Developers Turn to Cheaper LLM Providers and Smarter Architecture to Cut AI Costs

As AI-powered products gain traction, founders are finding that LLM costs scale sharply with usage, threatening the viability of their business models. A growing number of developers are switching from premium models like GPT-4 to cost-effective alternatives such as DeepSeek, which offer OpenAI-compatible APIs that require minimal code changes to adopt. Beyond provider swaps, engineers are building abstraction layers and using API aggregators to flexibly route requests across multiple models based on cost, latency, or capability. A key architectural strategy gaining traction is model tiering, where a classifier directs simple queries to cheaper models and reserves expensive ones for complex tasks. The broader shift mirrors the early cloud era, when over-provisioned infrastructure gave way to leaner, usage-optimized architectures.