Hugging Face

In Singapore, buying generative AI is less about which model you pick and more about what happens to your data once it enters a prompt — so anchor your evaluation to IMDA/PDPC's Model AI Governance Framework, the PDPA, and a hard look at hosting, evaluation, and cost.

How to evaluate an LLM / generative-AI provider in Singapore

• Decide your hosting model first: have the vendor map your specific use case to hosted API vs private/self-hosted deployment, and state plainly what data leaves your control and where it sits.
• Get a contractual answer to whether your prompts, retrieval indexes, or fine-tuning data are ever used to train a shared model — and align the terms with IMDA and the PDPC's Model AI Governance Framework for Generative AI before sending anything sensitive.
• Treat the PDPA as a design constraint, not paperwork: you stay accountable for any personal data put into a prompt or index, so pin down access, logging, retention limits, and sub-processor controls in writing.
• Require an evaluation against your own content before go-live — golden test sets, retrieval-accuracy scoring, and human review — and ask to see the failure cases, with AI Verify's generative-AI testing work as a reference point.
• Model the run cost, not just the build: ask for a per-token or per-seat (and GPU/inference) estimate tied to your expected volume, plus usage ceilings or spend alerts, and a clear split between one-off build and ongoing run.
• If you are a bank, insurer, or capital-markets firm, confirm the provider understands MAS model-risk, explainability, and human-oversight expectations rather than treating them as your problem to solve.

Verify for Hugging Face

• Confirm key details directly with the vendor — this listing isn't vendor-managed yet.
• Ask for two recent Singapore client references you can speak with.
• Ask for a written scope of services before comparing quotes.
• Request evidence of relevant certifications and their current validity.

Questions to ask

• Does our data — prompts, retrieval indexes, or fine-tuning sets — ever train a shared model, and where is it stored and processed?
• Can you run an evaluation against our own documents before go-live and show us the hallucination and data-leakage failure cases?
• What does the monthly run cost look like at our expected query and user volume, and how is build cost separated from the ongoing token, GPU, or seat bill?
• Who owns the prompts, fine-tuned weights, and generated outputs, and what guardrails make the system refuse rather than guess?

Prepared under the TechDirectory editorial byline with review credit June C; this label is not proof of identity, credentials, or a completed human review. This directory-authored checklist is not attributed to Hugging Face; editorial independence has not been independently audited. Editorial standards