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What the AI Inference Cost Crisis Means for Growing Software Companies
AI inference costs are reshaping software economics. Learn why running AI costs more than building it, and how to manage margins, pricing, and governance.
The AI Inference Market in 2025 — Hardware Consolidation, Pricing Wars, and What It Means for Buyers
The AI inference market hits $106B in 2025, headed for $255B by 2030. Hardware consolidation, pricing wars, and what it means for your infrastructure costs.
How to Build AI Infrastructure Cost Governance Without a Dedicated FinOps Team
Build AI infrastructure cost governance without a FinOps team. A practical mid-market framework covering observability, alerts, and agentic AI cost control.
How to Design AI Product Pricing That Survives Variable Inference Costs
Learn how to design AI product pricing that protects gross margins when inference costs vary. Compare consumption, workflow, and outcome-based pricing models.
Cloud vs On-Premises vs Hybrid AI Inference — A Decision Framework Based on Real Cost Data
Cloud vs on-premises vs hybrid AI inference: use Deloitte’s 60-70% threshold, TCO methodology, and real cost data to make a defensible infrastructure decision.
The AI Inference Optimisation Playbook — Caching, Quantization, and Model Routing in Priority Order
Cut AI inference costs with a proven playbook: prompt caching (50–90% savings) first, then model routing, then quantization. Ordered by effort-to-impact.
Why AI Gross Margins Are So Much Lower Than SaaS and What That Means for Your Business
AI gross margins average ~52% versus 70-90% for SaaS. Understand why inference costs are structural, what Jevons Paradox means for your P&L, and what to do about it.
Why Your AI Bill Exploded Between Pilot and Production and How to Predict the Real Cost
AI costs 717x higher in production than your pilot? Discover the 5 mechanisms behind the explosion and a concrete method to forecast real costs before launch.