7 posts in this topic.
Small language models can now run directly on phones with zero per-inference cost. Here's the decision framework for when on-device AI is the right architecture for your mobile app - and when cloud AI still wins.
The same AI model costs 6× more on one inference provider than another. 78% of enterprises now run their own inference. Here's how to pick providers by workload class and build the routing layer that cuts your AI agent costs by 30-50% without sacrificing speed.
DeepSeek just made a 75% price cut permanent. Inference costs are falling 50x per year. When AI models approach free, 60–75% of your project budget still goes to implementation. Here's where the real value in AI work lives - and what to prioritize.
AI-native enterprise spending surged 94% while traditional SaaS grew 8%. Here's the decision framework for evaluating which SaaS tools AI agents can actually replace today, which to keep, and where the real savings (and risks) are.
Princeton NLP found single agents match or outperform multi-agent systems on 64% of tasks. Here's the decision framework for when multi-agent orchestration is worth the complexity - and when a well-built single agent is the smarter investment.
AI coding agents can double your output - but if they also double your maintenance burden, you've quadrupled your costs. Here's the math, the research, and how to avoid the trap.
Benchmark data shows browser-based AI agents consume 45x more tokens than API-first alternatives on the same task. Here's why architecture decisions matter more than model choice for agent economics.
