Self-Hosted AI for Enterprises: Frequently Asked Questions

Leaders considering self-hosted AI keep hitting the same questions. Here are direct answers, without the vendor spin.

Why self-host at all when APIs are cheaper per token?

API pricing is cheaper until you factor in data egress, prompts that carry sensitive context, and vendor lock-in on your workflows. Self-hosting keeps your data, weights, and accumulated fine-tuning inside your walls — the means of production stay yours. For teams thinking about long-term unit economics, see LLM Cost Control.

Which open-source models are good enough for enterprise work?

For most knowledge-work tasks — summarization, extraction, cited Q&A over your memory — current open-weight models in the 30B–70B range match or beat frontier APIs on domain data once grounded in a good retrieval pipeline. Reserve frontier APIs for the narrow cases that need them.

Where does the data actually live?

With a self-hosted stack: model weights, inference logs, embeddings, and source documents all sit in infrastructure you control — your VPC, your data center, or a dedicated tenancy under your DPA. Nothing crosses to a third-party model provider.

What about hallucinations?

Hallucinations drop sharply when the model answers from a governed memory layer with citations, instead of from its training data. The lever is retrieval quality, not model size.

Do we need an ML team to run this?

No. A small platform team can operate a self-hosted stack once the memory layer and agent tooling are in place. Domain teams build their own agents through a no-code Agent Maker — no prompt engineering, no ML PhDs.

How long until we see value?

With memory ingested first and narrow agents second, most teams see measurable workflow wins in weeks. The AI SNAP Enterprise Platform is designed to compress that timeline.

What's the biggest mistake enterprises make?

Buying an agent platform before building shared memory. Agents without memory are demos. Memory without agents is still an asset.