Private AI platform

Instead of buying expensive GPU hardware, GPUaaS lets you access powerful GPUs from NVIDIA and Dell instantly, billed hourly, monthly or fractionally.

Yes. Datacom's platform is locally hosted within data centres in Australia and New Zealand, certified compliant with IRAP PROTECTED, NZISM and ISO27001 standards.

Typically within days, often faster. Your Kubernetes and GPU infrastructure can be provisioned almost immediately, not weeks or months.

We fully manage your GPU infrastructure, Kubernetes clusters, compliance and data storage. You focus purely on AI and machine learning innovation.

AI in the cloud refers to the use of artificial intelligence (AI) technologies and services hosted on cloud computing platforms. It allows businesses to leverage AI capabilities like machine learning, natural language processing, and computer vision without needing to invest in expensive on-premises infrastructure or specialised expertise. Cloud AI makes AI more accessible, scalable, and cost-effective for a wider range of organisations.

Cloud platforms provide the compute power, data pipelines, and machine learning frameworks needed to train and deploy AI models. Datacom’s AI-ready platforms support GPU workloads, compliance and data sovereignty.

A private AI platform enables organisations to train and deploy AI models within a secure, sovereign cloud , keeping sensitive data in-country and under your control. Datacom’s hosted private cloud includes GPU nodes, container orchestration, and machine learning tooling.

A private AI platform is a dedicated environment for building, training, and running AI models on infrastructure you control, rather than on shared public AI services. For Australian organisations, that means AI workloads and data can stay in Australian data centres, under Australian law ayour own security and governance policies, instead of being processed offshore. It matters most when AI uses sensitive customer, operational, or citizen data and needs to plug into core systems, where you must be able to explain how data is handled, how models behave, and who is accountable for outcomes.

Datacom’s private AI platform runs in Australian‑based data centres operated by Datacom teams, so AI workloads and data remain within Australian jurisdiction. This supports data residency and sovereignty expectations for government and regulated industries by reducing exposure to foreign laws and unmanaged external services. The platform can be integrated with your existing security, identity, logging, and incident‑response controls, giving you a clearly governed environment for AI that aligns with broader compliance and risk frameworks.

Data platforming support focuses on putting the right data foundations in place so AI projects can succeed at scale. Datacom helps you design and implement storage architectures, pipelines, and governance across object storage, data lakes, and warehouses, then connect those into the private AI platform. This includes data ingestion and transformation, quality and cataloguing, and access controls that align with your security and compliance needs. With consistent, trusted data feeding your models, you reduce time spent on plumbing and increase the likelihood that AI outputs are accurate, explainable, and usable by business teams.

Yes. Datacom’s private AI platform is designed to integrate with the hybrid and multi‑cloud environments most Australian organisations already run. It can connect to on‑premises systems, Datacom private and sovereign cloud, and public cloud services such as Azure and AWS through governed data pipelines and secure network connectivity. Models can consume data from lakes and warehouses, and deploy back into existing applications, APIs, or analytics tools, so AI enhances your current stack rather than sitting in a separate silo.

mix of systems most New Zealand organisations already use: on‑premises databases, Datacom private or sovereign cloud, and public cloud services. Through governed data pipelines and integrations, it can access data from lakes, warehouses, and line‑of‑business systems, then deploy models back into existing applications or APIs. This means you can bring AI to where your data and users already are, instead of creating a separate, hard‑to‑govern island.

A private AI platform is the better choice when AI will work with sensitive or regulated data, when you must meet strict privacy or sovereignty expectations, or when you need predictable performance and cost for GPU‑intensive workloads. It is also appropriate when AI will be embedded deeply into critical systems in sectors like government, healthcare, finance, and critical infrastructure, where you need clear control over where data is processed and how models are governed. Public AI services can still be useful for low‑risk experiments, but a private platform gives you a controlled foundation for long‑term, strategically important AI programs.

Object storage is a highly scalable, cost‑effective way to store large volumes of raw, often unstructured data such as images, documents, logs, and other AI inputs. Data lakes typically sit on top of object storage and hold raw structured and unstructured data together, applying structure when data is read, which is useful for exploration and training models across broad datasets. Data warehouses store cleaned, structured data in predefined schemas optimised for fast queries and reporting, making them ideal for production analytics and business insight on AI outputs. Datacom helps you use these layers together so your private AI platform has flexible storage for experimentation and governed stores for operational AI and decision‑making.