Two ways to run an agentic AI platform. One owns the hardware, the memory, the storage, and the network. The other rents all four from a multi-tenant vendor and reaches them over the Public Internet. The architectures are not comparable — and the spec sheets prove it.
Same agentic workload — answer a customer call, look up the CRM, book the meeting, send the email. Two completely different stacks underneath.
Compute, memory, storage, network, security, sovereignty, cost. Every layer of an AI platform measured against its real-world counterpart.
| Layer | SARAH AI Suite (NVIDIA DGX GB300) | OpenClaw / Hermes on a Public-Cloud VPS |
|---|---|---|
| GPU silicon | 72× NVIDIA Blackwell Ultra · GB300 full rack · Light Matter chips & switches | 1× shared instance GPU · whatever the cloud vendor schedules you |
| VRAM (total) | 20 TB HBM3e · single coherent pool | 16–80 GB on the instance · ends at the box boundary |
| VRAM (per call) | 3 GB dedicated · isolated to that conversation · zero contention | No per-call allocation · whatever the runtime scrapes from a shared pool |
| Memory bandwidth | 576 TB/s aggregate | ~2–3 TB/s peak per GPU · degrades under noisy-neighbour load |
| Model storage | Local NVMe · ~670 GB Deep Thinker + ~244 GB Doer · loaded once, served forever | Cloud block storage or HuggingFace pull at boot · re-downloaded on instance restart |
| Per-call working memory | 128K-token context window held in dedicated VRAM for the life of the call | Context window survives only as long as the shared GPU lets it |
| Backbone network | 4 TB/E Layer-2 fibre · Private Enterprise IP Network · physical interconnect | Shared cloud-vendor fabric · TCP over the open internet for anything external |
| Public-internet exposure | None. The platform is unreachable from the open web by design. | Public IPs · open ports · part of the cloud-vendor's blast radius |
| External-vendor reach | Direct peering with Google Cloud, AWS, Azure, Cloudflare · private interconnect, no public hop | Public-internet egress to every service, even same-cloud APIs unless you build VPC peering yourself |
| Inference latency | Sub-400 ms first-word · streaming TTS · parallel sentence synthesis | Variable: cold-start + queue + cloud-network hops + shared GPU contention |
| Tenant model | Single-tenant · the silicon is physically yours | Multi-tenant · your conversation shares hardware with arbitrary strangers |
| Data sovereignty | 100% on your premises (or our PEIPN) · data never crosses borders unless you say so | Vendor terms govern what they do with your prompts and outputs |
| Cost model | Buy once, own forever · zero per-token meter · zero per-block charge | Per-token, per-second-GPU, per-egress-GB · the meter never stops |
| Vendor lock-in | None. The hardware and the software are yours; open-source LLMs fine-tuned in-house. | Cloud vendor + framework vendor + occasional model vendor — three locks per workflow |
| Failure domain | A single rack you can see · 394 restore points · 200 kW EMG off-grid power | A region in someone else's data centre. Their outage is your outage. |
| Compliance posture | SOC 2 / ISO 27001 / GDPR / CCPA / HIPAA / PCI DSS · examiner-ready audit trail | Inherits cloud-vendor SOC 2 + your own scaffolding · audit trail you have to build |
Memory bandwidth is the silent variable that decides how many concurrent conversations a platform can sustain. The honest comparison, scaled.
Bars scaled to the GB300's 576 TB/s. Cloud-rented GPUs are also typically shared and oversubscribed, so real-world throughput is lower than peak.
The internet was built to connect strangers. Your AI platform should be built to connect you to your dependencies — at line rate, on physical fibre, with no shared pipe in the middle.
SARAH AI Suite's Private Enterprise IP Network terminates directly into the four interconnect fabrics that run most of the world's cloud workloads. When SARAH needs to read a Google Sheet, post to an S3 bucket, hit an Azure Cognitive endpoint, or push through Cloudflare — none of those packets touch the open internet. They ride a private cross-connect.
Every client site runs in its own VLAN on the PEIPN. The physical fibre is shared with our other clients, but the Layer-2 boundary is yours alone — no broadcast, no ARP visibility, no inter-tenant traffic ever lands on your interface. Your private network ends at your premises, full stop.
The OpenClaw / Hermes VPS comparison: a public IP, a TCP egress over a shared cloud fabric, a Public-Internet hop to every external dependency, and a full attack surface that the public web can probe at will. Same workload. Two universes of risk.
An open-source agent framework on a rented GPU is "free" the way a treadmill at a gym is free — you pay for everything attached to it. SARAH AI Suite does not have a meter to attach.
| Cost item | SARAH AI Suite | OpenClaw / Hermes on a VPS |
|---|---|---|
| GPU instance time | Included · the silicon is yours | Per-second meter · 24/7 to keep the agent warm |
| Token throughput | No per-token meter · run it as hard as the silicon will go | Per-token bill if you use a hosted LLM behind the framework |
| Egress bandwidth | Direct peering · effectively flat-rate inside the PEIPN | Per-GB egress meter to every external destination |
| Storage I/O | Local NVMe · no IOPS bill | Per-GB-month + per-IOPS on cloud block storage |
| Idle cost | Zero. Idle silicon is silicon you already own. | The VPS is billing the moment you spin it up — even at 3am with nobody calling |
| Year-3 cost trajectory | Maintenance only ($300K/yr Enterprise · $3M/yr DC) | Same line items, same meters, three more years of inflation |
OpenClaw and Hermes are good open-source agent frameworks. Run on a public-cloud VPS, they will get you a demo. They will not get you an enterprise. Once the conversation matters, the architecture decides everything — and a sovereign, single-tenant, GB300-class platform on a 4 TB/E private fibre network is a different category of system than a multi-tenant agent on a rented GPU.
Schedule a 30-minute architecture review with the Australian engineers who built SARAH AI Suite. We'll map your existing agent stack against the GB300 reference and tell you, honestly, whether you'd be better off with a sovereign deployment.