Introducing

Bud Sentinel

AI guardrails powered by Resource Aware Attention

State-of-the-art accuracy. Runs on any CPU. Performance that surpasses SOTA models running on $15,000 GPU servers.

CPU-Native SOTA Accuracy Zero GPU Cost

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Why Sentinel?

Today's GenAI is
built for GPUs.

On the commodity hardware most organizations actually have, today's GenAI systems struggle to perform. This often forces teams to run guardrail systems on GPUs, turning what should be lightweight safeguards into an unexpectedly expensive part of the stack.

Costly

Hard to Scale

Inaccessible

⬡ GPU

GPUs are fast but expensive. CPUs are affordable but unusably slow.

CPU · 16 Workers

~5s

Latency at 8,000 input tokens
Affordable hardware - unusable speed

GPU · A100 ($15K)

~300ms

Fast enough - but the guardrail costs
as much as the language model itself

The Optimization Wall

You can optimize the runtime.
But returns diminish.

No amount of tuning compensates for an architecture designed for GPUs.

The Solution

Resource Aware Attention

To truly democratise GenAI, you have to commoditise it. That requires rethinking the model architecture itself, not optimising for GPUs, but building for the hardware most organisations actually have.

Resource Aware Attention is designed from the ground up for CPUs, maximising their strengths while maintaining model-level accuracy. The result is a fundamentally more efficient way to run GenAI — without the cost and dependency of specialised infrastructure.

First Application

We built Sentinel with
Resource Aware Attention

Because guardrails are non-negotiable in any serious GenAI deployment, they sit on the critical path of every request.

User Request

Authentication

⛨ Input Guardrail

Language Model

⛨ Output Guardrail

Response

Accuracy

More Accurate Than Leading
Guardrail Systems

Low attack success rate and low false refusal rate. Every other model trades one for the other.

Performance

Faster on any CPU
than competitors on a $15K GPU.

Every competing guardrail was tested on an NVIDIA A100. Sentinel was tested on a laptop. Sentinel won.

Sentinel on a laptop vs. everyone else on an A100

Per-classification latency · 512 tokens · Sentinel includes gRPC network overhead

Bud Sentinel

i7 Laptop · CPU

8.39ms

Prompt Guard 2

A100 · $15K GPU

18.52ms

ArchGuard

A100 · $15K GPU

19.07ms

PIGuard

A100 · $15K GPU

19.00ms

↑ 2.3x faster on a laptop CPU than competitors on a $15,000 GPU

Same CPU hardware. Completely different architecture.

Per-classification latency on Intel Xeon 8272CL · 512 tokens

Bud Sentinel

Xeon 8272CL

5.99ms

Prompt Guard 2

Xeon 8272CL

334ms

56x slower

ArchGuard

Xeon 8272CL

380ms

63x slower

Prompt Guard

Xeon 8272CL

402ms

67x slower

↑ 56-67x faster on the exact same hardware

Production-grade throughput

Sentinel handles enterprise traffic on commodity CPUs alone - no GPUs in the loop.

Xeon 8592+

256 vCPU · 8K Tokens

1,432 req/s

p50: 0.70ms

16K tokens761 req/s

65K tokens89 req/s

Xeon 8272CL

16 vCPU · 512 Tokens

2,749 req/s

p50: 25.17ms

8K tokens508 req/s

65K tokens101 req/s

EPYC 9V74

16 vCPU · 8K Tokens

57 req/s

p50: 17.60ms

16K tokens29 req/s

65K tokens6 req/s

The bottom line

Sentinel redefines what hardware you need for guardrails.

Bud Sentinel

Any laptop CPU

CPU

8.39 ms

512 tokens · incl. network overhead · no GPU

Leading Guardrails

NVIDIA A100 · $15,000

GPU

~18.9 ms

512 tokens only · max seq 512 · needs 16 parallel for 8K

Bud Sentinel

Xeon 8592+ · Server

Server

0.70 ms

8K tokens · 5,000 concurrent · 1,412 req/s

Leading Guardrails

Same CPU · No GPU

CPU

~845 ms

512 tokens only · 100x slower on identical hardware

Requires a $15K GPU to reach 18ms

Ubiquitous Safety

Deploy guardrails
everywhere.

Guardrails stop being a cost center. They become infrastructure.

📱

Edge Devices

Phones, IoT, embedded

🤖

Every Agent

Per-action at zero cost

☁

Every Cloud

Multi-cloud, on-prem, hybrid

What's Next

Sentinel is just
the beginning.

We're rebuilding the entire GenAI stack with Resource Aware Attention.

Guardrails - Sentinel Live

Embeddings Next

Rerankers Next

Routing Planned

Compression Planned

Caching Planned

Redesign the foundation.
Redefine what's possible.

Not by waiting for cheaper hardware - but by building an architecture that works with what already exists.

Bud Sentinel

Today's GenAI is
built for GPUs.

You can optimize the runtime.
But returns diminish.

Resource Aware Attention

We built Sentinel with
Resource Aware Attention

More Accurate Than Leading
Guardrail Systems

Faster on any CPU
than competitors on a $15K GPU.

Deploy guardrails
everywhere.

Sentinel is just
the beginning.

Redesign the foundation.
Redefine what's possible.

Company

Product

Resources

Bud Sentinel

Today's GenAI isbuilt for GPUs.

You can optimize the runtime.But returns diminish.

Resource Aware Attention

We built Sentinel withResource Aware Attention

More Accurate Than LeadingGuardrail Systems

Faster on any CPUthan competitors on a $15K GPU.

Deploy guardrailseverywhere.

Sentinel is justthe beginning.

Redesign the foundation.Redefine what's possible.

Company

Product

Resources

Today's GenAI is
built for GPUs.

You can optimize the runtime.
But returns diminish.

We built Sentinel with
Resource Aware Attention

More Accurate Than Leading
Guardrail Systems

Faster on any CPU
than competitors on a $15K GPU.

Deploy guardrails
everywhere.

Sentinel is just
the beginning.

Redesign the foundation.
Redefine what's possible.