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What Is Semantic Caching?
Semantic caching stores and reuses responses based on the meaning of a request, not its exact text. If a new question means the same thing as one already answered, the cache returns the saved answer instead of computing a new one.
For example, "How do I reset my password?" and "I forgot my password, how can I change it?" are different strings but are asking the same question. A semantic cache treats them as one.
How does semantic caching work?
A semantic cache converts each request into a numeric representation of its meaning, then checks whether a close-enough match is already stored.
Embed the request. An embedding model turns the prompt into a vector, a list of numbers that captures its meaning. Similar meanings produce vectors that sit close together.
Search for a match. The cache runs a similarity search (such as cosine similarity) across vectors of previously answered prompts.
Apply a threshold. If the closest match scores above a set similarity threshold, it counts as a cache hit.
Return or generate. On a hit, the stored response is returned immediately. On a miss, the request goes to the LLM, and the new prompt, vector and response are stored for future use.
The similarity threshold is the key tuning knob. A strict threshold returns fewer but safer hits; a loose one returns more hits but risks serving an answer to a question that was subtly different. Most implementations also apply a time-to-live (TTL) so stale answers expire.
How is semantic caching different from traditional caching?
Traditional caching matches on an exact key, usually the URL or request string. Semantic caching matches on similarity of meaning, so it can reuse an answer across differently worded requests.
Traditional caching | Semantic caching | |
Match logic | Exact key match (URL, headers, query string) | Similarity of meaning between embeddings |
Hit condition | Identical request | Similar enough to pass a threshold |
Best for | Static assets, repeatable API responses | Natural-language queries, LLM prompts |
Hit rate on language input | Low, since small wording changes cause misses | Higher, since paraphrases still match |
Lookup cost | Hash lookup, near-instant | Embedding plus vector search, slightly more |
Main risk | Serving stale content | Serving a near-match that is not truly the same question |
The two are complementary. Traditional caching remains the right tool for images, scripts and deterministic responses; semantic caching covers the open-ended, free-text traffic that exact matching almost never hits.
Why does semantic caching matter for LLM and AI applications?
LLM calls are slow and expensive compared with typical web requests, and real-world users ask the same things in many different ways. Exact-match caching misses nearly all of that repetition.
Three characteristics of AI workloads make semantic caching a good fit:
Repetitive intent. Support bots, internal assistants, search and FAQ-style apps see the same questions phrased many ways.
Costly, slow inference. Each model call consumes GPU time and is typically billed per token, and responses can take seconds to generate.
Rate limits and scale. Provider rate limits and unpredictable traffic spikes make it valuable to answer a share of requests without calling the model at all.
Semantic caching suits queries with stable answers, such as documentation questions or product information. It is a poor fit for responses that must be personalized, real-time, or unique every time. Teams usually scope it to those safe cases, and key the cache by tenant, model or system prompt so answers are not shared where they should not be.
What are the benefits for latency, compute and cost?
Every cache hit is a request the model never has to process. That shows up in three places:
Latency. A hit returns in the time of an embedding and a vector lookup, typically milliseconds, instead of the seconds an LLM may take to generate a full response. Users see faster, more consistent answers.
Compute. Fewer inference calls means less GPU and API load, which frees capacity for requests that truly need fresh generation and reduces exposure to provider rate limits.
Cost. LLM usage is usually billed per token. Each hit avoids both input and output tokens, so savings scale directly with your hit rate.
How much you save depends on how repetitive your traffic is and how you set the similarity threshold. Measure your own hit rate and answer quality before projecting savings. Cache hits also add a small benefit for sustainability, since fewer model calls means less energy used per request.
How does semantic caching work at the edge?
At the edge, the semantic cache runs on a distributed network of servers close to users, in front of your LLM provider or model endpoint, rather than inside your application or in a single central region.
The flow looks like this:
A user's prompt reaches the nearest edge location.
Edge logic generates an embedding and searches the cache for a semantically similar prompt.
On a hit, the edge returns the stored response directly. The request never travels to the origin or the model.
On a miss, the edge forwards the request to the LLM, returns the answer to the user, and stores it for later matches.
Running this at the edge adds three advantages over a centralized cache:
Shorter distance. Hits are served from a location near the user, removing the round trip to a distant model or region.
Shared benefit. One user's question can warm the cache for others, and edge platforms can keep hot entries close to where demand is.
A single control point. Because the edge already sits in front of your traffic, you can apply caching, rate limiting, security and observability in one place, often without changing application code beyond pointing at a new endpoint.
How Fastly can help
Fastly's AI Accelerator applies this approach, using semantic caching on the Fastly edge platform to reduce calls to LLM providers. By bringing semantic caching to the edge, teams can cut LLM response times and API costs without rebuilding their applications. It sits between your app and your LLM provider, serves repeat and similar prompts from the Fastly edge, and sends only genuinely new requests on to the model.
Because it runs on the same platform that already delivers, secures and observes your traffic, you get caching, security and visibility in one place. To see how it fits your AI workloads, talk to a Fastly expert.