Quant Finance Gets Smarter: Semantic Caching Reduces LLM Costs and Latency

In the rapidly evolving landscape of quantitative finance, large language models (LLMs) have become indispensable tools for synthesizing reports, parsing indicators, and evaluating assets. However, their widespread adoption has often been hampered by escalating API costs and latency. Each interaction with a remote LLM endpoint, even for slightly rephrased versions of identical underlying financial questions, typically incurs a new charge and processing delay.

Addressing the LLM Efficiency Challenge

To mitigate these inefficiencies, a recent laboratory study explored the potential of a semantic caching layer. The central hypothesis was whether such a layer, powered by vector embeddings, could intercept redundant queries before they reached the remote model, thereby preserving the nuanced accuracy essential for financial analysis while simultaneously reducing token consumption and processing time.

Experimental Framework and Key Discoveries

Researchers constructed an experimental pipeline designed to test this theory. The setup incorporated live market data retrieval, dense vector encoding, and a FAISS index leveraging inner product similarity. Twenty distinct quantitative research prompts were simulated across three categories: asset valuation, technical indicator interpretation, and earnings report synthesis.

Initial expectations for universal cache suppression across all financial domains were refined by the experimental outcomes. While highly structured valuation queries demonstrated consistent semantic similarities, leading to a measurable cache reuse, more exploratory technical and earnings prompts exhibited different behavior. A significant finding was the impressive 20 percent cache hit rate specifically observed in asset valuation questions. This result underscores both the considerable promise and inherent limitations of strict cosine similarity thresholds within quantitative financial applications.

Architecting Real-Time Market Relevance

A critical component of the architecture involved dynamically fetching real-time market data. Static prompts containing outdated stock prices quickly lose operational relevance. To counteract this, a dedicated FinancialDataFetcher class was engineered to pull current pricing metrics and 30-day returns for a selected basket of tickers (e.g., Apple, Microsoft, NVIDIA, Alphabet, Amazon). This ensured that all generated queries reflected live market conditions.

Transforming Queries into Embeddings

The next phase involved converting natural language research queries into dense vectors suitable for semantic comparison. A PromptEmbedder utilized a sentence-transformers model, chosen for its optimal balance of low inference latency and robust semantic differentiation within financial terminology. This process enabled the system to understand the underlying meaning of queries, rather than just their surface-level text.

The Semantic Cache Engine

At the core of the optimization strategy was the SemanticCacheManager. This component employed FAISS with an IndexFlatIP, where L2-normalized embeddings allow inner product calculations to precisely equate to cosine similarity. Upon receiving a new query, the manager encoded its text, searched the index, and evaluated the highest similarity score against a predefined threshold (0.80 in this experiment). If a match was identified, a precomputed response was instantly delivered. Otherwise, the query seamlessly fell back to the remote LLM, such as the Gemini model.

The successful implementation and testing of this semantic caching pipeline reveal a viable path to substantially reduce LLM API costs and latency for specific, structured quantitative research tasks. While the approach proved particularly effective for asset valuation queries, it also highlighted areas where more adaptive caching strategies might be beneficial for less structured financial analyses.