73% Hybrid AI: Choose Cloud, On-Prem or Hybrid Simreka Setup

Pick the best Simreka deployment – cloud or on-prem AI.

The deployment decision for enterprise AI platforms has never been more critical—or more complex. As organizations race to harness AI’s transformative potential for materials R&D, the infrastructure question looms large: Should you deploy in the cloud, maintain on-premise control, or pursue a hybrid approach? The answer shapes everything from data security and compliance to cost structures and scalability.

For CIOs, IT directors, and R&D leaders evaluating Simreka for materials innovation, this decision carries particular weight. Your deployment model determines how seamlessly researchers access AI capabilities, how effectively you protect proprietary formulation data, and how flexibly you scale as innovation demands evolve. Making the right choice requires understanding not just technological capabilities, but your organization’s unique security requirements, regulatory constraints, and strategic priorities.

The Current Landscape: Where Enterprise AI Deployment Stands Today

The enterprise AI deployment landscape is experiencing dramatic shifts. According to Mordor Intelligence, on-premises installations held 56.4% of the AI infrastructure market share in 2024, while cloud solutions are projected to grow at 20.6% CAGR through 2030. This seemingly contradictory data—on-premise dominance alongside explosive cloud growth—reflects a market in transition.

But the most revealing trend isn’t the cloud versus on-premise split—it’s the surge toward hybrid architectures. Flexera’s 2024 State of the Cloud Report found that 73% of organizations now use hybrid cloud environments, reflecting growing demand for flexible infrastructure that balances security with agility. Even more telling, IDC predicts that by 2027, 75% of enterprises will adopt hybrid models to optimize AI workload placement, cost, and performance.

The investment numbers underscore AI infrastructure’s strategic importance. AI infrastructure spend reached $47.4 billion in 2024, marking a remarkable 97% year-over-year increase. Organizations aren’t just deploying AI—they’re making massive infrastructure commitments that will shape their competitive positioning for years to come.

Cloud Deployment: Agility, Scalability, and Rapid Value Realization

The Cloud Advantage

Cloud deployment offers compelling benefits that have driven its rapid adoption across industries. The most immediate is speed to value. With Simreka deployed in the cloud, organizations can provision access for research teams within days rather than months, eliminating lengthy hardware procurement and configuration cycles.

Scalability represents another critical advantage. Materials R&D workloads fluctuate dramatically—a team might run thousands of virtual experiments during formulation optimization, then require minimal computational resources during synthesis and testing phases. Cloud infrastructure scales elastically to match these varying demands, ensuring you pay only for resources actually consumed.

The cloud AI market’s explosive growth—projected to surge from USD 80.30 billion in 2024 to USD 327.15 billion by 2029 at a 32.4% CAGR—reflects these advantages resonating with enterprise decision-makers. Organizations recognize that cloud deployment removes infrastructure management burden, allowing IT teams to focus on value-adding activities rather than server maintenance.

When Cloud Makes Sense

Cloud deployment proves particularly advantageous for organizations with:

• Distributed R&D Teams: If your materials scientists span multiple sites or geographies, cloud-based Simreka provides seamless access regardless of location, facilitating collaboration without VPN complexity.
• Variable Computational Demands: Organizations experiencing feast-or-famine R&D cycles benefit from cloud elasticity, scaling up for intensive simulation campaigns then scaling down during quieter periods.
• Rapid Deployment Requirements: If you need AI capabilities operational quickly—perhaps to support a critical product launch or respond to competitive pressure—cloud deployment delivers fastest time-to-value.
• Limited IT Infrastructure Resources: Smaller organizations or those with constrained IT budgets can access enterprise-grade AI capabilities without capital expenditure on hardware.

Cloud Considerations and Challenges

Despite its advantages, cloud deployment introduces considerations that matter deeply to R&D organizations. Data sovereignty tops the list—when proprietary formulation data resides in cloud infrastructure, it crosses organizational boundaries, potentially raising regulatory and competitive concerns.

According to Cloudera research, 53% of organizations identified data privacy as their biggest AI adoption obstacle, outranking both technical integration challenges and implementation costs. For materials companies where formulation IP represents core competitive advantage, these concerns aren’t abstract—they’re existential.

Ongoing costs present another consideration. While cloud eliminates upfront capital expenditure, subscription-based pricing accumulates over time. For organizations with sustained, predictable AI usage, on-premise infrastructure may deliver better long-term economics despite higher initial investment.

On-Premise Deployment: Control, Security, and Strategic IP Protection

The On-Premise Case

On-premise deployment appeals to organizations prioritizing data sovereignty, regulatory compliance, and absolute control over their AI infrastructure. When Simreka runs on your servers, within your firewall, proprietary formulation data never leaves your facilities. For companies in highly regulated industries or those with particularly sensitive IP, this assurance proves invaluable.

Interestingly, on-premise isn’t declining despite cloud’s growth—it’s evolving. Recent trends show enterprises shifting to on-premises AI to control costs, with major vendors like HPE reporting that revenue from AI systems rose 16% to $1.5 billion, while Dell reported AI server orders reaching a record $3.6 billion. Organizations are making substantial on-premise investments, viewing local infrastructure as strategic assets rather than legacy burdens.

When On-Premise Excels

On-premise deployment proves particularly compelling for:

• Highly Regulated Industries: Pharmaceutical, defense, and certain chemical sectors face stringent data residency requirements that cloud deployment complicates. On-premise Simreka simplifies compliance by keeping all data within controlled boundaries.
• IP-Sensitive Organizations: Companies where formulation expertise constitutes primary competitive advantage may mandate on-premise deployment to eliminate external data exposure risk.
• Sustained High-Volume Usage: If your R&D teams consistently run intensive AI workloads, on-premise infrastructure delivers better economics than cloud subscription costs, achieving ROI through continuous utilization.
• Existing Infrastructure Investments: Organizations with substantial on-premise computational resources can leverage existing hardware, maximizing prior investments rather than incurring new cloud costs.
• Network Latency Requirements: Applications requiring extremely low latency or high-bandwidth data transfer benefit from local infrastructure, avoiding internet connectivity constraints.

On-Premise Trade-offs

On-premise deployment’s advantages come with corresponding challenges. Capital expenditure represents the most obvious—purchasing servers, storage, and networking equipment requires upfront investment before realizing any AI value. For organizations with limited capital budgets or those preferring operational expense models, this hurdle can prove significant.

Maintenance burden also shifts to internal IT teams. Server updates, security patches, hardware failures, and capacity planning become organizational responsibilities. While this provides control, it also demands dedicated resources that could otherwise focus on strategic initiatives.

Scalability proves less elastic than cloud alternatives. If R&D demands suddenly spike—perhaps due to an unexpected competitive challenge or breakthrough requiring intensive follow-up investigation—on-premise infrastructure scales only as quickly as you can procure and configure additional hardware.

The Hybrid Middle Ground: Best of Both Worlds

Why Hybrid Architectures Are Winning

The surge toward hybrid deployment—recall that 73% of organizations now employ hybrid cloud environments—reflects recognition that cloud versus on-premise needn’t be binary. Hybrid architectures allow organizations to strategically place different workloads based on their specific requirements, optimizing across multiple objectives simultaneously.

Simreka’s flexible deployment architecture supports exactly this approach. Organizations can deploy core Virtual Experiment Platform capabilities on-premise while leveraging cloud infrastructure for burst computing, collaborative projects involving external partners, or non-sensitive workloads.

Real-World Hybrid Implementations

Major manufacturers demonstrate hybrid architecture’s practical advantages. Toyota opted for a hybrid architecture combining on-premises infrastructure and cloud computing, with the hybrid environment coupled with microservices-based architecture allowing rapid iteration and deployment of new features while maintaining robust security.

Aurobay collaborated with Microsoft to implement hybrid cloud architecture powered by Microsoft Entra ID and Azure Arc, better managing operational applications both on-premises and in the remote cloud. These implementations aren’t compromises—they’re optimized solutions leveraging each deployment model’s strengths for appropriate use cases.

Hybrid Use Case Examples

Consider how a materials company might deploy Simreka in a hybrid configuration:

• Proprietary Formulation Development: Core R&D on next-generation products runs on-premise, ensuring competitive formulation data never leaves corporate infrastructure.
• Collaborative Research Projects: Joint development initiatives with academic partners or customers operate in cloud environments, facilitating data sharing without granting external parties access to internal networks.
• Burst Computing for Intensive Campaigns: When running thousands of virtual experiments during optimization, the system automatically provisions additional cloud resources, then releases them when the campaign completes.
• Disaster Recovery and Business Continuity: Cloud-based backups ensure R&D continuity even if on-premise infrastructure experiences failures or disasters.

Security and Governance: Critical Considerations Across All Models

Regardless of deployment choice, security and governance demand careful attention. Research reveals that nearly two-thirds (64%) of organizations lack full visibility into their AI risks, leaving them vulnerable to security blind spots and compliance failures. Meanwhile, 95% of businesses recognize the need to revamp governance for AI’s evolution, though many struggle with budget limits and organizational inertia.

The shadow AI phenomenon compounds these challenges. According to LayerX research, AI tools now drive most enterprise data leaks, with 77% of sensitive data pasted via personal accounts. While 40% of organizations have purchased enterprise LLM subscriptions, over 90% of employees actively use AI tools in their daily work—often through unapproved channels that bypass security controls.

Simreka addresses these concerns through comprehensive security frameworks applicable across all deployment models:

• Role-Based Access Control: Granular permissions ensure users access only data and capabilities appropriate to their roles.
• Audit Logging: Comprehensive activity tracking provides visibility into who accessed what data and when, supporting both security monitoring and regulatory compliance.
• Data Encryption: Both at-rest and in-transit encryption protects sensitive formulation data throughout its lifecycle.
• Integration with Enterprise Identity Systems: Support for SAML, LDAP, and other enterprise authentication mechanisms ensures Simreka fits seamlessly into existing security infrastructure.

Making Your Decision: A Framework for Deployment Choice

Choosing your Simreka deployment model should be systematic, considering multiple factors specific to your organization. Here’s a decision framework to guide your evaluation:

Step 1: Assess Your Data Sensitivity and Regulatory Environment

Begin by categorizing your R&D data by sensitivity level. Proprietary formulations under active patent prosecution likely demand highest protection, while published research data may tolerate cloud storage. Map your regulatory obligations—ITAR, EAR, GDPR, industry-specific requirements—and determine which deployment models satisfy them.

Step 2: Evaluate Your Existing Infrastructure and IT Capabilities

Take honest stock of your current infrastructure and team capabilities. Do you have underutilized computational resources that could host AI workloads? Does your IT team have bandwidth to manage additional on-premise systems, or are they already stretched thin? Can your network infrastructure support cloud connectivity requirements?

Step 3: Project Your Usage Patterns and Scale Requirements

Estimate your AI usage patterns. Will workloads be relatively steady, or will they spike during intensive development campaigns? How many researchers need access, and where are they located? How quickly do you need to scale as adoption grows? These patterns heavily influence which deployment model optimizes cost and performance.

Step 4: Calculate Total Cost of Ownership Across Models

Build comprehensive TCO models for each deployment option over a 3-5 year horizon. For cloud deployment, project subscription costs based on expected usage. For on-premise, include hardware, software licensing, facilities costs (power, cooling, space), and personnel time for management and maintenance. Don’t forget to factor in opportunity costs—what else could those capital dollars or IT personnel accomplish?

Step 5: Consider Your Strategic Priorities and Risk Tolerance

Finally, weigh strategic factors. How critical is time-to-market for the innovations Simreka will enable? Can you afford longer on-premise deployment timelines, or do competitive pressures demand immediate capability? How risk-averse is your organization regarding data sovereignty? What does your board think about cloud versus on-premise for strategic AI capabilities?

Implementation Best Practices Across Deployment Models

Regardless of which deployment model you choose, several best practices enhance success:

Start with a Pilot

Rather than enterprise-wide rollout, begin with a pilot project involving a single R&D team and well-defined use case. This allows you to validate the deployment model, identify integration challenges, refine security controls, and build organizational confidence before broader deployment.

Plan for Integration

Simreka doesn’t operate in isolation—it needs to integrate with existing systems like ELN (Electronic Lab Notebook) platforms, PLM (Product Lifecycle Management) systems, and enterprise data warehouses. Design these integrations early, considering data flow, authentication, and synchronization requirements.

Invest in Change Management

Technology deployment alone doesn’t drive adoption. Invest in training, documentation, and change management to ensure researchers understand Simreka’s MatIQ – the AI Co-Pilot for Material Innovation and other capabilities, trust its predictions, and incorporate it into their workflows.

Monitor and Optimize

Post-deployment, continuously monitor usage patterns, performance metrics, and cost trends. Cloud deployments offer particular flexibility—you might discover that certain workloads perform better or cost less if migrated to on-premise infrastructure, or vice versa. Hybrid architectures enable ongoing optimization as you learn which workloads best fit each environment.

Future-Proofing Your Decision

The AI deployment landscape continues evolving rapidly. Edge AI, federated learning, and confidential computing represent emerging paradigms that may influence future deployment decisions. Choose deployment options that preserve flexibility rather than locking you into rigid architectures.

Simreka’s architecture supports this flexibility. Organizations can begin with cloud deployment for rapid value realization, then migrate specific workloads on-premise as requirements or capabilities change. Or start on-premise and add cloud capacity for burst workloads or collaboration. The platform’s consistent experience across environments means researchers don’t need to learn different systems as deployment evolves.

Conclusion: Deployment as Strategic Enabler, Not Just Technical Decision

The cloud versus on-premise decision for Simreka isn’t merely a technical infrastructure choice—it’s a strategic decision that influences innovation speed, data security, cost structures, and competitive positioning. Getting it right requires understanding your organization’s unique requirements, constraints, and priorities.

The good news? You don’t have to choose once and forever. The trend toward hybrid architectures—adopted by 73% of organizations and projected to reach 75% by 2027—reflects recognition that different workloads have different optimal homes. The most sophisticated organizations strategically place AI capabilities based on workload characteristics, optimizing across security, performance, cost, and agility simultaneously.

As you evaluate deployment options, remember that the infrastructure decision should serve your innovation objectives, not constrain them. Whether you choose cloud for rapid deployment and scalability, on-premise for maximum control and compliance, or hybrid for strategic optimization, the goal remains constant: empowering your materials scientists with AI capabilities that accelerate discovery, reduce development costs, and drive competitive advantage.

The AI infrastructure market’s explosive growth—from $87.6 billion in 2025 to a projected $197.64 billion by 2030—demonstrates that organizations are making substantial commitments to AI-powered innovation. Your deployment decision positions you to capture maximum value from that investment, ensuring Simreka’s Virtual Experiment Platform, Databank, and MatIQ deliver transformative impact aligned with your organization’s strategic priorities and risk tolerance.

Frequently Asked Questions

Q1. Can we switch deployment models after initial implementation?

Yes, Simreka’s architecture supports migration between deployment models. Many organizations start with cloud deployment for rapid value realization, then migrate specific workloads on-premise as requirements evolve. The platform maintains consistent user experience across environments, so researchers don’t face disruption during transitions. However, migration does require planning—particularly for data transfer, integration reconfiguration, and validation testing.

Q2. How does hybrid deployment work technically—won’t managing two environments double complexity?

While hybrid deployment adds some complexity compared to single-environment approaches, modern orchestration tools substantially mitigate this. Simreka provides unified management interfaces that span cloud and on-premise environments, allowing IT teams to monitor, configure, and maintain both through consistent tooling. Organizations like Toyota and Aurobay successfully operate hybrid AI architectures by treating them as unified systems rather than separate deployments. The key is proper architectural planning upfront.

Q3. What’s the typical cost difference between cloud and on-premise deployment?

Cost comparison depends heavily on usage patterns and time horizon. Cloud deployment typically costs less in year one due to no capital expenditure, but ongoing subscription costs accumulate over time. On-premise requires upfront hardware investment but delivers lower total cost for sustained, high-volume usage over 3-5 years. A general rule: if you expect to utilize AI infrastructure heavily and consistently, on-premise economics often win beyond year two. For variable or lower usage, cloud remains more cost-effective. Request a customized TCO analysis from Simreka based on your specific requirements.

Q4. How do we ensure data security with cloud deployment?

Cloud security relies on multiple complementary controls: encryption in transit and at rest, role-based access control, comprehensive audit logging, network isolation, and compliance certifications (SOC 2, ISO 27001, etc.). Simreka’s cloud deployment implements enterprise-grade security frameworks and can be configured to meet specific industry requirements. For particularly sensitive data, consider hybrid deployment where confidential formulations remain on-premise while less sensitive workloads run in the cloud.

Q5. What regulatory compliance frameworks does Simreka support?

Simreka supports deployment configurations compliant with major regulatory frameworks including GDPR for data privacy, ITAR and EAR for controlled technical data, FDA 21 CFR Part 11 for pharmaceutical applications, and various industry-specific requirements. On-premise deployment simplifies compliance for highly regulated industries by keeping all data within organizational boundaries. Simreka’s security team can work with your compliance officers to configure deployments meeting your specific regulatory obligations.

Q6. How long does deployment typically take for each model?

Cloud deployment of Simreka typically takes 2-4 weeks from contract signing to researchers running experiments, including configuration, integration, and training. On-premise deployment takes longer—typically 8-16 weeks—due to hardware procurement, installation, network configuration, and validation. Hybrid deployment timelines fall in between, usually 6-12 weeks depending on which components deploy where. However, these timelines assume standard configurations; highly customized implementations or complex integrations may require additional time.

Bibliographical Sources

1. Mordor Intelligence (2024). ‘AI Infrastructure Market Size, Share Analysis & Growth Research Report, 2030.’ Available at: https://www.mordorintelligence.com/industry-reports/ai-infrastructure-market
2. InfraCloud (2024). ‘On-Premise AI vs. Cloud AI: Making the Right Infrastructure Choice.’ Available at: https://www.infracloud.io/blogs/on-premise-ai-vs-cloud-ai/
3. TechTarget (2024). ‘Enterprises shift to on-premises AI to control costs.’ Available at: https://www.techtarget.com/searchenterpriseai/news/366617361/Enterprises-shift-to-on-premises-AI-to-control-costs
4. MarketsandMarkets (2024). ‘Cloud AI Market Size, share, Trends, Growth Analysis.’ Available at: https://www.marketsandmarkets.com/Market-Reports/cloud-ai-market-24849814.html
5. Google Cloud Blog (2025). ‘How Toyota is revolutionizing manufacturing with AI.’ Available at: https://cloud.google.com/blog/topics/hybrid-cloud/toyota-ai-platform-manufacturing-efficiency
6. Microsoft Industry Blogs (2025). ‘Unlocking the potential of manufacturing with cloud modernization.’ Available at: https://www.microsoft.com/en-us/industry/blog/manufacturing-and-mobility/manufacturing/2025/08/19/unlocking-the-potential-of-manufacturing-with-cloud-modernization/
7. Cloudera Report via Kiteworks (2025). ‘AI Agents Are Advancing—But Enterprise Data Privacy and Security Still Lag.’ Available at: https://www.kiteworks.com/cybersecurity-risk-management/ai-agents-enterprise-data-privacy-security-balance/
8. PRNewswire (2024). ‘New Study Reveals Major Gap Between Enterprise AI Adoption and Security Readiness.’ Available at: https://www.prnewswire.com/news-releases/new-study-reveals-major-gap-between-enterprise-ai-adoption-and-security-readiness-302469214.html
9. The Hacker News (2025). ‘New Research: AI Is Already the #1 Data Exfiltration Channel in the Enterprise.’ Available at: https://thehackernews.com/2025/10/new-research-ai-is-already-1-data.html

Ready to Choose Your Optimal Deployment?

Explore how Simreka’s flexible deployment options—cloud, on-premise, or hybrid—can be tailored to your organization’s security requirements, infrastructure capabilities, and strategic priorities.

Schedule a consultation to discuss your optimal Simreka deployment strategy →

Tag Cloud