Mrutyunjay (Jay) Mohapatra | Expert Advisor | Alysian.gettyWhile every enterprise is exploring AI, they eventually run into the same conundrum: Beyond the AI model itself, what are the infrastructure implications? Multiple decisions that can significantly shape cost, control, compliance and competitive advantage sit a layer below the infrastructure layer for AI. The AI infrastructure landscape is awash with choices—whether it's cloud vendors, sovereign cloud providers, specialist AI hardware vendors or SaaS providers. Choosing among these seemingly credible options requires careful consideration.Before evaluating any vendor, it's important to consider what you actually want AI for in your enterprise. Is it:• Embedding general-purpose intelligence and acceleration into knowledge work?• Fine-tuning models on existing proprietary data?• Serving high-volume inference to end customers?Each answer might mean a different infrastructure choice.Infrastructure Requirements And Capabilities​Below are some key capabilities and potential infrastructure considerations:​Elastic GPU CapacityRequire elastic GPU capacity, bundled managed services and pay-as-you-go economics with minimal capital investments for AI experiments/PoC’s, etc.? Public cloud provides a low barrier to entry; however, the trade-off is operating cost: At sustained high utilization, cloud GPU bills can eat up into AI budgets rather quickly.​​Sensitive DataRequire predictable, sustained inference or training load, have sensitive data that cannot leave a perimeter and can you amortize AI hardware investments over an extended period?On-premise infrastructure for AI is a compelling option. Large CapEx investments, longer procurement cycles and in-house engineering expertise (e.g., AI-Ops/ML-Ops) are usually not constraints here. In regulated industries (banking, healthcare, etc.) with deeply embedded IP in training data, this can be attractive.​Hybrid: Cloud And On-PremiseTrain or fine-tune models in the cloud, but deploy inference at the edge or on-premises?In mature AI organizations, where you have clear controls over where data goes, you can leverage a hybrid infrastructure model.​​Prebuilt ModelsYour data is in the public cloud, and you want to leverage existing compliance and commercial agreements, take advantage of single billing and existing security/IAM boundaries for your AI infrastructure needs?Anthropic Claude is offered through AWS (Bedrock), Google (Vertex AI) and Microsoft Foundry. OpenAI models are offered through the Azure OpenAI Service. In such cases, the AI infrastructure decision is less about the AI model itself, but more about your cloud affinity/stickiness, operating model, governance, networking, data-residency, etc.​​Open-Weight ModelsIs your requirement complete control of model weights (learnable parameters), deterministic versioning (versus probabilistic AI) or fully air-gapped, no API-fees and no-censorship models?Self-hosting open-weight models on your own GPU infrastructure is a great choice. Remember, you take on the operational burden of serving, scaling and securing such infrastructure.​Sovereign AIIs your requirement sovereign AI, where your models, training data and inference need to stay within a specific jurisdiction and the underlying infrastructure is governed under jurisdictional laws?Public cloud providers offer dedicated sovereign cloud infrastructure. If public cloud is not an option, fully on-premises AI infrastructure stacks let you address constraints such as GDPR, data transfer in and out, etc.​​Compute HardwareAre you after specific AI compute hardware? AI hardware vendors offer a wide array of choices and often bundle with software ecosystems. For on-premise, large traditional hardware vendors offer turnkey solutions, with pre-certified GPU configurations, integrated networking, storage and reference architectures. With such options, you avoid the pain of sourcing and stitching components together. Cloud hyperscalers also offer their own,​Embedded AIDon’t want to bother about AI infrastructure at all? In many cases, you might not need to build or worry about AI infrastructure at all. Multiple big SaaS vendors offer their own AI embedded into their products, and you consume AI as a capability or a service, with the infrastructure layer abstracted away entirely.​​Proprietary Vs. Open StandardsWhile proprietary infrastructure can enable quicker adoption, it also introduces a risk of vendor lock-in. Standardizing AI infrastructure on open interfaces, OpenAI-compatible APIs (now widely supported), open-source Kubernetes, OpenTelemetry (for observability) and vector databases that support open protocols is key.​​Key Design Paths: A Decision TreeThink about building your decision tree with key design decisions and paths while choosing your AI infrastructure.1. Start from the AI workload itself until you reach a deployment pattern.2. Assess your AI workloads before you start assessing vendors.3. If you need custom behavior, decide whether sensitive data needs to stay in-jurisdiction or on-premise; the answer splits the choices between sovereign or self-hosted paths and public cloud paths.4. Where is your data? AI runs best where data already resides. Think about your infrastructure architecture in the context of data sovereignty and regulation.5. Begin by asking whether the AI capability you need already exists inside a SaaS suite you own—if yes, leverage it and govern the data flows. You don't need a GPU strategy.6. Time horizon decides CapEx versus OpEx: With on-premise AI infrastructure providers, you get predictable unit economics; however, that comes with large CapEx with multiyear amortization. You also need mature AI engineering and MLOps. Sustained training and inference require CapEx-heavy on-premise; bursty or experimental workloads are ideal for cloud (OpEx).7. Cloud-hosted Anthropic, OpenAI or cloud hyper-scaler GPUs offer better time-to-value and elastic scaling; however, as usage grows, so do the costs.8. Also think about data egress fees, model version drift, GPU supply lead times and the slow creep of proprietary APIs that quietly become very sticky.Sometimes, the answer lies in the middle—a hybrid strategy: cloud for speed, on-premise for sensitive or steady-state inference work, open-standards layer to preserve optionality and clarity where infrastructure is not a consideration at all.Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?