Cloud or Local Device? Why Are AI Applications Using Both?

For years, Artificial Intelligence applications have relied primarily on the cloud. Data is sent to a remote server, the model processes it, and the response is returned to the application or the user.

This model provides high processing power and access to advanced models, but it is not always the most suitable solution. Some processes involve private code, internal documents, or sensitive data that an organization may not want to send outside its infrastructure.

In other cases, continuous cloud use may increase costs, slow down response times, or make the application dependent on an internet connection.

For this reason, another approach is emerging: hybrid AI architecture. In this model, one part of the process takes place in the cloud, while the other part runs locally on a computer, phone, private server, or another device.

The goal is not to choose between the cloud and the local device. The goal is for each task to be performed where it makes the most sense.

The Cloud and the Local Model Have Different Roles

A cloud model may have broader reasoning capabilities, a larger context window, and access to powerful processing resources. It can analyze the problem, create the plan, and define the steps that the system should follow.

A local model, on the other hand, can perform tasks directly connected to private data or the user’s environment. It can analyze files, review code, prepare changes, and use the device’s resources without sending the complete materials to the cloud.

Google has incorporated this division into the Antigravity SDK, enabling local models to be used for offline processes. In the architecture presented, the cloud model can serve as the planner, while local models perform data-intensive tasks, such as reviewing and updating code directly on the device.

This creates a system in which the most powerful model does not need to perform every action, while the local model does not need to understand the entire process on its own.

Planning Can Take Place in the Cloud While the Work Happens on the Device

Consider a company that wants to review a software project for security issues.

A cloud model can receive a general description of the project and prepare a review plan. It can determine which parts should be analyzed, what types of problems should be identified, and how the results should be evaluated.

The complete code does not need to be sent to the cloud.

The local model can use the plan to analyze the files within the company’s device or infrastructure. It can identify problematic areas, propose updates, and run tests.

At the end, only a controlled summary of the result can be sent to the cloud, without exposing the entire private codebase.

This division of the process seeks to combine the reasoning capabilities of cloud models with the control and privacy of local execution.

Privacy Becomes Part of the Architecture

In a fully cloud-based system, data must leave the device in order to be processed. Even when communication is protected, the organization must decide which data can be sent and which must remain within its environment.

Hybrid AI architecture addresses this issue from the system-design stage.

Internal documents, source code, personal data, or confidential information can be processed locally. Only instructions, anonymized data, or results approved for sharing may be sent to the cloud.

This does not mean that local execution is automatically secure. Local models must be managed, updated, and monitored. Access to files and the use of tools must be restricted according to the application’s role.

However, local processing gives organizations greater control over the location and movement of their data.

Speed No Longer Always Depends on the Internet

When every request is sent to the cloud, response time is affected by the internet connection, the distance from the server, and the service load.

A local model can provide faster responses for limited tasks because the data does not need to travel to an external server and return again.

This is useful for processes such as:

• Analyzing documents on the device
• Reviewing code during development
• Processing short commands
• Classifying data
• Detecting anomalies
• Operating with a weak internet connection
• Automating internal processes

The cloud can be used only when the task requires more advanced reasoning, broader context, or resources that the local device does not have.

Hybrid Architecture Can Also Affect Costs

The use of cloud models is generally connected to the number of requests, the amount of data processed, and the complexity of the model.

If every file, document, or code fragment is sent to a large model, costs can increase quickly.

Local models can take over repetitive tasks or processes that require large amounts of data. The cloud can be reserved for decisions or problems that require the capabilities of a more powerful model.

However, local execution is not free of costs. The organization must provide the devices, memory capacity, maintenance, and model updates.

Therefore, the decision should not be based only on the price of a cloud request. The total cost of the system must be calculated, including infrastructure, energy, administration, and security.

Not Every Task Should Be Sent to the Largest Model

In a traditional AI application, the same model may be used for every request. However, not all tasks have the same level of difficulty.

Classifying a document, searching through several files, and creating a short summary can be performed by a local model. Planning a complex system or analyzing a problem with multiple constraints may require a cloud model.

Hybrid AI architecture turns model selection into a software-architecture decision.

The system must determine:

• Which model should receive the task
• Which data can be sent to the cloud
• Which data must remain local
• When a more powerful model is required
• How the result will be verified
• What happens if one of the models fails
• Where the process history is stored

These decisions directly affect the application’s privacy, performance, cost, and reliability.

The System Must Know Where Each Action Is Executed

When a process is divided between the cloud and a local device, traceability becomes more important.

The organization must be able to understand which model made the decision, where the data was processed, which tools were used, and what result was produced.

If the cloud model creates the plan and the local model changes the code, the system must record both parts of the process.

This helps with error analysis, incident investigation, and verification of compliance with the organization’s policies.

Without this history, it may be difficult to determine whether a problem came from the planning stage, local execution, or communication between the two models.

Local Models Do Not Automatically Replace the Cloud

Local models provide advantages, but they also have limitations. The device may have less memory, processing power, and capacity for long context.

A local model may be highly efficient for a narrow task but less suitable for a problem requiring broad knowledge or deep analysis.

At the same time, a cloud model may offer greater capabilities but requires connectivity and creates more dependence on external infrastructure.

Therefore, hybrid AI architecture does not present local models as a complete replacement for the cloud. It uses both according to the role that each can perform best.

Developers Must Also Design the Data Path

In AI applications, it is no longer enough to define only the function’s logic. Developers must also design how data moves between the models, the device, and the cloud.

They must determine what information can leave the device, how it will be filtered, and whether certain elements should be anonymized.

Fallback mechanisms must also be established. If the local model cannot complete the task, the system must know whether it can send the task to the cloud, stop the process, or request human approval.

This makes the application architecture more complex, but also more controllable.

From Choosing One Model to Designing a System of Models

For Soft&Solution Group, hybrid AI architecture demonstrates that the future of intelligent applications will not be determined solely by selecting the most powerful model.

Attention is shifting toward how different models collaborate, how tasks are divided, and where data is processed.

As Ermal Beqiri, founder of Soft&Solution Group, explains:

“An AI application should not automatically send every task to the cloud. The architecture must determine what requires the power of a large model and what can be performed locally, faster, and with greater control over the data. The value lies in assigning responsibilities correctly.”

Hybrid AI architecture is creating a model in which the cloud and the local device do not compete but complement one another.

The cloud can provide planning, advanced reasoning, and access to large-scale capabilities. Local models can provide privacy, speed, and more direct control over data.

The challenge for software teams is to define the boundaries between them.

As AI becomes more deeply integrated into real applications and processes, it will become increasingly important to determine which model should perform a task, where it should run, and which data it should be authorized to use.