Compare cloud, dedicated, local and hybrid AI properly? WZ-IT evaluates data classes, quality, load and operating cost, then implements the selected model with gateway, monitoring and an exit path. Explore LLM hosting
AI operation is no longer a binary cloud-or-server choice. Public APIs, dedicated managed instances, self-hosting and hybrid combinations are all viable. This comparison covers data protection, quality, total cost, control and operational responsibility. As of August 2026.
For a centrally managed employee chat, private ChatGPT for business compares the four practical deployment paths from a user perspective.
Table of contents
- Two operating models for AI
- Data protection and control
- Cost: per token versus your own hardware
- Cloud AI and self-hosted compared
- When to use which model
- Dedicated and hybrid options
- Total cost instead of token price
- Decide with your own evaluation set
- How WZ-IT implements the platform
- Sources
Two operating models for AI
With cloud AI you use a language model as a service: your application sends requests to a provider's API, the model computes on their servers, the answer comes back. You need not provide hardware or operate anything - but model and data lie in someone else's hands.
With self-hosted AI, a selected model runs on controlled infrastructure (see What is local AI?). Your team or a managed-service provider may operate it. Actual control depends on administration, keys, data flows, exports and exit rather than the label alone.
Data protection and control
The central question is: which data is processed by whom, for what purpose and where? Beyond prompt and response, consider documents, embeddings, usage metadata, telemetry, error reports, support access and backups.
Self-hosting can avoid transfer to a model provider, but only if cloud features, web search, telemetry and external observability are controlled as well. Ollama, for example, documents a local-only setting that disables its cloud features. Test outbound connections rather than relying on product labels. International cloud transfers can be lawful under GDPR mechanisms; they still require a use-case-specific assessment.
Cost: per token versus your own hardware
Cloud AI is generally usage-based across input, output, embeddings and tools. It can be inexpensive at entry and for irregular demand. Variable costs grow with usage, while a self-hosted GPU mainly produces idle capacity when demand is low.
Self-hosted AI shifts cost into capacity and operations. Hardware, energy, redundancy, storage, backup, updates, monitoring and people all belong in the calculation. The useful metric is cost per successful task including human correction, not token or server cost alone.
Cloud AI and self-hosted compared
| Dimension | Public cloud API | Self-hosted AI |
|---|---|---|
| Data | processed under provider contract and controls | flows governed by the target architecture |
| Cost model | mainly variable with usage | capacity, platform and operations |
| Control | provider controls platform and portfolio | model, version and stack are selectable |
| Operations | low effort, integration remains | high effort, outsourceable as managed service |
| Scaling | strong for peaks | limited by provisioned capacity |
| Exit | depends on API, features and exports | easier with open formats, still needs testing |
When to use which model
The rule of thumb follows data and usage:
- Cloud AI where the data and contract fit, demand varies widely or a specific closed model is required.
- Self-hosted AI where data flows need tight control, base load is predictable or model and platform portability matter.
Dedicated and hybrid options
A dedicated managed instance combines stronger isolation and a defined region with outsourced operations. A hybrid platform exposes several model targets through a gateway. It can keep selected data classes local while using external models for specific capabilities or demand peaks.
LiteLLM can centralise deployments, fallbacks, timeouts and load balancing. It does not classify secrets automatically. The application must reliably provide tenant, data class and allowed targets, and fallback rules must not silently downgrade protection.
Total cost instead of token price
Include model and tool use, GPU idle time, storage, network, backup, high availability, replacement capacity, security updates, monitoring, human correction and exit. A mixed model is often economical: stable base load on controlled capacity, with selected external use for uncommon peaks or specialist tasks.
Decide with your own evaluation set
Define 30 to 100 representative tasks, expected results and prohibited data paths. Compare two to four variants on quality, grounded answers, latency, throughput, cost and operational risk. Include update, outage, load spike and rollback. This shows whether a smaller local model is sufficient or whether a dedicated or hybrid design adds measurable value.
How WZ-IT implements the platform
WZ-IT combines architecture and operations: LLM hosting for controlled endpoints, managed AI for the production stack, AI Cube for on-site scenarios and the AI platform as a dedicated instance with Open WebUI and models operated in Germany by German providers. Gateway, identity, RAG, monitoring, backups, evaluation and recovery can be included.
The result is not a blanket cloud or local recommendation, but an architecture with explicit data paths, quality limits, costs and responsibilities. The open-source LLM stack explains its layers.
For the broader buying decision across private cloud, self-built stacks, AI appliances, and GPU clusters, see the comparison of on-premises AI approaches.
