Which strategy would best optimize the deployment of an AI-based video analytics system under budget constraints?

Implementing a hybrid cloud solution that combines local servers with cloud resources is an effective strategy for optimizing the deployment of an AI-based video analytics system while adhering to budget constraints. This approach allows organizations to leverage the strengths of both on-premises hardware and cloud computing.

By utilizing local servers, organizations can minimize latency for real-time processing of video data since this data can be processed close to where it is collected. This is particularly important for applications that require immediate data analysis and response, such as security monitoring or traffic management. The local servers can handle lower-volume data or tasks that require quick results.

On the other hand, cloud resources can be used effectively for heavier workloads, such as batch processing, storage, and training of AI models, which often require more computational power and can be scaled easily. The hybrid model allows the organization to allocate resources efficiently based on demand, thereby avoiding overspending on excess hardware or cloud services that may not be used continuously.

This strategy not only optimizes cost by balancing the use of local and cloud resources but also ensures flexibility and scalability as the needs of the AI-based video analytics system evolve.