AI Deployment

Today, when integrating AI solutions into existing or new projects, it’s crucial to choose the right AI infrastructure deployment strategy, as this significantly impacts both the budget and technical performance.

Cloud Deployment:

• Cloud Services: Utilizing services like AWS, Google Cloud, and Microsoft Azure for deploying and managing LLMs. This allows for scaling resources based on needs and reduces infrastructure costs.
• API Services: Leveraging third-party APIs (e.g., OpenAI API) to integrate LLMs into your applications without worrying about infrastructure management.

On-Premise Deployment:

• Servers in the company: Installing and running LLMs on company’s own servers. This provides more control over data and privacy but requires more technical resources and expertise to manage the infrastructure.
• Virtual Machines and Containers: Deploying models in isolated environments such as Docker containers or on virtual machines, which makes it easy to move them between different environments.

Hybrid Deployment:

• Combining cloud and on-premise servers: This is used to keep critical data stored locally while leveraging scalable cloud computing resources. For example, data may be stored locally while large-scale data processing occurs in the cloud.

Edge Deployment:

• Deployment on end-user devices: Installing models directly on devices (such as mobile phones or IoT devices), enabling data processing on the device itself and reducing latency. This is ideal for applications that require fast response times.

Distributed Deployment:

• Distributing computations across multiple nodes: The model is distributed among multiple servers or devices, which allows processing large amounts of data in parallel and reduces latency.

Selecting the right deployment type will help you achieve the best results for your project.