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Infrastructure for Running Your Own AI

Infrastructure for Running Your Own AI

If building unique AI capabilities offers a competitive advantage, organizations must factor in infrastructure costs, whether in the cloud or on-premises. AI model training demands high performance GPUs and CPUs running 24/7, shifting cost structures throughout the AI development lifecycle.


Cloud Infrastructure

All major cloud providers offer AI-optimized infrastructure, from configurable compute and storage to turnkey platforms fine-tuned for their ecosystems. Choosing the right cloud solution depends on cost, performance, and integration needs.


Cloud Capacity Constraints

The demand for Generative AI is skyrocketing – McKinsey reports adoption nearly doubled from 33% in 2023 to 65% in early 2024, while Gartner predicts that 10% of all data will be AI-generated by 2025. This surge is straining cloud capacity, particularly for GPU-powered services.

To mitigate availability risks, organizations must proactively secure GPU reservations. Managing AI infrastructure procurement now requires long-term resource planning, like traditional on-premises capacity management.


On-Premises Infrastructure

Whereas on-prem AI requires significant upfront investment, a cost analysis is essential to compare capital expenses against ongoing cloud costs.  Data movement costs often become a tipping point in this decision.  In response, some organizations train AI models on-premises to reduce data transfer costs, then deploy to the cloud for scalability.


On-premises AI infrastructure requires:

  • Compute (GPUs) and Storage

  • Virtualization and HPC management software

  • Networking, power, and cooling upgrades (if datacenter expansion is necessary) Organizations must weigh long-term control and cost stability against cloud flexibility and scalability when deciding their AI infrastructure strategy.

 

Labor Costs and Skill Gaps

The accessibility of Generative AI has accelerated cloud adoption across product, marketing, sales, and leadership teams. The 2024 McKinsey Global State of AI report highlights that nearly half of all AI development is now “build your own”, increasing demand for in-house AI software development despite the availability of serverless services.


AI development labor, like traditional software, is often capitalized over its useful life. However, many organizations lack the required skill sets, leading to increased outsourcing costs or the need for specialized training. Key AI skill sets include:

  • Programming: Python, R, C++ for AI model development.

  • Data Management: Big data expertise and analytics proficiency.

  • Machine Learning: Deep learning algorithm implementation.

  • AI Frameworks: TensorFlow, PyTorch for model training.

  • Analytical Thinking: Validating AI outputs to ensure accuracy


Labor Costs and Skill Gaps


AI Training Costs

AI model training demands significant resources. Factors impacting costs include:

  • Data Complexity: Unstructured data increases computational requirements.

  • Natural Language Processing (NLP): Large datasets drive higher storage and processing costs.


Defining clear objectives, selecting efficient architectures, and experimenting with smaller models before scaling can help control AI training expenses.


Data Management and Movement

Effective data management is critical for cost-efficient AI operations. Key strategies include:

  • Data Cleaning: Deduplication, normalization, and validation reduce inefficiencies.

  • Lifecycle Management: From storage to disposal, optimized handling improves cost efficiency.

  • Volume Control: Prioritizing high-quality, relevant data reduces unnecessary storage and compute expenses.


Governance, Compliance and Security Costs

AI expands the cyberattack surface, increasing risk exposure. The 2025 Davos Conference identified foreign cyber threats as a top economic concern. Organizations must implement:

  • Robust Governance: Ethical AI guidelines and decision-making frameworks.

  • Security Measures: Encryption, privacy safeguards, and AI system monitoring.

  • Compliance Controls: Regular audits to mitigate legal and financial risks.

 

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