Dedicated vs Serverless Inference as You Scale

Choosing between serverless and dedicated inference isn't a one-time decision — it evolves as your AI system grows. Serverless is ideal early on for its flexibility and zero infrastructure overhead, but as traffic becomes predictable and latency expectations tighten, dedicated GPU infrastructure becomes more cost-effective and performant. The post walks through this lifecycle using a meeting assistant example and compares two platforms: Modal (pure serverless, per-second billing) and Together.ai (serverless-to-dedicated migration without changing code). Key insight: serverless is a phase, not a permanent default. Once workloads stabilize, delaying the move to dedicated infrastructure costs more in both money and performance.