Artificial intelligence has a unique selling point. It truly shines where traditional software falls short: with unstructured data, inconsistent formats, and complex patterns. Especially in technical procurement, where 2D drawings, 3D models, and evolving bill of materials come together, AI makes all the difference. But this capability raises a fundamental question: Where does this AI run? In the cloud or on-premises? What sounds like an IT question has long since become a strategic decision - especially for the manufacturing industry.

What do “On-premises“ and “Cloud“ mean?

On-premises refers to IT solutions in which companies operate servers, databases, and applications on their own premises. The hardware is located in the company’s own data center, and the company is responsible for its operation, maintenance, and security. This means full control, but also high initial investments and significant maintenance costs.

Cloud computing, on the other hand, refers to the provision of IT resources via the Internet. Servers, storage, and applications are operated by external providers and scaled as needed. Companies pay based on usage and do not have to worry about the infrastructure.

Cloud vs. on-premises: Pros and cons in the context of AI

• On-premises – the supposed model of control

On-premises solutions promise full data sovereignty on your own hardware, no dependency on external providers, and one-time licensing costs instead of recurring fees. However, the limitations of this model become particularly apparent in the context of AI: AI workloads require specialized GPU infrastructure, which demands high initial investments in hardware and personnel. Furthermore, patch management, monitoring, and security processes often fail in practice due to a lack of resources. Adding new resources is time-consuming - a real problem when data volumes grow rapidly.

• Cloud – the enabler for AI workloads

The cloud, on the other hand, enables a quick start: Instead of months-long infrastructure projects, a solution can be up and running in just a few weeks. Automatic scaling is particularly relevant for AI applications. Companies benefit from lower fixed costs, as server procurement and operational and licensing complexities are eliminated. Additionally, the cloud allows for faster hardening and consistent security standards. An often underestimated advantage is the ease of rollout. New locations and teams can be connected without requiring an additional server infrastructure. IT can focus on governance and integration, while patches, security updates, and availability are managed by the provider. The result: faster value creation. ROI is realized sooner because data becomes usable more quickly. The trade-off is ongoing costs rather than a one-time investment, and the need to prioritize data protection when selecting a provider.

The two most common misconceptions – and why they don’t hold up

• Myth 1: “Cloud = publicly accessible”

In reality, modern cloud systems are logically isolated - with multi-tenant capabilities and access control mechanisms. With professional providers, customers decide exactly who sees what.

• Myth 2: “On-premises is always more secure”

The theory sounds logical. In practice, however, on-premises solutions often fall short when it comes to patch levels, consistent monitoring, and end-to-end security processes. Cloud solutions enable standardized, high-level security in this regard.

PartSpace is fully committed to the cloud - and for good reason. The PartSpace AI platform analyzes CAD files, technical drawings, and bill of materials to intelligently support procurement decisions. This requires an infrastructure that can scale with the industry’s data volumes. After all, companies ranging from SMEs to enterprise-level corporations face the same challenge: many parts, many suppliers, distributed locations, and increasing pressure on costs and time-to-quote. Michael Neuhauser, Chief Technical Officer (CTO) at PartSpace, says: “AI workloads are erratic: Today we analyze 5,000 drawings, tomorrow perhaps 250,000, and the day after tomorrow a few million. Cloud scaling ensures that import, analysis, and mass calculation remain high-performing - without the customer having to buy infrastructure on a hunch.”

What companies gain in concrete terms

For companies transitioning from on-premises systems or those that previously lacked a systematic solution for their technical data, cloud architecture offers concrete advantages: It provides transparency regarding drawing parts - from similarities and identical parts to cost and price outliers, all the way to supplier comparisons. At the same time, it bridges the gap between engineering and procurement: fewer media breaks, less manual classification, and less Excel work. And last but not least, it scales significantly better for CAD libraries that have grown over time. Another advantage is data fusion. CAD data, drawings, and procurement and supplier data converge on a single platform, without data silos.

Enterprise-level security

PartSpace combines cloud flexibility with strict security mechanisms. All data is encrypted both during transmission (TLS) and at rest. Access is granted via a role-based permissions model based on the principle of least privilege - optionally supplemented by SSO integration and multi-factor authentication. A strict logical separation ensures that customer data remains isolated across tenants. For full traceability, audit logs record all critical actions and enable auditable histories. The security concept is rounded out by continuous monitoring, alerting, regular backups, and a defined incident handling process.

Conclusion: The cloud is not a compromise, but a prerequisite

For AI-powered applications in technical procurement, the cloud is not just a “nice-to-have.” It is essential for efficiently processing millions of CAD data sets, identifying similarities, and calculating target costs in real time. Anyone who truly wants to leverage engineering data needs an infrastructure that keeps pace with the reality of data - not hardware designed for yesterday’s scenarios.