On-premises AI solutions in 2026: six approaches compared

Which AI platform fits your data, users, and existing systems? WZ-IT provides AI Cube Pro as a preconfigured local starting point and designs custom GPU servers, RAG pipelines, and managed operations for larger requirements. Assess the operating model together

Organisations searching for an on-premises AI solution often compare products that do not perform the same job. A cloud workspace provides a complete interface immediately. A model API supplies models and compute only. A self-hosted open-source stack requires internal infrastructure expertise. An AI appliance combines hardware and platform, while a larger GPU server is designed for more load, models, or availability.

This guide compares six practical approaches for organisations. It does not declare a universal winner. The right choice depends on data, workflow, user count, model requirements, integrations, and who owns operational responsibility.

Product information checked on 16 August 2026.

Contents

• Six approaches at a glance
• 1. Cloud workspace
• 2. European model service
• 3. Dedicated private cloud
• 4. Self-built open-source stack
• 5. Preconfigured AI box or appliance
• 6. Custom GPU server or cluster
• Which approach fits which organisation?
• What to assess before deciding

Six approaches at a glance

The terms overlap. “Private AI” may mean a dedicated cloud or fully local infrastructure. “Self-hosted” does not say whether the organisation operates the platform itself or appoints a provider. Every proposal should therefore state where models, chat, knowledge, logs, and backups live and who holds administrative access.

1. Cloud workspace

Cloud workspaces such as ChatGPT Business or Microsoft 365 Copilot are the fastest route to a managed AI workplace. User administration, interface, models, and many functions come from the provider. This fits when rapid adoption and current cloud models matter more than open model choice or local operation.

Cloud does not automatically mean business data is used for model training. OpenAI states that business data is not used for training by default. Microsoft describes Enterprise Data Protection for Microsoft 365 Copilot, existing identity and permission controls, and that prompts and responses are not used to train foundation models. The actual plan, enabled features, data region, retention, web search, and connected services still require assessment.

Usually fits when: few integrations are needed, the intended data scope has been approved, and the organisation does not want to build a platform team.

Usually fits less when: offline operation, open model choice, local inference, or direct control of updates and data paths is required.

2. European model service

A European model service provides models through an API. Your application sends requests to the service and handles the responses. This can fit contractual and regional requirements better than an arbitrary consumer service, but it is not a complete employee platform.

Interface, identity, permissions, knowledge sources, logging, and cost controls still need integration. The word “European” alone does not establish which subprocessors are involved, where inference and storage occur, or how long data is retained.

Usually fits when: own GPU hardware is undesirable, demand varies, and an existing application needs a model API.

Usually fits less when: processing must remain exclusively on the internal network or work without internet access.

3. Dedicated private cloud

In a dedicated private cloud, the AI platform runs in an isolated environment at a European operator or in the organisation's data centre. Compared with a shared API, network, storage, models, and administrative access can be specified more precisely. Compared with hardware in the office, infrastructure remains easier to scale.

The decisive question is not only whether the environment is dedicated, but who can access the host, hypervisor, backups, and keys. A credible proposal documents tenant separation, data location, support access, recovery, and exit procedures.

Usually fits when: an organisation wants to avoid on-site hardware while retaining more control than a public workspace provides.

Usually fits less when: air-gapped operation, local network latency, or a strict requirement for on-site processing applies.

4. Self-built open-source stack

A self-hosted stack usually combines an interface such as Open WebUI, an inference engine such as Ollama or vLLM, a language model, and potentially a vector database. Open WebUI documents users, groups, permissions, and knowledge spaces. Mistral documents self-deployment of its models, including with vLLM.

Source availability alone does not create a production service. Container and driver maintenance, identity, TLS, secrets, updates, monitoring, backups, model testing, and a vulnerability process are also required. RAG adds connectors, permission checks, index refresh, and quality evaluation.

Usually fits when: GPU hardware, Linux and container expertise, and durable operating ownership already exist.

Usually fits less when: an administrator is expected to build the platform on the side and run it without assigned responsibility afterwards.

5. Preconfigured AI box or appliance

An AI appliance or AI box combines hardware, base system, model runtime, interface, and a defined commissioning scope. The task changes from assembling components to defining the use case and integration.

WZ-IT's AI Cube Pro uses compact NVIDIA GB10-class hardware with 128 GB of unified memory. The scope includes hardware, Open WebUI, local model serving, an agreed model, base hardening, functional testing, initial setup, and support. Personal and shared knowledge spaces are available through the interface. Automatic Nextcloud, SharePoint, DMS, or file-share connections remain a separate integration scope.

NVIDIA documents 128 GB unified memory, 10 GbE, and ConnectX-7 for the DGX Spark class. Actual performance depends not only on model size but also on quantisation, context, concurrency, and runtime. A user count without model and workload assumptions is therefore not a fixed guarantee.

Usually fits when: an organisation wants to introduce local AI quickly with a defined scope and retain a path to expansion.

Usually fits less when: very high concurrency, several large models, or high availability are already confirmed requirements.

6. Custom GPU server or cluster

A custom GPU server is sized for models, memory, throughput, data storage, redundancy, and location. Multiple systems may distribute load or serve larger models together. This class begins with a defensible workload description, not a particular model name.

Planning covers active concurrent users, input and output lengths, target response time, knowledge retrieval, batch workloads, and failure requirements. Power, cooling, rack space, network, replacement strategy, and recovery also matter. WZ-IT sizes GPU servers and AI Cube Custom systems from measured or realistically modelled load.

Usually fits when: a compact appliance demonstrably does not meet the requirement or availability and growth need technical commitments.

Usually fits less when: the use case and real demand have not yet been validated.

Which approach fits which organisation?

A hybrid platform can also work. Sensitive tasks run locally while approved cloud models remain available for other content. Users must be able to see which model processes a request, and confidential knowledge must not be routed to external endpoints accidentally.

What to assess before deciding

1. Workflow: Which exact task should become faster or better?
2. Data: What content is processed, and what may leave the environment?
3. Usage: How many people generate answers at the same time, not merely in total?
4. Quality: Which model produces acceptable results on real material?
5. Knowledge: Are manual knowledge spaces enough, or must sources synchronise automatically?
6. Identity: Which users, groups, and existing permissions apply?
7. Operations: Who patches the platform, models, and operating system and responds to incidents?
8. Exit: Can data, configuration, and the knowledge index be handed over in documented form?

Only then is a product decision defensible. Cheap hardware becomes expensive if nobody operates it. A convenient cloud service may be unsuitable if its data paths or capabilities do not match the use. A large GPU platform does not compensate for an undefined use case.

Sources

• OpenAI: business data privacy
• Microsoft: Enterprise Data Protection for Microsoft 365 Copilot
• Open WebUI: Knowledge
• Open WebUI: permissions
• Mistral AI: self-deployment
• NVIDIA: DGX Spark hardware overview
• WZ-IT: cloud AI vs self-hosted
• WZ-IT: size a local AI server