Should You Run Your Own LLM?

Dimension	🖥 Local LLM	☁️ Cloud API	🛠 SaaS AI Tool
Privacy	STRONG Data never leaves your machine	MODERATE Sent to provider; subject to ToS	WEAK Data processed + often retained
Cost	LOW Hardware cost, then ~$0/query	VARIABLE Per-token pricing; scales with use	HIGH Monthly subscription, per-seat
Control	FULL Choose model, prompt, fine-tune	PARTIAL Model locked; provider can change	MINIMAL Locked to vendor features
Threat Model	MINIMAL No external exposure if airgapped	MODERATE API key exposure, provider breach risk	ELEVATED Credential theft, supply chain risk
Data Residency	GUARANTEED Stays on your hardware, your network	REGIONAL Provider-dependent; may vary	UNKNOWN Often unclear in ToS
Offline Capability	FULL Works without internet access	NONE Requires internet + provider uptime	NONE Fully cloud-dependent

START

Are you considering using an LLM for security or privacy-sensitive work?

Will you feed it sensitive data?

IOCs, incident notes, internal docs, customer data, credentials

YES

Consider Local

Sensitive data should not leave your network. Local inference is the appropriate model.

→ Q2

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Do you have data residency or compliance requirements?

HIPAA, GDPR, FedRAMP, internal data governance policies

YES

Local or Private Cloud

Cloud APIs and SaaS tools rarely satisfy strict residency requirements.

→ Q3

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Do you need inference to work offline or air-gapped?

Field ops, air-gapped networks, incident response without internet

YES

Local Only

Cloud API and SaaS tools are non-starters for offline environments.

→ Q4

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Do you have suitable hardware available?

A modern GPU (8GB+ VRAM) or a capable CPU for smaller models

YES

Go Local

You have everything you need. Start with Ollama + a 3B–7B model.

Cloud API

Use a cloud API for non-sensitive work. Avoid feeding it anything you wouldn't publish.

Recommended: Local

Acceptable: Cloud API (non-sensitive only)

Avoid for security work

LOCAL vs. CLOUD LLM — DECISION GUIDE