Capacity placed where your workload needs it
Placement and latency, keeping data inside a jurisdiction, how nodes are monitored around the clock, and the security model the platform is architected around.
Inference moved the problem closer to the user
Training rewards one enormous cluster. Serving does not. Inference workloads are latency-sensitive and geographically spread, which makes distance from the user a cost rather than a detail.
Distance is measurable
Every kilometre between the request and the processor shows up in response time. Distributed placement shortens that path structurally.
Data that cannot leave
GDPR, HIPAA and defence procurement increasingly require compute to run physically inside a given jurisdiction. Placement becomes a compliance control.
Capacity without the queue
Because nodes attach to energised services, time-to-capacity is not tied to an interconnection application.
Is your workload actually a fit?
Three questions decide it, and they are the same three the team will ask you. Working through them yourself saves a call.
What is the workload?
Training or inference, and roughly what scale. Training rewards one very large co-located cluster. Inference is latency-sensitive and geographically spread, which is where a distributed footprint earns its place.
Where must it physically run?
Is a location a preference or a legal obligation? If a regulator requires processing inside a jurisdiction, placement stops being an optimisation and becomes a control you have to exercise.
When do you need it?
Because nodes attach to service that is already energised, time-to-capacity is not gated by an interconnection application — the step that dominates a conventional build.
When DAIT is the wrong choice
Worth saying plainly. Stretching to fit wastes your time and ours, and these are the cases where something else serves you better.
- Frontier-scale trainingVery large training runs genuinely want one enormous co-located cluster with high-bandwidth interconnect between every node. That is not what a distributed footprint is for.
- Latency and location do not matterIf the workload has no placement sensitivity and no residency obligation, commodity cloud is usually cheaper and you should use it.
- You need capacity in production todayDAIT is pre-pilot with no nodes in commercial operation. If you have a live workload that must land this quarter, we are not the right conversation yet.
Monitoring and incident response are covered on the Operations page, and the hardware and security architecture on Technology.
Specifications and technical overview
Technical overview
Node specifications, tier configurations and the operating model, as a single document.
Coming soon
Platform walkthrough
A recorded tour of the monitoring stack and how workloads are placed and observed.
Coming soon
Detailed performance, throughput and commercial figures are shared under NDA. Request them here.
Tell us what you need to run
Workload type, where it has to sit, and roughly how much capacity. We will come back on placement and timing.
Talk about capacity