Centralize session management, caching, & rate limiting

Session state fragments across services, rate limits behave differently at the edge than at the core, and caches drift out of sync under load. As a unified real-time data layer, Redis cuts through that complexity giving every API, service, and device a single, authoritative view of user state and controls.

We'll move beyond theory and walk through reference architectures you can adapt directly, covering the patterns that engineering teams need to handle traffic spikes with predictable performance, eliminate stale data without sacrificing speed, and build the low-latency context layer that modern AI-driven workloads require.

• Portable, region-aware session management: centralize user state so sessions stay hot and consistent across regions, services, and devices.
• Consistent rate limiting at the edge and core: enforces the same limits everywhere, not just at the gateway, so traffic spikes don't punch through to your backend.
• Cache patterns that eliminate stale reads: offload expensive queries without serving outdated data, cache invalidation strategies that actually work in distributed environments.
• Low-latency context for AI-driven workloads: structure your data layer to serve the sub-millisecond context lookups that LLM-powered features depend on at scale.

Live Q&A to follow.