Build vs Buy in 2026 – Private AI Infrastructure or Token Consumption Forever?

By 2026, enterprise AI strategy hinges on one core bet: do we continue to consume tokens from hyperscaler LLMs (OpenAI, Anthropic, Gemini, Grok, etc.), or do we build and operate private AI stacks powered by open-source models on our own fabrics? This session puts that decision under a microscope. Executives will compare real TCO/ROI of token-only versus hybrid versus private-first approaches, factoring in data residency, GPU FinOps, compliance, and strategic control over models and data.
Key Questions: – What are the non-negotiable criteria for when a workload must move from public LLM APIs to private infrastructure (e.g., data sensitivity, latency, cost predictability)? – How do you compare a token-based cost curve versus multi-year capex/opex for GPU farms, storage, networking, and operations teams? – How do open-source LLMs (and fine-tuning on your own data) change the build versus buy calculus? – What decision frameworks are boards and regulators expecting for model sourcing, data control, and sovereignty?
Takeaways: – A decision framework to categorize workloads into Token, Hybrid, or Private/On-Prem. – Guidance on aligning AI infrastructure decisions with GPU FinOps and budget governance. – Example 24–36 month roadmaps from enterprises already moving off token-only usage.