decision framework and learning from real-world examples, you can make informed decisions that align with your organization's goals.

Whether you’re a global enterprise or a growing startup, the right infrastructure strategy can empower you to unlock the full potential of AI.

Help your teams build the skills they need to deploy AI.