Originally published on Medium.com on November 6, 2025.
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When “intelligence” wastes cycles​

Most teams building LLM-powered products eventually realize that a large portion of their API costs come not from new insights, but from repeated questions.

A support bot, an internal assistant, or an analytics copilot, all encounter thousands of near-identical queries:

“How do I pass the API key to the local model gateway?”
“Why is the dev database connection timing out?”
“How can I refresh the cache without restarting the service?”

Each of those prompts gets re-tokenized, re-embedded, and re-sent to an LLM even when the model has already answered an equivalent question a minute earlier.

What do we have as a result? Burned tokens, wasted latency, and duplicated reasoning.

Vector databases solved storage, not reuse​

The industry's first instinct was to throw vector databases at the problem. They excel at persistent embeddings and semantic retrieval, but they were never built for reuse. What they lack are TTL policies, eviction strategies, and atomic snapshotting of in-flight state. In other words, they store knowledge, not memory.

Traditional vector databases follow a key:value paradigm: they persist embeddings indefinitely so they can be queried later, much like records in a datastore. A semantic cache, by contrast, treats embeddings as dynamic memory — governed by similarity, expiration, and adaptive retention. Its goal is not to archive information, but to avoid redundant reasoning across millions of semantically similar requests.

With a semantic cache such as VCAL, cached answers can stay valid for days or weeks, depending on data volatility and TTL settings. This moves caching from short-term repetition avoidance to long-horizon semantic reuse where reasoning itself becomes a reusable resource rather than a recurring cost.

In essence, VCAL bridges the gap between data retrieval and cognitive efficiency, turning past computation into future acceleration.

Originally published on Dev.to on October 27, 2025.
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Have you ever wondered why LLM apps get slower and more expensive as they scale, even though 80% of user questions sound pretty similar? That’s exactly what got me thinking recently: why are we constantly asking the model the same thing?

That question led me down the rabbit hole of semantic caching, and eventually to building VCAL (Vector Cache-as-a-Library), an open-source project that helps AI apps remember what they’ve already answered.

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The “Eureka!” Moment​

It started while optimizing an internal support chatbot that ran on top of a local LLM. Logs showed hundreds of near-identical queries:

“How do I request access to the analytics dashboard?”
“Who approves dashboard access for my team?”
“My access to analytics was revoked — how do I get it back?”

Each one triggered a full LLM inference: embedding the query, generating a new answer, and consuming hundreds of tokens even though all three questions meant the same thing.

So I decided to create a simple library that would embed each question, compare it to what was submitted earlier, and if it’s similar enough, return the stored answer instead of generating an LLM response, all this before asking the model.

I wrote a prototype in Rust — for performance and reliability — and designed it as a small vcal-core open-source library that any app could embed.

The first version of VCAL could:

• Store and search vector embeddings in RAM using HNSW graph indexing
• Handle TTL and LRU evictions automatically
• Save snapshots to disk so it could restart fast

Later came VCAL Server, a drop-in HTTP API version for teams that wanted to cache answers across multiple services while deploying it on-prem or in a cloud.