Decision guides
Private AI or cloud AI? A deployment checklist for Malaysia
Choose where the whole workflow runs, not just where the model runs. For a business in Malaysia, the useful comparison starts with your data boundaries, workload and the people who will operate the system.
Planning guide. Examples are illustrative; they are not customer case studies or measured results.
Start with a data-flow map
Write down where source files, extracted text, search indexes, prompts, answers, logs and backups are stored and processed. Include OCR, embeddings, monitoring and support access. A locally hosted model can still depend on an external document service. Review every dependency before describing a deployment as fully on-premises.
Match the deployment to the work
On-premises can be appropriate when the organisation needs control of a local environment and can support the equipment. A managed private environment can be appropriate when dedicated resources and agreed data boundaries are needed without operating every layer internally. A public API may be suitable for permitted data and a bounded task. These choices are not interchangeable; compare them against the same representative cases.
Test document search separately from fluent answers
For retrieval-augmented generation (RAG), a plausible sentence is not enough. Check whether the relevant document was found, whether the source actually supports the answer, and whether the user was allowed to access it. Include questions with no supported answer and documents that contradict an older version. Test the languages your staff actually use.
Estimate the full operating cost
Include document preparation, integration, access management, hardware or hosting, inference usage, backups, updates, monitoring, evaluation and staff review. Compare an agreed period and workload. Do not compare a complete cloud subscription with hardware alone, or assume local AI will always be cheaper.
Make the pilot a purchasing decision
Before purchasing production equipment, agree the representative documents and tasks, user access groups, expected workload and acceptance conditions. Record the exact hardware, model, configuration and test date when reporting performance. A successful pilot should leave a deployment decision, known limitations and a named operating owner—not only a chat demonstration.
| Decision | On your infrastructure | Managed or API-based service |
|---|---|---|
| Data path | Map local storage and every outbound dependency | Verify processing locations, retention and support access |
| Operations | Name the hardware, patching, recovery and model owners | Separate provider obligations from your own responsibilities |
| Capacity | Test actual model, memory, context and concurrent workload | Confirm quotas, resource allocation and variable usage cost |
| Continuity | Rehearse backup, restore and equipment failure | Check export, recovery, service failure and exit options |
Plan the next step
- Which source is authoritative when documents disagree?
- Can one department retrieve another department’s restricted content?
- What happens when a task cannot be answered from approved sources?
- Can the business operate if the model or document connector is unavailable?
AAA Labs can scope a private AI and document-search evaluation around these decisions. Send the task, document types and constraints first; agree a separate channel before sharing confidential material.
All decision guides