The "Déjà Vu" Effect: Cutting GenAI Costs with Semantic Caching

 There’s a common pattern in enterprise AI: users ask the same questions again and again.

Think about your internal knowledge base. Employees constantly ask things like:

  • “How do I reset my VPN?”
  • “What are the Q3 sales figures?”
  • “What’s the company policy on expense reports?”

A standard RAG system has no memory of these past interactions. Every time a question comes in, it repeats the same expensive steps:

  • Embed the query
  • Search the vector database
  • Retrieve the top documents
  • Send everything to the LLM (GPT-4, Llama, etc.)
  • Generate an answer

Even if the question—and the answer—was identical five minutes ago.

This is incredibly wasteful. You’re paying for retrieval and inference on every request, even when nothing has changed. It’s like running a factory production line just to print the same invoice twice.

The result is predictable: rising costs and unnecessary latency.

The fix is semantic caching—a technique that recognizes repeat intent and serves answers instantly, without re-running the expensive “thinking” steps.


The Prototype Trap vs. Production Reality

In a GenAI proof of concept, cost rarely matters. The goal is simple: make it work.

In production, the priorities flip. Cost is king. Your AI needs to be accurate and economical. Relying on full RAG and LLM calls for every single query is a fast path to budget overruns.

At the heart of the problem is a missing layer: memory.

Without it, the system treats every request as a cold start—even when it has already solved the exact same problem moments earlier.


The Solution: The Semantic Cache

Semantic caching introduces a simple but powerful architectural pattern.

Before a query reaches the expensive retrieval and LLM pipeline, the system checks whether it has seen a meaningfully similar question before. If it has, it returns the cached answer immediately.

Here’s how the flow works:

  • Query interception - Before the agent does any work, the incoming query is checked against the cache.
  • Semantic similarity - The cache doesn’t rely on exact string matches. It uses vector similarity search to find questions with the same meaning (for example, “Reset VPN” ≈ “Fix VPN connection”).
  • Cache hit - If a close match is found (for example, similarity > 0.95), the cached response is returned instantly. Cost: effectively zero.
  • Cache miss - If no match is found, the system falls back to the normal RAG pipeline and generates a fresh answer.


Technical Implementation Pattern (Databricks)

On Databricks, this pattern is typically implemented using Unity Catalog for governance and Mosaic AI Vector Search for similarity lookup.

from databricks.vector_search.client import VectorSearchClient

vsc = VectorSearchClient()
cache_index = vsc.get_index("main.finops.semantic_cache_index")

def semantic_cache_lookup(query, tenant_id):
    """
    Checks the cache for a similar question asked previously by this tenant.
    """
    results = cache_index.similarity_search(
        query_text=query,
        num_results=1,
        # Mandatory filters ensure we don't leak answers between clients
        filters={"tenant_id": tenant_id},
        debug_level=1
    )
    
    # If we find a high-confidence match, return it
    if results and results["score"] > 0.95:
        return results["answer"]
        
    return None


When this check succeeds, the system bypasses retrieval and inference entirely. For frequently asked questions, responses become instant—and essentially free.


The Business Impact: Speed, Savings, and Sanity

  • A well-designed semantic cache has a direct impact on the metrics executives care about:
  • Reduced latency - Answers arrive in milliseconds instead of seconds, improving user satisfaction and reducing drop-off.
  • Lower inference costs - By avoiding repeated LLM and retrieval calls, token usage drops sharply. In repetitive workloads, this can translate into 50% or more savings on inference spend.
  • Improved throughput - The same serving infrastructure can support more users, because fewer requests reach the expensive stages.
  • Higher consistency - Common questions receive consistent answers, reducing variability and the perceived risk of hallucinations.


Managerial Takeaway: The Cheapest Inference Is the One You Don’t Run

Caching isn’t a performance trick. It’s an AI FinOps necessity.

When designing your RAG system, ask a few simple questions:

  • What queries show up every day?
  • Do those answers really need to be regenerated each time?

By optimizing for the most common patterns, you can cut costs dramatically while improving the user experience. The principle is simple—and powerful:

The cheapest inference is the one you don’t have to run.

Location: Wyoming, USA

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