In an era of rapid digitalization and business automation, companies are increasingly adopting machine learning to optimize processes and improve efficiency. These projects often require significant computational resources, and one of the most popular solutions is cloud infrastructure, which can handle large volumes of data and support high-load calculations.

A typical example is an ML-based service for automatic order forecasting. However, a strategic question arises early on: where should such a service be deployed - in the cloud, on local servers, or in a hybrid environment?

Some clients point directly to the vulnerabilities of the cloud model: unstable internet connections can lead to service disruptions, and cloud providers themselves may become targets of cyberattacks. In the event of a breach, both data confidentiality and business continuity are at risk. As a result, organizations are increasingly exploring alternatives from full on-premises deployment to hybrid configurations, especially for mission-critical services.

Cloud providers, in turn, are actively developing protection mechanisms: backup systems, geographically distributed data centers, multi-level access control, activity monitoring, and automated incident response. However, even these measures cannot fully eliminate risks related to connectivity issues or external threats.

There is no universal answer, the right choice depends on your specific tasks, acceptable risk level, and availability requirements. To make an informed decision, it’s helpful to consider three key deployment scenarios: local, cloud-based, and hybrid. In this article, we’ll analyze each of them using the example of an ML-powered auto-ordering service, highlighting their strengths and limitations.

In this scenario, the entire ML service - model, API, user interface, and database - operates in the cloud. Clients access the functionality via a secure connection, enabling use from anywhere in the world.

Advantages:

• Minimal infrastructure costs: everything is hosted by the provider, reducing hardware and maintenance expenses.
• Flexible scalability: resources can be easily adjusted based on business growth or demand fluctuations.
• Automatic updates: both the model and interface are updated without client involvement.
• High availability and fault tolerance: cloud platforms ensure continuous service even during node failures.
• SaaS model support: subscription-based pricing without major capital investments.
• Broad integration options: compatibility with the provider’s ecosystem (e.g., Microsoft) and other cloud services.

Features and Limitations:

• Requires stable internet connection: network disruptions can affect performance or cause downtime.
• Data stored in the cloud: security and confidentiality requirements must be carefully considered, especially for sensitive information.

Advantages:

• Fast implementation: minimal effort required to launch, thanks to cloud-hosted heavy components.
• Resilience to outages: the local module continues operating even during temporary internet loss.
• Model updates without infrastructure load: the cloud component is updated centrally.
• Remote administration: reduces the need for local technical support.
• Daily backups: data is stored in the cloud and available for analytics.

Technical Features:

• Forecasts delivered in advance: results are calculated ahead of time, before actual use.
• Local storage: forecasts are saved on the client side and used offline.
• Buffer forecasting: the model generates predictions for an extended horizon to reduce risks during connectivity loss.
• Secure connection: data is transmitted via SSL protocol.

Example Use Case

Each morning, when internet access is available, the local module downloads fresh forecasts from the cloud. The ML service calculates results in advance and transfers them to the local system, ensuring stable operation even if connectivity issues arise later.

This option suits companies that prioritize full control over infrastructure and data. The entire system - ML model, API, user interface, and database - is deployed on the organization’s own servers. This approach offers maximum autonomy and independence from external providers.

Advantages:

• Complete control: all infrastructure and data remain within your organization, with no third-party involvement.
• Internet independence: the service continues functioning even without any network connection.

Features and Limitations:

• Infrastructure costs: resources are needed for setup, maintenance, and ongoing monitoring.
• Limited scalability: expanding resources or updating the model can be more complex and costly than in the cloud.
• High performance requirements: sufficient computing power must be available on-site.
• Access configuration: secure channels must be set up for developers and administrators.
• Significant upfront investment: hardware, licenses, and software must be purchased and configured.

For many companies, the optimal first step is launching a hybrid or fully cloud-based pilot project. This allows you to test forecast accuracy and auto-ordering performance in real conditions without major upfront costs. Once results are validated, you can develop a final technical specification and choose the most suitable deployment scenario — whether cloud, on-premises, or a combination of both.

Selecting the right infrastructure for an ML service is a balance between convenience, deployment speed, reliability, and security. A full cloud deployment offers maximum flexibility and ease of scalability, a hybrid model combines autonomy with cloud capabilities, and an on‑premises solution provides full control and independence.

In an environment of increasing digital dependence and external risks, there is no one‑size‑fits‑all scenario: the optimal path depends on the specifics of the business, the acceptable level of risk, and the resources available. This is why it is advisable to start with a pilot project in the cloud or in a hybrid format, to test the system in a real‑world setting and evaluate its effectiveness without major investments.

A well‑considered choice of architecture will not only reduce risks and optimize costs but also create a solid foundation for the further development of ML solutions that can scale alongside your business.