[01.00] PREPARED FOR DAVID MURACO · RAPIDAI

The same edge-to-cloud, a fraction of the footprint.

You already run ~600 edge Kubernetes clusters in hospitals and 25 EKS clusters on AWS.[1] Unikraft slots in as a virtual kubelet, no rearchitecting, and gives every workload VM-level isolation with 10ms cold boots.

Talk to an engineer Relevant use cases
~/rapidai — unikraft ● live
$ unikraft run --metro=fra \ --image=stroke-inference:latest \ -p 443:8080/http+tls \ --scale-to-zero policy=on,cooldown-time=300 ✓ instance live, cold-start 9ms $ unikraft instances list --metro fra ✓ stroke-inference running scale-to-zero: on # GPU stays put. CPU layer flexes.
01 · LOWER COST ~50% vs traditional cloud, more on-prem
02 · AUTOSCALE <10ms instant scale-to-zero, no idle spend
03 · DROP-IN 0× rearchitecting via virtual kubelet
[02.00] WHERE IT FITS RAPIDAI

Three use cases mapped to your stack.

01 / 03

Bursty cloud overflow on EKS

Your architecture already pushes resource-intensive tasks to the cloud during spikes, and stroke alerts are inherently spiky and time-critical.

Millisecond autoscale handles the spike, then scales to zero between alerts, so no warm pools sitting idle. Slots into your 25 EKS clusters on AWS[3] as a virtual kubelet.

Maps to
  • 25 Amazon EKS clusters
  • Dynamic cloud burst processing
  • Time-sensitive stroke workflows
  • Pay-for-idle elimination
02 / 03

Hardware isolation for multi-tenant + 3rd-party algorithms

You run vetted third-party algorithms and multi-hospital PHI on shared infrastructure, under HIPAA, ISO 27001/27701, and SOC 2 Type 1.[4]

Every instance gets VM-level isolation, not container-level. So you can pack more partner algorithms onto the same nodes without weakening tenant boundaries, a security win, not just a cost one.

Maps to
  • Third-party algorithm hosting
  • PHI kept local + isolated
  • HIPAA / ISO 27001 / SOC 2 Type 1
  • Encryption + tenant isolation
03 / 03

Less hardware needed on-prem

You ship physical compute into 600+ hospitals,[1] and your own stated goal is "minimal on-premises infrastructure."[2]

GPU inference stays put. But the CPU layer around it, DICOM ingest, preprocessing, orchestration, routing, can run far denser per box, so fewer and smaller machines to ship and service.

Maps to
  • Rapid Edge Cloud on-prem nodes
  • NCCT processing at the edge
  • Minimal-hardware mandate
  • Fewer boxes to service
Runs anywhere

Runs within AWS, GCP, Azure & on-prem, the same workload across all of them.

Secure

Unikraft is SOC 2 Type 2 & HIPAA certified, with hardware-level VM isolation per instance.

Drop-in

Deploying is simple, we support Dockerfiles natively with full drop-in Kubernetes support (EKS, GKE, ...).

[03.00] THE BENEFITS

Unprecedented efficiency & scalability

Run faster, scale smarter, don't pay for idle.

[04.00] SEE IT IN ACTION

How the integration works

[05.00] NEXT STEP

Worth a 20-minute look at your edge tier?

No switching cloud providers, no rearchitecting. We can model the footprint reduction against your current 600-cluster setup.

Talk to an engineer How it works
[06.00] SOURCES
  1. ~600 edge clusters + 25 Amazon EKS clusters. spectrocloud.com — RapidAI case study
  2. Hybrid edge-to-cloud, minimal on-premises infrastructure. rapidai.com/enterprise-infrastructure
  3. AWS as cloud backbone. rapidai.com — RapidAI & AWS press release
  4. HIPAA, ISO 27001/27701, SOC 2 Type 1 certified. rapidai.com — Security & Compliance one-pager (PDF)