Building an AI infrastructure in an uncertain environment: key considerations
Many companies are reassessing their cloud exposure. Building an on-premise, or at least hybrid, AI infrastructure is a strategic task that requires careful planning, investing in the right technology, and following best practices. Unlike pure cloud-based solutions, an on-premise setup gives you more control over your data, better security, and the ability to customize the system to meet your specific needs. However, it also requires technical knowledge, resources, and regular maintenance to work effectively.
Recent geopolitical developments have triggered a broader rethink around the use of certain cloud services - particularly for European companies handling sensitive data or operating in critical industries. The risk of dependency on infrastructure governed by non-EU jurisdictions has moved from a compliance issue to a strategic concern. This shift is accelerating interest in on-prem, or at least hybrid, setups and in European alternatives that offer more control, data sovereignty, and legal clarity. For many, it’s no longer just a question of performance or cost - it’s about resilience, autonomy, and risk management.
This guide provides a step-by-step plan for organizations that want to set up or improve their on-premise AI infrastructure. It covers essential areas like choosing the right hardware, managing data efficiently, ensuring compliance with regulations, and making the system sustainable. It also explains why having a skilled team and building strong partnerships with vendors are key to long-term success.
"Implementing AI responsibly requires a solid strategic foundation. This guide outlines a future-ready approach to building AI infrastructure that adapts to evolving needs - while prioritizing security, cost-efficiency, and resilience in a shifting geopolitical and regulatory landscape."
- Jens Eriksvik, CEO Algorithma
Building a successful on-premise AI infrastructure is a continuous process, not a single event. By adhering to these principles, optimizing your approach, and embracing continuous adaptation, you can lay a sustainable foundation for your AI initiatives and drive innovation and success in the long term.
Laying the foundation: assessing AI computational needs
Building a robust on-premise AI infrastructure starts with a solid foundation – your hardware. To select the right AI hardware, start by defining your needs: identify key objectives, quantify expected impact, and assess computational demands based on algorithm complexity and data size. When evaluating hardware, consider CPUs for versatility, GPUs for high-performance deep learning, and TPUs for specialized tensor operations, ensuring sufficient RAM and storage.
In today’s geopolitical climate, control over infrastructure is a strategic concern. Many European organizations in particular are reassessing their reliance on foreign cloud providers, looking instead at local or on-prem alternatives that offer more clarity around legal jurisdiction, operational sovereignty, and long-term risk exposure. This makes the hardware foundation not just a technical decision - but a strategic one.
Prioritize scalability by choosing solutions that adapt to growing AI workloads, leveraging distributed computing when necessary while optimizing power and cooling efficiency. Lastly, balance cost and performance by exploring open-source software, benchmarking different setups, and seeking expert guidance to make informed decisions.
Optimize training with targeted power
When training complex AI models, standard hardware might not be enough. High-Performance Computing (HPC) offers specialized solutions to accelerate your training process.
Prioritize speed when standard hardware isn’t enough
Complex AI algorithms: Deep learning models and their vast datasets require significant computational resources. Standard CPUs might struggle, leading to longer training times.
Accelerate training, achieve results faster: HPC hardware can significantly reduce training times, enabling quicker model iterations and faster achievement of desired outcomes.
Scale for future growth: As your AI ambitions evolve, so will your model complexity. HPC ensures you have the power to handle these future demands.o
Consider migrating away from monolithic systems towards distributed clusters for significant performance gains. Before investing heavily, utilize a "rent before you buy" approach, scaling and verifying at least 50% long-term elastic utilization first.
Evaluating HPC options: GPUs vs. TPUs
GPUs (Graphics Processing Units)
Strengths: Offer parallel processing capabilities that significantly accelerate deep learning tasks.
Considerations: Higher cost and power consumption compared to CPUs.
Best suited for: Training large-scale deep learning models where speed is crucial.
TPUs (Tensor Processing Units)
Strengths: Designed specifically for tensor operations in deep learning, offering potentially even faster performance than GPUs for specific tasks.
Considerations: Require specialized software support and limited applicability outside deep learning.
Best suited for: Large-scale deep learning training where maximum speed and efficiency are critical, often in research or cloud-based environments
Optimizing data management for your AI journey
Large data sets are essential fuel for your AI initiatives but managing them efficiently can be a challenge.
Where data is stored - and who ultimately has access to it - is now a board-level concern. With shifting policies around data transfer and increasing scrutiny on transatlantic cloud arrangements, European companies are turning to on-premise or EU-based storage solutions to meet both compliance requirements and internal policies around data sovereignty. Distributed, high-speed storage systems offer a way to build secure, scalable setups without compromising on performance or independence.
Prioritize efficient data storage
Modern AI models and their vast datasets: Traditional hard drives may not keep pace with data access demands, potentially impeding training and inference.
Impact on performance and results: Slow storage can be a bottleneck, hindering training progress and delaying valuable insights.
Streamlined workflows for your AI team: Fast data access facilitates collaboration and analysis, empowering your team to achieve more.
Leveraging advanced storage technologies
Solid-State Drives (SSDs): Significantly faster read/write speeds compared to HDDs, accelerating data access and improving model training times.
Non-Volatile Memory Express (NVMe): The latest generation of SSDs, offering even higher IOPS (Input/Output Operations Per Second) for demanding AI workloads. NVME is the option if you need to swap.
Distributed Storage Systems: For truly massive datasets, consider distributed storage solutions that spread data across multiple nodes, enabling parallel access and enhanced scalability.
Enabling high-speed connectivity
Your on-premise AI infrastructure relies on a robust network to facilitate seamless data exchange.
Prioritize speed and minimize latency
AI workloads demand efficient data movement: Large datasets, complex model training, and real-time inference require high-speed networks with minimal latency to avoid bottlenecks and delays.
Optimize performance for faster results: Slow networks can significantly hinder your AI pipeline, impacting training times and delaying valuable insights.
Empower collaboration and agility: Efficient network infrastructure enables smooth communication and data sharing across your AI team, leading to faster progress and collaboration.
Choosing the right network techniques
Fast Ethernet (10 GbE or above): A cost-effective option for moderate-sized deployments, providing significant speed improvements over standard Ethernet.
Network optimization techniques: Consider implementing techniques like network segmentation and Quality of Service (QoS) to further optimize data flow and prioritize AI traffic.
Equipping your AI team with the right tools
Software frameworks are an essential part of your AI toolkit, providing the foundation for building and training your models.
Leveraging frameworks for efficient development:
Simplify complex tasks: AI frameworks offer pre-built components and functions, streamlining development and reducing coding effort, allowing your team to focus on the core logic of your models. Also, open source options offer transparency, serviceability and known costs.
Utilize popular options: TensorFlow, PyTorch, and scikit-learn are widely used frameworks, each with distinct strengths and specializations.
Choose framework and hardware compatibility: Choose the framework first and make sure to get a compatible hardware (CPU, GPU, TPU) that supports it, which is crucial for optimal performance and efficient resource utilization.
Selecting the right framework for your project:
Align with your AI tasks: Different frameworks excel in specific areas. TensorFlow is powerful for deep learning, while PyTorch offers flexibility for research and rapid prototyping.
Consider your team's expertise: Select a framework familiar to your team or one with readily available learning resources to minimize onboarding time.
Evaluate community support: A large and active community can provide valuable assistance and resources during development and troubleshooting.
Installation and configuration for smooth operations:
Follow official documentation: Each framework provides detailed installation guides and configuration options tailored to your specific hardware and operating system.
Utilize online resources: Tutorials, community forums, and online courses can deepen your understanding of the framework and help you troubleshoot any issues.
Building a data foundation for your AI journey
The success of your AI journey hinges on a secure and well-managed data foundation. Ensuring data security requires comprehensive measures such as encryption for data at rest and in transit, strict access controls, and user permission systems to prevent unauthorized access and misuse. Regular software updates, security audits, and adherence to best practices help minimize vulnerabilities, while prioritizing the protection of high-value data in line with ISO 27001 principles.
Compliance with data privacy regulations like GDPR and CCPA is essential, requiring a clear understanding of industry-specific legal requirements. Establishing strong data governance policies ensures responsible data handling, while consulting legal experts helps maintain regulatory alignment and address compliance concerns as laws evolve.
To prevent data loss, organizations should implement robust backup and recovery mechanisms, scheduling regular backups across multiple locations. A well-defined disaster recovery plan enables swift response to incidents, and routine testing of backup procedures ensures data can be effectively restored when needed.
Gaining transparency for a healthy AI infrastructure
Maintaining a clear view of your on-premise AI infrastructure is essential for smooth operations and quick issue resolution. Proactive monitoring helps identify potential problems before they affect AI workloads, while tracking key performance metrics ensures efficient resource utilization and cost optimization. Comprehensive logging also aids in debugging, providing critical insights into system behavior and model performance.
Effective monitoring tools should cover both system and application levels, tracking CPU, memory, network, and storage usage, along with AI-specific metrics. Setting up alerts for critical events ensures timely intervention, preventing disruptions. A centralized logging system further enhances visibility by collecting structured and unstructured data, enabling deeper analysis and faster troubleshooting.
Choosing the right monitoring and logging tools depends on infrastructure complexity and integration needs. Larger deployments may require advanced solutions, while seamless integration with existing IT systems ensures efficient data collection and analysis. Leveraging these tools effectively enhances performance, reliability, and operational efficiency.
Building a fortified AI environment
Protecting your on-premise AI infrastructure is crucial for its success. Key security measures to implement, ensuring your valuable data, models, and systems remain secure against unauthorized access, vulnerabilities, and cyber threats need to be in place.
Building a multi-layered defence
Implement a comprehensive approach: Combine firewalls, intrusion detection/prevention systems (IDS/IPS), and endpoint protection to build a layered defence strategy mitigating diverse threats.
Granular access controls: Establish and enforce strict access controls, granting personnel only the specific permissions needed for their roles, minimizing potential damage from unauthorized access.
Data encryption: Encrypt sensitive data both at rest and in transit, ensuring confidentiality even if breached.
Maintaining vigilance through regular update
Patch management: Regularly apply software updates and security patches to address known vulnerabilities and prevent attackers from exploiting them.
Vulnerability scanning: Conduct proactive vulnerability scans to identify and address potential weaknesses in your systems and applications.
Security awareness training: Educate your team on cybersecurity best practices to minimize human error and phishing risks.
Additional security considerations for your environment
Network segmentation: Segment your network to isolate critical AI components and limit the potential impact of breaches.
Multi-factor authentication (MFA): Implement MFA for all user accounts to add an extra layer of security beyond passwords.
Regular security audits: Conduct regular security audits by internal or external experts to identify and address emerging threats and ensure continued security posture.
Building resilience for your AI infrastructure
Data and system disruptions can significantly impact your AI initiatives. Establish robust backup and disaster recovery (DR) mechanisms to ensure business continuity and data protection in the face of potential disruptions.
Prioritizing data resilience
Mitigate data loss risks: Implement comprehensive backup and DR strategies to protect your AI data, models, and training pipelines from accidental deletion, hardware failures, or cyberattacks.
Minimize downtime: Effective DR procedures ensure a swift and efficient response, minimizing potential business interruptions caused by unforeseen events.
Build trust and confidence: Robust data protection measures foster trust and confidence in your AI initiatives, demonstrating your commitment to responsible data stewardship.
Implementing reliable backup solutions
Regular backups: Establish a scheduled backup regimen, storing copies of your data in different locations (e.g., on-site and offsite) to ensure redundancy and availability.
Version control: Maintain multiple versions of your backups to facilitate recovery to specific points in time if needed.
Automated backups: Automate the backup process to minimize human error and ensure consistent data protection.
Developing a comprehensive DR plan
Identify potential threats: Analyze your infrastructure and processes to identify potential vulnerabilities and disaster scenarios.
Define recovery objectives: Determine the acceptable downtime for your AI applications and prioritize critical systems for faster recovery.
Test and refine: Regularly test your DR plan through simulations and exercises to ensure its effectiveness and identify areas for improvement.
Choosing the right backup and DR solutions
Evaluate your needs: Consider the complexity of your infrastructure, data volume, and recovery time objectives (RTOs) when choosing backup and DR solutions.
Scalability and cost-effectiveness: Select solutions that can scale with your growing data needs and offer cost-effective protection.
Integration with existing systems: Choose solutions that integrate seamlessly with your existing IT infrastructure.
Environmentally responsible on-premise AI infrastructure
High-performance AI workloads consume significant energy, making environmentally responsible on-premise infrastructure essential. Prioritizing energy efficiency starts with selecting models and hardware that minimize power usage, such as low-power CPUs, GPUs, and advanced cooling systems. Comparing energy consumption ratings and optimizing power management—through automated workload-based adjustments, off-peak scheduling, and virtualization—reduces both costs and carbon footprints.
Sustainability efforts can be further enhanced by integrating renewable energy sources like solar, wind, or geothermal power, either through on-site generation or partnerships with energy providers. Aligning AI infrastructure with corporate sustainability goals ensures responsible resource usage, from procurement to disposal, supporting broader environmental and CSR commitments.
Securing optimal support for on-premise AI infrastructure
Building and maintaining an on-premise AI infrastructure requires not only technology, but also reliable vendor partnerships for ongoing support. Select and cultivate strategic collaborations with vendors, ensuring you have the resources and support needed for long-term success.
The power of strategic partnerships
Ongoing support: Secure access to timely technical assistance, troubleshooting expertise, and problem resolution from your vendors.
Assured updates and patches: Ensure your infrastructure benefits from the latest security patches, software updates, and performance enhancements.
Proactive guidance: Collaborate with vendors to gain insights into industry best practices, emerging technologies, and potential optimization opportunities.
Selecting the right partners
Proven track record: Choose vendors with a demonstrably reliable track record, strong customer service, and expertise in your chosen technologies.
Needs alignment: Select vendors whose offerings and support services directly address your specific infrastructure requirements and future goals.
Clear communication and collaboration: Prioritize vendors who value open communication, actively engage with your team, and demonstrate a commitment to understanding your unique needs.
Building collaborative relationships
Open communication: Maintain regular communication with your vendor contacts, share information freely, and proactively discuss potential challenges or concerns.
Collaborative problem-solving: Work together with your vendors to identify solutions to challenges, leverage their expertise, and explore mutually beneficial opportunities.
Regular reviews and feedback: Conduct periodic reviews of your vendor relationships, provide constructive feedback, and seek areas for improvement in service and support.
Operating a compliant on-premise AI infrastructure
Operating an on-premise AI infrastructure demands strict adherence to relevant data protection and privacy regulations
Adhering to legal requirements
Identify applicable regulations: Understand the data protection and privacy regulations that apply to your industry, location, and data usage practices.
Implement compliance measures: Establish procedures and controls that demonstrably meet the requirements of relevant regulations, safeguarding individual rights and responsible data governance.
Minimize legal risks: Proactive compliance mitigates potential legal risks, data breaches, and fines associated with non-compliance.
For many European companies, regulatory compliance is only part of the picture - maintaining control over where and how data is processed is increasingly tied to geopolitical developments and uncertainty around data access by foreign authorities.
Securing sensitive data
Data encryption: Encrypt sensitive data both at rest and in transit to ensure confidentiality even in case of security incidents.
Granular access controls: Implement access controls that grant personnel only the specific permissions needed for their roles, minimizing unauthorized access.
Data anonymization and pseudonymization: Explore techniques like anonymization and pseudonymization when possible, to reduce risks associated with personally identifiable information (PII). And also avoid legal issues.
Key regulatory considerations
General Data Protection Regulation (GDPR): For organizations operating in the European Union or processing data of EU citizens, GDPR compliance is mandatory.
Industry-specific regulations: Additional regulations may apply depending on your industry, such as HIPAA in healthcare or PCI DSS for financial data.
Beyond meeting compliance obligations like GDPR, operating on-premise or with EU-based infrastructure partners is increasingly viewed as a way to reduce external dependencies. This is particularly relevant for sectors handling sensitive, regulated, or IP-critical data, where relying on extra-EU cloud platforms may present unacceptable strategic or legal exposure.
Extending on-prem capabilities with external APIs
While on-premise AI infrastructure offers full control, there are still valid reasons to integrate external capabilities, especially when it comes to accessing large language models (LLMs) via APIs.
For many European companies, the goal is not to isolate systems completely, but to stay in control of how and where data flows. API-based access to external LLMs makes it possible to use advanced models for tasks like summarization, classification, or natural language interaction - without committing to a full cloud-based architecture.
This hybrid approach allows teams to test and deploy LLM capabilities in a modular way, with clear boundaries and governance. Sensitive data can be pre-processed locally, stripped of identifying information, or run through prompts that are carefully engineered to avoid exposure. The API call becomes a tactical extension - not a dependency.
From a European perspective, this matters. It supports strategic autonomy while staying open to innovation. It also gives organizations room to evaluate emerging European LLMs and AI providers, many of which are growing fast in response to the demand for regionally governed alternatives.
A controlled use of external APIs - backed by on-prem infrastructure - offers the best of both worlds: flexibility to access cutting-edge capabilities, and the sovereignty to do it on your terms.
