The deployment decision
When implementing AI automation, businesses face a deployment choice: cloud-based AI services or on-premise AI infrastructure. The decision affects data control, cost structure, capability access and operational requirements.
Neither approach is universally better. The right choice depends on the business's specific requirements, constraints and priorities.
Cloud AI: capability without infrastructure
Cloud AI services provide access to the latest AI models without requiring the business to manage infrastructure. The provider handles the hardware, the model updates and the operational complexity. The business uses the AI through an API.
Cloud AI advantages include:
- Access to the best models. Cloud providers offer the most capable models, which are continuously updated.
- No infrastructure management. The provider handles servers, scaling, updates and availability.
- Pay-per-use pricing. Cost scales with usage, which can be economical for variable or low-volume use.
- Rapid deployment. Cloud AI can be integrated into applications quickly without infrastructure setup.
Cloud AI considerations include:
- Data leaves your infrastructure. Data sent to cloud AI services is processed on the provider's servers, which may not meet all data residency or security requirements.
- Cost at high volume. Pay-per-use pricing can become expensive for high-volume, continuous use.
- Provider dependency. The business depends on the provider's continued operation, pricing and terms.
On-premise AI: control and independence
On-premise AI involves running AI models on the business's own infrastructure. This can mean dedicated servers, private cloud or edge devices, depending on the requirements.
On-premise AI advantages include:
- Data control. Data stays within the business's infrastructure, which addresses security, compliance and data residency requirements.
- Predictable cost. Once the infrastructure is in place, the marginal cost of additional usage is low.
- Independence. The business is not dependent on a third-party provider's continued operation, pricing or terms.
On-premise AI considerations include:
- Infrastructure investment. On-premise AI requires hardware, software and the expertise to manage both.
- Model capability gap. On-premise models may be less capable than the latest cloud models, particularly for the most demanding tasks.
- Operational burden. The business is responsible for updates, security, scaling and availability.
The hybrid reality
Many businesses end up with a hybrid approach: cloud AI for the capabilities that benefit from the latest models and on-premise AI for the data that must remain within their infrastructure. A customer-facing assistant might use cloud AI for its superior language capability. An internal document processing system handling sensitive data might use on-premise AI.
The hybrid approach provides the benefits of both models at the cost of managing two deployment environments.
For a related comparison focused on the service model rather than infrastructure, see hosted vs self-hosted AI. For a comparison of model providers, see OpenAI vs Anthropic for business.
Moonshot Monkeys helps businesses evaluate and implement the right AI deployment approach for their specific requirements. If you are uncertain whether cloud, on-premise or hybrid AI is right for your needs, we can help you make an informed decision.