Solving Real AI Problems Using Valkey

Valkey
AI
Open source
Developer

Wednesday September 16, 2026
8am PDT | 11am EDT | 4pm BST | 5pm CEST | 8:30pm IST | 6pm EEST

AI apps hit three problems in production, all with one root cause: sending the model work it has already done. Near-duplicate prompts bill as fresh inference calls (cost), wait seconds for an answer (latency), and eat into throughput at scale (performance).

The fix is to route requests to Valkey first. With FT.SEARCH, the cache you already run becomes a vector search layer that catches a near-duplicate prompt and serves it from memory in milliseconds, before it ever reaches the model. One move addresses cost, latency, and performance together.

Here's what we'll cover:

• Why AI apps hit cost, latency, and performance limits, and how they trace back to redundant inference calls.
• A live demo: stand up a semantic cache on Aiven for Valkey using FT.SEARCH.
• Where to go next: semantic routing, Bloom filters, hybrid search, and Valkey 9 throughput gains.

You'll leave knowing how to cut token spend and latency in your own LLM or RAG app.