Bud Cache
The enterprise-grade, accuracy-first response cache for large language models — reuse answers across genuinely equivalent requests, on commodity CPUs, cutting inference cost and latency without serving a wrong answer. Built on the Resource Aware Attention family behind Bud Sentinel.
Reuse only what is genuinely equivalent.
When a request arrives, Bud Cache decides — in under a millisecond, on a CPU — whether it has already produced a trustworthy answer to a genuinely equivalent request. If so, that answer returns instantly and the expensive model call is avoided. If not, the request passes through untouched and the new answer is learned for next time.
In the serving path, in front of Layer 04.
Bud Cache sits in front of Bud AI Foundry's serving plane. A hit returns from cache before the model is touched; a miss flows through Foundry unchanged. Either way, the response is governed.
On a hit — the cached answer returns in ~0.2ms median from a single CPU core. The model is never called, and the GPU capacity it would have used serves new work instead.
On a miss — the request passes through to Bud AI Foundry serving unchanged. The model's new answer is cached, so the next genuinely equivalent request is a hit.
Bud Cache is not a stack layer of its own. It sits in the serving path in front of Bud AI Foundry (Layer 04), and is part of the Resource Aware Attention model family alongside Bud Sentinel, the guardrail engine inside Bud SENTRY. Hits return from cache, misses pass through to the model — and SENTRY governance applies either way.
Safety and savings, at once.
Six capability groups — the equivalence decision, the accuracy it delivers, the guarantee behind it, and the freshness and operability that make it deployable, expanded to the specifics an evaluator needs.
How the decision is made.
Sitting in front of your model, Bud Cache reads the parts of a request that actually decide the answer — and reuses one only when two requests are genuinely equivalent. The whole check stays under a millisecond, on CPU.
The decision flow — six steps, every request
Hits and misses alike stay inside the governed serving path — SENTRY policy and audit apply to every response.
Why one similarity score fails
The signals that decide whether an answer transfers are exactly the ones a single embedding distance blurs together. Two requests that differ by one negation or one quantity read as nearly identical to an embedder — and mean different things.
Naive semantic cache — one collapsed score
- Reduces two requests to a single similarity number; above the threshold, reuse — below, don't.
- Flipped negations, changed quantities, and swapped entities all hide inside a high score.
- Every available threshold trades wrong answers for coverage — on the benchmark, no GPTCache setting keeps useful coverage below roughly 35% wrong answers.
- Each lookup runs a transformer embedder: ~57ms per request.
Bud Cache — five signals, one verdict
- Each signal is checked in its own right; an answer is reused only when they agree the requests are equivalent.
- Operates below the whole threshold curve: 79.4% coverage at 1.5% wrong, under a 5% risk ceiling.
- The full decision completes in under a millisecond on a commodity CPU — no transformer in the lookup path.
A missed reuse costs one model call. A wrong reuse costs trust. Bud Cache is biased toward caution by design — the asymmetry is built into the decision, not left to a threshold.
Part of the Resource Aware Attention family
Bud Cache is built on Resource Aware Attention — the CPU-native architecture behind Bud Sentinel, designed against its resource envelope from the start rather than compressed down to it. The same property that lets Sentinel classify at single-digit milliseconds lets Bud Cache judge equivalence at serving speed, on the CPUs the application already runs on.
Run it like infrastructure, not like a model.
The cache tier runs entirely on commodity CPUs — cheap to run, easy to place in front of the serving path, and shipped with the security, multi-tenancy, data-governance, and operability controls an enterprise needs, on by default.
Where it runs
A served hit returns in ~0.2ms on a single CPU core; the equivalence check itself stays under a millisecond. Cheap to run, and easy to place anywhere in the stack.
How it integrates
Hits skip the model; misses pass through unchanged. Standard HTTP cache directives are honoured per request, so existing caching policy carries straight over — and SENTRY governs both paths.
Network defaults
Binds to loopback until access control is configured. Tenant-scoped API keys with a separate admin key compared in constant time; per-caller rate and request-size limits.
Enterprise controls, on by default
Every headline number, with its basis.
All accuracy figures come from the sealed CacheBench held-out set — 400 pairs, held out from any tuning — with GPTCache 0.1.44 measured on the same set. Latency is reported on a single commodity CPU core.
Head-to-head — same benchmark, both systems
Every threshold setting available to a single-similarity cache trades wrong answers for coverage; no GPTCache setting keeps useful coverage below roughly 35% wrong answers. Bud Cache operates below that curve entirely.
| Metric | GPTCache 0.1.44 | Bud Cache | Direction |
|---|---|---|---|
| Decision accuracy | 52.6% | 92.0% | higher is better |
| Wrong-answer ratethe safety metric | 68.2% | 1.5% | lower is better |
| Adversarial wrong-answer rate | 96.3% | 2.4% | lower is better |
| Coverage (recall) | 93.3% | 79.4% | higher is better |
| Lookup latency, per request | ~57ms | 0.2ms | lower is better · ~285× |
Sealed CacheBench held-out set (400 pairs). Latency on a single commodity CPU core; GPTCache's lookup cost is dominated by its transformer embedder. GPTCache's higher coverage is the direct consequence of the reuse behaviour that also produces its 68.2% wrong-answer rate — the two numbers are one trade, not two results.
Against the alternatives an enterprise actually weighs.
Reuse is only the first requirement. Here is how Bud Cache compares with the real options — no cache at all, a generic exact-match key-value store, and a naive semantic cache.
| Capability | No cache | Exact-match KV | Naive semantic (GPTCache) | Bud Cache |
|---|---|---|---|---|
| Reuses paraphrases | No | No | Yes | ✓ Yes |
| Useful hit rate | None | Very low | High only if unsafe | ✓ High (79%) |
| Wrong-answer risk | None | None | High (68%) | ✓ Near zero (1.5%) |
| Statistical safety guarantee | — | — | None | ✓ Bounded false-hit rate |
| Multi-tenant isolation | — | Manual | Not built in | ✓ Strict, built in |
| Auth & rate limiting | — | Yes (mature) | No (library) | ✓ Secure by default |
| GDPR erasure that survives restores | — | Delete only | No | ✓ Yes (tombstoned) |
| Data / log retention age-out | — | TTL only | No | ✓ Configurable |
| Encryption at rest | — | Yes (enterprise) | No | ✓ 0600 + volume/KMS seam |
| Freshness / time-sensitivity | — | No | No | ✓ Built in |
| Standard cache-control (RFC 9111) | — | No | No | ✓ Yes |
| Observability (metrics / health) | — | Yes | Limited | ✓ Prometheus + health |
| Self-tuning | — | n/a | Manual threshold | ✓ Automatic |
| CPU-only, sub-millisecond | — | ✓ Yes | Varies | ✓ Yes (0.2ms) |
Where repeat questions pay for themselves.
Any workload where the same questions recur in different words is a caching workload — provided the cache never serves a wrong answer. Six places that holds.
LLM API platforms & gateways
Absorb redundant traffic across many customers, cutting GPU spend per request — while strict tenant isolation guarantees answers are never mismatched across tenants.
Customer support & FAQ assistants
The same questions recur endlessly in countless phrasings — the ideal high-redundancy, high-value workload for safe reuse.
Enterprise copilots & knowledge bots
Employees ask overlapping questions about policies, code, and docs. Reuse is high, and correctness matters.
Retrieval-augmented (RAG) Q&A
Repeated questions over a stable corpus reuse cleanly — and freshness rules keep answers current when the corpus changes.
Agentic & tool-using systems
Multi-step agents repeat sub-queries and planning steps. Caching them shortens chains and cuts cost per task.
High-traffic consumer chat
Popular prompts and trending questions serve instantly from cache, freeing model capacity for the long tail.
The platform behind the cache.
This brief is the reference for Bud Cache. For the platform-level argument — why serving, governance, and cost control belong on one plane — read the whitepaper, or return to the product overview.
Platform White Paper
The Enterprise AI Management Platform
Where serving economics come from
The platform-level argument — why serving, governance, and cost control belong on one plane, and the economics that follow when a cache, a guardrail, and a gateway share it.
Read the white paperBack to overview
Bud Cache
A cache that understands the question
Return to the high-level product page — the headline numbers and the hit/miss story at a glance.
Back to the product pagePut your data on it.
The fastest way to see what an integrated AI operating system does for your enterprise is a proof-of-concept on your infrastructure, with your data.