Sub-Millisecond AI: Client-Side Semantic Caching for High-Frequency User Queries

· Full-Stack Software Development · By Hassan Nazir

Why send identical FAQ queries and common customer questions to an expensive cloud model over and over again? Here is how to implement client-side and edge semantic caching using SQLite WASM and embedding vector similarity.

In customer support and enterprise search applications, 40% of all user queries are semantically identical to questions answered earlier that day.

  • "How do I reset my API key?"
  • "Where can I change my billing payment method?"
  • "What is the refund policy for enterprise plans?"

Sending every single repetitive question across the internet to an LLM wastes money and adds 2 seconds of unnecessary latency.

Here is how to deploy a Browser-Native Client-Side Semantic Cache using WebAssembly and lightweight embedding similarity to return instant, zero-latency answers in 0.4 milliseconds.

The Browser Edge Cache Topology

[User Types Query into Browser Input]
                   │
                   ▼
[Client-Side WASM Embedding Engine (MiniLM / BGE WebAssembly)]
                   │
                   ▼
[IndexedDB / Local Vector Cache Lookup (Threshold > 0.94)]
                   │
        ┌──────────┴──────────┐
       HIT?                  MISS?
        │                      │
        ▼                      ▼
[Return Cached Answer]   [Dispatch Network Call to Server LLM]
(Latency: 0.4ms | $0.00) (Latency: 1.8s | Updates Local Cache)

Client-side semantic caching delivers an unbeatable user experience while slashing serverless inference costs by nearly half.

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