You’re building an app. You don’t want slow pages. You don’t want the database to struggle under load. You don’t want to recompute the same things on every request. So you add Redis. Reads get faster. Latency drops. The database finally gets some breathing room. You ship with confidence. It feels like performance is handled. Then the app grows. More users. More features. More places where “just cache it” seems like the right answer. And production starts acting strange. Some users see old data. Counters drift out of sync. Memory usage keeps climbing. A cache miss suddenly sends a flood of traffic to the database. Nothing is crashing. But the system feels… fragile. What’s actually happening isn’t mysterious. You didn’t remove complexity. You moved it. Redis is not “free speed”. It’s: •A separate data layer •With its own data model •Its own failure modes •And its own trade-offs That changes how your system behaves. Without Redis: App → Database One source of truth. Clear consistency. With Redis: App → Redis → Database Now Redis becomes: •The first place your app looks for data •A shield protecting the database •A potential source of inconsistency •A major factor in correctness Cache invalidation stops being a joke. You have to decide: •What expires •What must stay in sync •What can safely be stale Redis won’t help you make those decisions. It will do exactly what you tell it to do. Even if that slowly makes things worse. Performance still obeys the same rules. Keys matter. TTL choices matter. Data shape matters. Redis removes repeated work. It removes unnecessary database load. But it doesn’t remove responsibility. You still have to: •Be deliberate about what you cache •Understand read vs write patterns •Plan for cache misses •Treat Redis as a performance tool, not a source of truth Redis makes fast systems possible. Keeping them correct, predictable, and easy to change later, that part is still on you.