Jev Engineering just cracked the LLM cost problem.
They split reasoning from execution—expensive models only run when needed, everything else routes through the cheapest capable model.
Result? 193x faster, 444x cheaper in testing.
This is how you actually scale AI infra without bleeding capital. Most teams are still burning cash running GPT-4 on every single request like idiots.
If you're building anything AI-native, this architecture shift matters. Efficiency = survival in the next 12 months.
They split reasoning from execution—expensive models only run when needed, everything else routes through the cheapest capable model.
Result? 193x faster, 444x cheaper in testing.
This is how you actually scale AI infra without bleeding capital. Most teams are still burning cash running GPT-4 on every single request like idiots.
If you're building anything AI-native, this architecture shift matters. Efficiency = survival in the next 12 months.