Cloud-based artificial intelligence vs traditional AI
Artificial Intelligence (AI) has become an essential tool for businesses that want to stay competitive. AI can help companies automate tasks, make predictions, and gain insights that they might have missed otherwise. With the advantages of AI, we also need to consider the methods of deploying it. Traditionally, AI has been deployed locally, or on-premises. But with the recent advancements of cloud computing, AI can now be deployed in the cloud.
So, what are the differences between cloud-based AI and traditional AI? In this article, we will provide a factual comparison that will help you determine which method is best for your business.
Traditional AI
Traditional AI is deployed locally, which means that the AI software is installed and runs on hardware in the company's facility. The hardware infrastructure includes servers, storage, and networking components. The company is responsible for maintaining and upgrading the hardware and software components.
Pros and Cons
Pros
- Full Control: With traditional AI, companies have full control of their hardware and software, which provides more customization options and flexibility in terms of data privacy, security, and compliance.
- Low Latency: Since the AI infrastructure is on-premises, it provides low latency and high performance.
- No Internet Dependence: It doesn't rely on internet connectivity or cloud providers availability.
Cons
- High Infrastructure Cost: Companies have to incur capital and operational expenses like purchasing hardware, software licenses, maintenance, upgrades, and energy costs.
- Scalability and Sizing: Upgrading or scaling the traditional AI infrastructure is a complex and expensive process. Getting the right size to support AI workloads and maintaining it is a daunting task.
- Limited Capacity: Traditional AI infrastructure has limited carrying capacity to support large-scale applications and machine learning datasets.
Cloud-based AI
Cloud-based AI is deployed in a cloud environment, provided and managed by the cloud service provider. The AI software runs on virtual machines (VMs) with flexible and scalable computing resources, storage, and networking options.
Pros and Cons
Pros
- Less Infrastructure Cost: Cloud-based AI eliminates the cost of infrastructure investments, upgrades, and maintenance.
- Elasticity and Scalability: Cloud-based AI allows companies to scale resources up or down based on usage and real-time demands with just a few clicks.
- Easy Implementation: Setting up a cloud environment is quick, and it doesn't require technical expertise.
- Machine Learning as a Service: Cloud providers offer machine learning services, making the development and deployment of production-grade models easier.
Cons
- Latency: Internet speed and cloud provider availability can cause latency issues, which impact the AI application's performance.
- Data Security and Privacy: Companies must ensure that their data is secure and complies with regulatory requirements. Although cloud providers have robust security mechanisms in place, there is always the risk of data breach or theft.
- Limited Customizations: Cloud providers offer a limited set of hardware and software options, which may not suffice some customers' requirements for hardware customizations.
So, which AI deployment option should you choose?
Both traditional AI and cloud-based AI have their pros and cons. Traditional AI provides more control and flexibility, while cloud-based AI offers more scalability and less maintenance. The decision depends on the company's budget, specific use case, and how they prioritize their resources.
For small to medium-sized businesses with limited budgets, cloud-based AI can be the best choice. It provides instant access to computing resources, eliminates the need for infrastructure investment, and reduces implementation time. However, larger organizations with complex infrastructure requirements might prefer the traditional AI deployment model.
In conclusion, companies should choose the AI deployment method that's best for their use case, and there's no one-size-fits-all solution. Companies should consider the application's sensitivity, cost, scalability, customizations, and regulatory requirements to make an informed decision.