Your LLM may be answering the same question twice, just in different words. And every repeated answer can mean another API call, more tokens, and a bigger bill.
What if your AI could recognise the meaning, reuse the answer, and skip the work? That’s where semantic caching comes in.
“The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency.”
— Bill Gates
Semantic caching is a way to save an LLM’s previous answers and reuse them when a new question has the same meaning.
Normally, every time someone asks an LLM a question, the system sends that question to the model, waits for a response, and uses computing resources to generate the answer. If another person asks almost the same question in different words, the LLM may generate the answer again.
Semantic caching avoids this repeated work.
Instead of checking whether the exact words are the same, it checks whether the meaning of the new question is similar enough to a question that has already been answered.
Imagine someone asks:
“How do I reset my password?”
The LLM answers and the system saves that answer.
Later, another person asks:
“I forgot my password. How can I change it?”
The words are different, but the meaning is almost the same.
Semantic caching can recognise this similarity and return the previously saved answer instead of sending the question to the LLM again.
An embedding is a list of numbers that represents the meaning of a piece of text. It helps a computer understand how closely two sentences are related.
For example:
“How do I reset my password?”
“I forgot my password. How can I change it?”
The words are different, but their meaning is similar, so their embeddings will also be close.
Now, semantic caching uses embeddings to compare the meaning of a new question with questions already stored in the cache.
Similar embeddings → Similar meaning → Reuse the saved answer
Different embeddings → Different meaning → Send the question to the LLM
A common way to measure this similarity is cosine similarity, where a score closer to 1 means the two texts are more similar.
They sound alike, but they do different jobs. You can use both.
| Prompt Caching | Exact-Match Cache | Semantic Caching | |
|---|---|---|---|
| What is saved | The start of the prompt | A full answer | A full answer |
| Does the LLM run? | Yes, a fresh answer | No | No |
| Matches on | Same prompt prefix | Same text | Same meaning |
| Main risk | Low | None for wrong matches | Wrong or stale answers |
With prompt caching, the model still generates a new answer. It simply reuses cached parts of the input, which can reduce cost and latency.
Google Cloud’s prompt caching documentation also explains how prompt caching reuses repeated input content.
Let’s use a simple support-bot example. Each request has 7,550 input tokens and 450 output tokens. At Claude Sonnet 5.5 pricing, one request costs around $0.0196.
At 10,000 requests per day, that adds up to about $5,880 per month.
This is where semantic caching for LLMs can make a big difference. When a new question has a similar meaning to a cached question, the system can return the saved response instead of making another LLM API call.
It can also make responses faster. A study showed that a cache hit reduced response time from 2.7 seconds to 0.3 seconds. Semantic caching can also reduce API calls by up to 68.8%.
The actual savings depend on your semantic cache hit rate, how often users ask similar questions, response size, and LLM pricing. The more repeated or similar queries your application receives, the more LLM costs and latency semantic caching can help reduce.
Try it: Work out your own semantic caching savings
Cache hit rate: 38%
Uses 30 days. Add the cost of embeddings and cache storage in the third box. The hit rate is your guess. Measure it before you promise savings.
Semantic caching can save cost and improve speed, but matching by meaning can sometimes return the wrong answer. Here are five common problems:
| Problem | Example | Fix |
|---|---|---|
| Wrong match | “What is Python?” gets an answer about Java. | Raise the similarity threshold and test with real questions. |
| Stale answer | A saved price or policy is no longer up to date. | Use short TTLs for frequently changing information. |
| Shared private answer | One user sees another user's account balance. | Never cache personal answers. Separate caches by customer. |
| Mixed topics | Support and product questions share the same cache. | Separate the cache by topic or use case. |
| No testing | The similarity threshold is chosen without real testing. | Measure how often cached answers are actually correct. |
The risk is real. A poorly tuned cache can produce a false-positive rate as high as 99%, meaning it may confidently return an incorrect answer. Read the false-positive example
For stale answers, use TTL based on how quickly the information changes. Time-sensitive data may need a TTL of 30–300 seconds, while stable content such as documentation can use 3,600 seconds or more.
The similarity threshold is the score two questions must reach for the system to treat them as a match. A typical range is 0.7 to 0.95.
→ Lower threshold: More cache hits, but a higher risk of wrong answers.
→ Higher threshold: Fewer cache hits, but better accuracy and safer responses.
The right threshold depends on your use case, data, and risk level. Test it with real user questions before using it in production.
At Azilen, semantic caching is one of three levers in the token optimization pillar of our APEX framework. The other two are context engineering and model routing. APEX also includes hallucination guards, so caching should never make an AI response less safe.
→ We use semantic caching where questions repeat and the answer is safe to reuse.
→ We test the cache with real user questions before using it in production.
→ We set clear TTL rules so cached answers do not stay longer than needed.
Balance is part of our culture.
Our logo is inspired by orbits, where two forces work together to keep a planet steady. Semantic caching needs the same balance: speed and cost savings on one side, accuracy and safety on the other.
Our ORBIT values of Openness and Trust mean we keep the results clear, so you can understand both the cache hit rate and the error rate.
Do not trust a cache you cannot measure. Track these key semantic caching metrics:
→ Cache hit rate: Track exact matches and semantic matches separately. This shows whether the cache is actually reducing LLM calls.
→ Similarity score spread: Check how close cache hits are to your threshold. Hits that barely cross the threshold may need closer review.
→ Embedding and lookup time: The cache should respond faster than calling the LLM. Otherwise, the performance benefit may be limited.
→ Answer quality: Compare cached answers with fresh LLM responses. Review a sample regularly to catch incorrect matches.
→ Set alerts: Watch for sudden changes in hit rate, unusual similarity scores, or slower lookups. These can indicate changes in user questions, data, or the embedding model.
It also needs the math to work. The LLM bill must be bigger than the cost of embeddings, the vector store and upkeep.
Semantic caching stores previous LLM responses and reuses them when a new question has a similar meaning. This reduces repeated LLM calls, lowers token usage, cuts costs, and can improve response speed.
Azilen uses semantic caching along with context engineering and model routing to reduce unnecessary LLM calls. This approach helps enterprises optimize token usage, control AI costs, improve application performance, and maintain response quality.
Yes. If the similarity threshold is too low, semantic caching may treat different questions as similar and return the wrong response. Testing real user queries and setting a safe threshold helps reduce this risk.
Start by testing different similarity thresholds with real user questions. A higher threshold generally provides safer matches, while a lower threshold increases cache hits. Choose the level that provides the right balance.
Semantic caching can create risks when responses contain personal or sensitive information. Avoid sharing these answers through common caches, separate customer data, and apply appropriate access controls to prevent accidental exposure.
Avoid semantic caching for live data, personalised responses, and tasks requiring a fresh answer every time. It may also be unsuitable when embedding, storage, and maintenance costs are higher than the savings.
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