Building an AI app can now take just minutes. With developer tools like the AI SDK , teams can build both AI frontends and backends that accept prompts and context, reason with an LLM, call actions, and stream back results. But going to production requires reliability and stability at scale. Teams that connect directly to a single LLM provider for inference create a fragile dependency: if that provider goes down or hits rate limits, so does the app. As AI workloads become mission-critical, the focus shifts from integration to reliability and consistent model access. Fortunately, there's a better way to run. AI Gateway , now generally available, ensures availability when a provider fails, avoiding low rate limits and providing consistent reliability for AI workloads. It's the same system that has powered v0 .app for millions of users, now battle-tested, stable, and ready for production for our customers. Link to heading The case for flexibility AI capabilities have grown and changed with incredible speed.…