8 articles
Unit EconomicsBatching inference requests reduces AI compute costs by 40–70% for asynchronous workloads. This is the complete economic framework for when to batch, how to price for it, and how to structure product architecture to maximize batching benefits.
Unit EconomicsCaching is the highest-ROI infrastructure investment in AI-native SaaS. But the margin impact varies dramatically by product type and implementation quality. Here is the complete framework for measuring and maximizing caching's contribution to gross margin.
Unit EconomicsWhen inference costs spike unexpectedly, AI-native SaaS companies without a mitigation playbook face margin collapse. Here is the complete framework for diagnosing, absorbing, and recovering from COGS shocks in AI-native products.
Unit EconomicsAI-native SaaS gross margin is not a single number — it is a composite of inference costs, orchestration overhead, human-in-loop costs, and storage. Here is the complete decomposition framework and target benchmarks by ARR stage.
Unit EconomicsWhen inference throughput becomes the binding constraint on AI-native SaaS growth, the symptoms look like product failures but the root cause is infrastructure. Here is the complete diagnosis and mitigation framework.
Unit EconomicsModel deprecation is a hidden risk in AI-native SaaS pricing. When the model version your product depends on is retired, costs change immediately. Here is how to price for that risk before it arrives.
Unit EconomicsToken-budget pricing — selling AI access in pre-purchased token bundles — can align COGS with revenue, but poorly designed token budgets create customer confusion and churn. Here are the design rules for token-budget pricing that works.
Vertical GTMWhy AI SaaS gross margins compress below traditional SaaS targets, the specific cost drivers (inference, training, fine-tuning, infrastructure), and the architectural and pricing strategies that restore 65–75% gross margin in AI-native products.