As smart factories adopt digital twins (virtual replicas of physical assets/processes) to optimize operations, simulate what-if scenarios, and enable predictive maintenance, scale becomes a defining challenge. For many stakeholders, the choice between relying on public cloud / hybrid models vs building a private cloud AI infrastructure is no longer academic but central to long-term competitiveness.
The Stakes and Current Landscape
- Market growth: The global digital twin market is expected to grow from about USD 24.97 billion in 2024 to USD 155.84 billion by 2030, at a CAGR of ~34.2%. (Source: Grand View Research)
- Deployment preference: In 2024, the on-premise (private/internal) segment accounted for over 74% of deployments in digital twin solutions in manufacturing/smart factories. (Source: Grand View Research)
These data points suggest that not only is demand for digital twin large and growing, but many enterprises are already choosing or preferring private or on-premise infrastructures.
Why Private‐Cloud AI Infrastructure Is Being Considered—and in Many Cases Deployed
Data Sovereignty, Compliance, Latency & Reliability
When factories operate in regulated industries (e.g. pharmaceuticals, defense, energy), data must often remain within controlled borders. Public clouds have made strides, but compliance risk remains a concern. Additionally, digital twins that feed on real-time sensor streams for control loops or anomaly detection require extremely low latency and high availability—something public clouds may not always guarantee, especially in emergent/offshore facilities or where connectivity is intermittent.
Cost Predictability at Scale
While public cloud models offer low upfront capital expense, at sufficient scale (many factories, many sensors, continuous AI model training/inference, high volume of simulations), operating costs (bandwidth, data egress, compute time) can balloon unpredictably. A private cloud or internal AI infrastructure can allow for tighter cost control once investment amortises.
Customization, Integration & Edge / Hybrid Continuum
Factories often have heterogeneous legacy equipment, OT (operational technology) networks, proprietary protocols. Private cloud + edge infrastructure offers greater flexibility to integrate and customize data pipelines and models, and to localize processing (edge or on-factory floor) while aggregating to central private cloud layers.
Challenges of Building Private Cloud AI Infrastructures
But building private cloud AI is far from trivial. Key challenges include:
- Capital & Operational Expenditure: High up-front spend, ongoing costs for hardware refresh, cooling/power, skilled personnel for AI/ML, infrastructure, cybersecurity, etc.
- Scalability & Elasticity: Public clouds are built for scaling up/down on demand; private clouds must be designed with over-provisioning, redundancy, or else risk bottlenecks during peak loads (e.g. batch simulations, model retraining, large-scale what-if scenarios).
- Talent & Governance: Building and maintaining AI infrastructure requires specialists in DevOps, MLOps, data engineering. Governance of data, model drift, versioning, security, and regulatory compliance is crucial.
- Upgrades and Future Proofing: AI/ML frameworks, GPUs/accelerators, networking tech evolve fast. A private infrastructure must plan for continuing investment to avoid being obsolete.
When Private Is the Right Strategy
Private-cloud AI infrastructure makes sense under these conditions:
- High Volume, Mission Critical Digital Twins
- If the factory is operating many digital twins concurrently (e.g. per asset/fleet), running heavy simulations, or requiring real-time closed-loop control.
- Sensitive or Regulated Data
- Industries with strict privacy, trade secrets, or regulatory regimes (government contracts, exports, etc.).
- Frequent Edge Requirements
- When a large amount of data must be processed at the edge (factory floor) due to latency or bandwidth constraints, a closely integrated private cloud + edge architecture yields benefits.
- Long-Term Total Cost of Ownership Advantage
- Organizations with capital to invest and sufficient scale to amortize that investment across many sites and years, so that ongoing costs per unit (simulation run, model training) become cheaper than cloud alternatives.
Hybrid and Modular Alternatives
Often the optimal approach is hybrid:
- Edge devices process immediate sensor data and do inference close to machines; heavy model training and simulations run on private cloud, or in mixed mode using private + public clouds for bursts.
- Use containerized / microservices architecture so workloads can shift between private cloud and public cloud as demands vary, enabling elasticity without full dependence on third parties.
Also consider modular private infrastructure—starting small, proving ROI, then scaling out. This reduces risk compared to building large monolithic private clouds from scratch.
ROI & Metrics That Stakeholders Should Watch
To convince boards/investors, stakeholders should define and monitor:
- Cost per model training / per simulation run (cloud vs private).
- Latency being reduced (edge-to-digital twin predictions etc.).
- Operational interruption or downtime avoided (predictive maintenance, optimisations).
- Security incident risk, compliance cost savings.
One case: using Value Chain Digital Twin, companies improved forecast accuracy by 20-30% and saw 50-80% reductions in delays and downtime. (Source: BCG)
Strategic Recommendations for Stakeholders
- Start with Use Case Prioritization: Not all digital twin use cases require ultra high performance or low latency. Prioritize high-value ones first to build experience and validate private infrastructure.
- Adopt a Phased Build-Out Approach: Pilot with one facility or line to design architecture, then scale. Ensure architecture is modular, secure, and capable of integrating with future public cloud or edge computing.
- Invest in MLOps & Dataops: Infrastructure alone isn’t enough—processes for data ingestion, cleaning, feature versioning, model drift, monitoring are equally important.
- Partner with specialists: For hardware (GPUs, networking, accelerators), software stack (frameworks, virtualisation), security. Using proven frameworks or vendors reduces time and risk.
- Governance & Security First: Design for auditability, regulatory compliance, data lineage, encryption, and disaster-recovery. Private cloud infrastructure must meet or exceed thresholds set by public cloud providers.
Conclusion: Build, Borrow, or Hybrid?
For many large scale, regulated, high-volume smart factories, building a private cloud AI infrastructure for digital twins is not just desirable—it can become a competitive imperative. It enables control over latency, security, integration, and costs when done correctly. However, small or mid-sized operations, or those with less stringent requirements, may find public cloud or hybrid models more pragmatic, faster to deploy, and less capital intensive.Stakeholders should evaluate based on current and anticipated scale, regulatory landscape, latency/edge needs, and total cost of ownership. The optimal path often lies in a hybrid strategy that balances agility with control.
