The AI Caching Layer Every Solutions Architect Must Master
Disclaimer: I work at Redis, and Semantic Caching is a top feature of our latest software. The opinions expressed here belong to me and do not reflect the views of my employer
AI is showing up in almost every architecture conversation right now. As an architect, it is exciting because it means being right at the forefront of companies embracing the AI revolution. The first principle of AI aligns closely with the motivation and personality of architects: we love to problem solve, and AI promises to solve problems. But as with anything new and shiny, especially when implemented quickly, troubling patterns emerge in these systems: they’re slow, the costs are unpredictable, and they repeatedly burn expensive compute cycles to answer the same questions. For the first AI prototype or demo, this might not be an issue, but after a few production deployments, cost cycles, and customer feedback come in, these pain points will get the spotlight.
A few weeks back, I joined Redis, the company that is synonymous with caching as Google is to search, and during my onboarding and training, I encountered a term that has stuck with me throughout my tenure: semantic caching. At a conceptual level, semantic caching is similar to the typical caching pattern, saving calls to the database by storing “hot” data in memory, but instead of storing raw data, it stores the meaning of LLM responses, all without having to use subsequent LLM calls. In traditional caching, we focused on saving network and database costs; with semantic caching, the focus shifts to saving tokens. If you are unfamiliar with tokens, check this out.
Tokens are the main currency in AI operations, and using fewer of them while maintaining the same impact can be a huge win. This is why mastering semantic caching has become a non-negotiable skill for every Solutions Architect. In AI system design, semantic caching is quickly becoming what key-based caching was to classic system design.
The Architectural Blueprint
At a high level, semantic caching uses vector embeddings to match intent. But as architects, we need to know how the components fit together.
Here’s the blueprint:
Step 1: Prompt Received - The process begins when the App receives a Prompt from the User.
Step 2: Level 1 Cache Check (Exact-Match) - Before any complex processing, the App performs a fast, key-value lookup in Redis using the raw prompt text. If there's an exact match (Yes), the cached response is returned immediately, and the process is done. This is the most efficient path. If there is No match, the flow continues.
Step 3 & 4: Level 2 Cache Check (Semantic Search) - On a Level 1 cache miss, the App sends the Prompt to an Embedding Model (Step 3). The model converts the prompt's meaning into a Vector, which is then sent to Redis to perform a vector similarity search (Step 4).
Step 5a: Semantic Cache Hit - If the vector search finds a stored response with a high enough similarity score, it’s a Cache Hit. The response is sent back to the App and then to the user. The process is complete without ever calling the expensive LLM.
Step 5b & 6: Level 3 Fallback (LLM) - If the vector search also fails to find a suitable match, it’s a Cache Miss (Step 5b). The system now falls back to the LLM. The App sends the necessary data (the prompt or its Vector) to the LLM for processing (Step 6).
Step 7 & 8: Response and Cache Population - The LLM generates a new Response and sends it to the user via the App (Step 7). To make the system smarter for the future, the App then stores the new vector + response pair back into Redis (Step 8), ensuring a similar query next time will result in a fast, semantic cache hit.
Quick plug, this is all possible in a single Redis instance, out of the box. There are no extra costs or special features required to use both key-value and semantic search in the same instance, continuing the focus on simplicity. This multi-layer approach is the most efficient pattern, ensuring you use the cheapest possible resource at every stage. It demonstrates a nuanced understanding of performance and cost optimization that goes beyond a basic implementation.
The Logic in an Example
Let’s walk through an example that highlights how and why this can be useful.
Imagine a company has an internal chatbot that employees can use to ask questions based on the company’s internal wiki and documentation. This kind of bot is increasingly common, whether it’s for HR policies, onboarding guides, or product specs. And importantly, it operates within a fixed knowledge base, not the open internet.
That’s a big deal: when you know the information space is bounded, semantic repetition is practically guaranteed.
For example:
On Monday, someone asks:
“How many vacation days do we get?”
▸ Latency: ~2.8 seconds
▸ Tokens (input + output): ~350
▸ Cost: ~$0.01 (assuming GPT-4)On Tuesday, another person asks:
“What’s our PTO policy?”
▸ Latency: ~90 ms (cache hit)
▸ Tokens: 0 additional tokens
▸ Cost: $0.00 (served from cache)Later that week:
“How much paid time off do employees get per year?”
▸ Latency: ~90 ms (cache hit)
▸ Tokens: 0 additional tokens
▸ Cost: $0.00 (served from cache)
To a traditional cache, those are three completely different strings. But a semantic cache recognizes that they’re all asking the same thing, and returns the same answer.
Let’s say the first user triggers an LLM call and gets the following response:
“Full-time employees receive 20 days of paid time off (PTO) per calendar year. This includes vacation, personal time, and sick days. PTO does not roll over, so it’s encouraged to use it within the year. You can submit time-off requests via the HR portal.”
Thanks to semantic caching, that same answer would now be served instantly to the other two users, even though they asked in totally different ways.
This kind of reuse isn't just about speed. It saves money, avoids redundant processing, and keeps answers consistent. And when you’re dealing with hundreds or thousands of these queries daily, the impact compounds quickly.
Why It Matters Now: Cost, Latency, and Predictability
LLMs aren’t cheap. A semantic cache can drastically cut costs, but the business impact is even bigger.
It’s not just about saving money on tokens; it's about cost predictability. Semantic caching is one of the most powerful tools we have for the emerging discipline of AI FinOps.
When business leaders ask, "What will our AI feature cost at scale?" a system without caching has a terrifyingly variable answer. A system with a 70% semantic cache hit rate, however, has a much more stable and predictable cost model. It allows you to calculate the unit economics of your AI features with actual confidence. In a world where CFOs are getting nervous about runaway AI bills, being able to provide that predictability is a superpower.
For a visual example, I took the cost of an LLM serving 1M requests a month, averaging 500 tokens each, with GPT-4o pricing. I used the monthly price of a B10 instance on Azure Managed Redis (which can comfortably handle this load), then modeled how increasing the cache hit rate reduces token costs. Even at a 50% cache hit rate, you are already saving 37% on spend, even after factoring in the Redis instance.
Cost savings isn’t the only improvement. Latency improves dramatically. Instead of waiting 3–10 seconds for a streamed response from an LLM, semantic cache hits can respond in under 100ms. You could serve 100 cached requests in less time than a single worst-case LLM response. It’s an instance where both the customer and the business benefit.
From Experience
In my work with Redis, I've seen how powerful this becomes when you can implement it efficiently. For instance, using a multi-model database lets you combine your semantic cache, application cache, and session store in a single, high-performance system. This avoids architectural sprawl and reduces TCO, a conversation every architect should be ready to have.
In Closing
If you’re a Solutions Architect, this is going to come up. But the conversation is already evolving. Today, we're talking about caching LLM text responses. Tomorrow, we'll be applying the same semantic principles to multi-modal inputs: caching the results of image generation prompts, recognizing similar audio commands, and detecting duplicate items in product image catalogs.
Semantic caching isn't just a fix for expensive LLMs; it's a fundamental pattern for building efficient, real-time retrieval and recognition systems across domains. It's becoming the baseline for performant AI. And like all fundamentals, mastering it now will prepare you for whatever comes next.