Bud Cache · Resource Aware Attention · Semantic Caching

Stop paying to compute the same answer twice.

Production LLM traffic re-asks the same questions in different words — and a string-keyed cache misses every one of them. Bud Cache decides, in under a millisecond on a CPU, whether a genuinely equivalent request has already been answered. Hits return at cache speed. Misses pass through untouched.

Bud Cache Product Brief Bud AI OS Whitepaper

Overview

A cache that understands the question.

Bud Cache is the accuracy-first response cache for large language models, built on the same Resource Aware Attention family as Bud Sentinel. It reuses answers only across genuinely equivalent requests — cutting inference cost and latency without serving a wrong answer.

92% decision accuracy 1.5% wrong-answer rate 79% useful hit rate 0.2ms median hit latency

Measured on the sealed CacheBench held-out set (400 pairs); latency on a single commodity CPU core. On the same benchmark, GPTCache 0.1.44 scores 52.6% accuracy with a 68.2% wrong-answer rate.

08Bud Agentaugment

07Bud Studioconsume

06Bud SENTRYgovern

05Bud MCP Foundryintegrate

Bud Cache in the serving path 04Bud AI Foundryserve

03Bud Model Foundrytrain

02Bud Podexperiment

01Bud LayerZerohardware

01 · Where it sits

Bud Cache

Serving path · in front of Layer 04 · Resource Aware Attention

The accuracy-first response cache for LLMs — reuse answers only across genuinely equivalent requests.

Judges equivalence, not similarity — five signals, one decision
Resource Aware Attention family — alongside Bud Sentinel · CPU-only, no GPU in the cache tier
Hits return from cache; misses pass through untouched — and are learned
SENTRY governance applies either way

02 · Hit at cache speed. Miss without a penalty.

requests

BUD CACHECPU-only

refunds · 30-day policy

no match — pass through

HIT

MODEL

~0ms

model call ~1,500ms

“Can I get a refund after 30 days?”

“Is a refund possible past the 30-day mark?”

“Can I get a refund within 30 days?”

answer

learned

entities quantities constraints negation intent

~0.2ms median · ≈1ms p99 — the model lane never lights

First request — a miss. It travels the long lane to the model. The new answer is learned for next time.

Same question, different words. Five signals checked — all agree. The cached answer flashes back near-instantly.

Looks alike — isn't. One signal disagrees; the request passes through untouched. A missed reuse costs one model call. A wrong reuse costs trust — biased toward caution by design.

92% decision accuracy 1.5% wrong-answer rate 79% useful hit rate 0.2ms median hit sealed CacheBench · 400 pairs · single CPU core

03 · Inside the cache

01 / 06

Equivalence, not similarity

5 signals · 1 decision

sealed CacheBench · 400 pairs · single CPU core · GPTCache 0.1.44

The result

Safety or savings.

Safety and savings. At once.

Value proposition

Safety or savings becomes safety and savings.

Today's caches make you pick a side: exact-match caches almost never hit, and single-score semantic caches serve confidently wrong answers at production volume. Bud Cache reuses an answer only when five signals agree the request is genuinely equivalent — and when they disagree, it lets the request through. A missed reuse costs one model call; a wrong reuse costs trust.

Decision accuracy92%

Wrong answers1.5%

Useful hit rate79.4%

Median hit latency0.2ms

sealed CacheBench held-out set · single CPU core · methodology in the product brief

Key features

A cache that earns the reuse.

Single-score caches can't tell “looks alike” from “means the same.” These six are why Bud Cache can — and why it never has to choose between accuracy and speed.

Equivalence, not similarity

Entities, quantities, constraints, negation, and intent are checked in their own right — an answer is reused only when every signal agrees.

5 signalsone decision · reuse only on agreement

Near-zero wrong answers

Coverage is traded away deliberately — 2.4% wrong under adversarial paraphrases, where GPTCache 0.1.44 reaches 96.3% on the same benchmark.

1.5% wrongvs 68.2% for GPTCache · sealed set

Sub-millisecond, CPU-only

~285× faster per lookup than GPTCache and ~7,500× faster than a model call — with no GPU anywhere in the cache tier.

0.2ms median≈1ms p99 · single commodity CPU core

A safety bound you set

The wrong-answer rate is held under a target you choose — a mathematical PAC bound, not a hopeful threshold. When signals disagree, the request passes through.

5% risk ceilingPAC-bounded · 79% coverage held under it

Self-tuning, zero-config

Calibrates to each workload automatically — one Lenient / Balanced / Strict knob, and no ML team required to operate it.

0 thresholds to tuneLenient · Balanced · Strict

Never stale by design

RFC 9111 native — time-sensitive questions get short lifetimes and never cross a day boundary, honouring no-store, no-cache, max-age, and min-fresh.

3 TTL scopesper request · per domain · global

Go deeper

The full story, in depth.

Every headline number paired with how it was measured, the full comparison matrix, and the enterprise control list.

Get started with Bud

Put 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.

Request a POC Talk to the team

01 Identify a use case where complexity, cost, or governance is a known pain point.

02 Joint discovery — Bud maps your AI pain points to platform capabilities.

03 POC in days, on your hardware, with your data.

Stop paying to compute the same answer twice.

A cache that understands the question.

Bud Cache

Equivalence, not similarity

Safety or savings becomes safety and savings.

A cache that earns the reuse.

Equivalence, not similarity

Near-zero wrong answers

Sub-millisecond, CPU-only

A safety bound you set

Self-tuning, zero-config

Never stale by design

The full story, in depth.

Bud Cache Product Brief

The Enterprise AI Management Platform

Put your data on it.