FortaifyDeployment comparison
Local AI and cloud AI solve different constraints.
Local and cloud inference look identical in a chat window and create different operating boundaries underneath. Compare them on data destination, model capability, hardware, connectivity, administration, cost shape, and who carries the responsibility — then decide per task rather than per company.
Download GlyphSide by side
The dimensions that change the decision, with the honest answer for each side.
| Dimension | Local inference | Cloud inference |
|---|---|---|
| Data destination | Device or server you operate; nothing leaves unless a feature explicitly sends it | Provider infrastructure; governed by that provider’s terms and retention settings |
| Model capability | Open-weight models from 1B to 70B+; near-frontier quality needs a large machine | Frontier models with no local hardware; capability changes on the provider’s schedule |
| Hardware | You size it: 16 GB laptop for mid-sized models, GPU host for large ones | None beyond a browser; capacity is elastic and metered |
| Connectivity | Works offline once models are present | Requires a working connection and a healthy service |
| Latency | Predictable; depends on your hardware | Usually fast; subject to network and provider load |
| Administration | Model lifecycle, endpoint security, updates, and troubleshooting are yours | Provider operates the model; you manage accounts, keys, and usage |
| Cost shape | Hardware and operations; near-zero marginal cost per request | Per-token or per-seat; scales with usage |
| Evidence and control | As strong as your own logging and policy — server-side in Glyph Private | Provider dashboards and audit features vary by plan |
When local wins
Local inference is the right default when the content is the point: contracts, source code, medical or legal material, unreleased plans, anything a client would be surprised to learn had been sent to a third party. It also wins when connectivity is unreliable, when per-request costs would add up across a team, and when predictability matters more than peak capability. A mid-sized open model on a modern laptop handles most drafting, summarising, and coding assistance well; see the local LLM guide for sizing.
When cloud wins
Hosted frontier models are still ahead on the hardest reasoning, on very long documents, and on breadth of knowledge. They are the right choice when a task needs that capability, when no suitable hardware exists, or when a team is evaluating before committing to infrastructure. The discipline is to know what you are sending: with a cloud model selected, the prompt, any attached files, and any retrieved context go to the provider. Review the provider’s current terms and data controls before permitting it for a class of data.
The mixed policy most teams end up with
The useful question is not “local or cloud” but “which destination for which data.” A workable policy looks like: confidential and disconnected work goes to approved local models; a named hosted model is permitted for tasks that need it, with specified data classes excluded; the interface shows the destination on every response; and the organisation can enforce the policy rather than rely on training.
Glyph implements that policy shape. Local models are the default and labelled as local. Cloud providers appear only when a user adds a key, and every hosted response is attributed to its provider. On desktops enrolled with Glyph Private, the administrator publishes the allowed models and the gateway enforces routing, DLP rules, and usage records server-side.
Cost, honestly
Cloud costs are visible and variable; local costs are up-front and fixed. A team of ten on a hosted plan pays every month and per token above allowances. The same team on 16–32 GB laptops already owns the hardware for mid-sized local models; a single GPU host in the tens of gigabytes of VRAM serves a large model to everyone with no marginal cost. What local adds is operations time — model updates, an occasional runtime issue — which is small on a desktop and real on a server. Price the operations time, not just the hardware.
Security and compliance considerations
Local inference removes the disclosure-to-vendor risk and leaves everything else: device security, access control over indexes, prompt injection through connectors, backups. Cloud inference moves the model risk to the provider’s controls and adds a contractual dependency. Neither side is “compliant” on its own; compliance is your process, informed by what each deployment actually does. Fortaify does not claim certifications for Glyph; the Trust Center documents implemented controls and defaults so your assessment can start from evidence.
A decision worksheet
Answer these for each workflow, not for the company. The answers usually sort tasks into local, cloud, or “either with a policy” within an hour.
- What is the most sensitive thing this workflow will ever contain?
- Would a mid-sized local model handle it acceptably? Have we tested that, or assumed it?
- Does it need to work without a connection?
- What hardware do the people doing it already have?
- If a hosted model is used, which data classes must be excluded, and how is that enforced?
- What evidence do we need afterwards, and where would it come from?
Frequently asked questions
Is local AI more secure than cloud AI?
It removes one specific risk — sending content to a model vendor — and leaves device and access risks in your hands. Whether that is “more secure” depends on how well you run devices and access control compared with the provider’s controls.
Can I use both in one tool?
Yes. Glyph runs local models by default and lets you add your own keys for hosted providers; responses are labelled with their destination. Glyph Private enforces which destinations a team may use.
Will local models catch up to hosted ones?
Open-weight models have closed much of the gap on everyday tasks and continue to improve. For the hardest tasks, hosted frontier models remain ahead; the sensible plan is to re-evaluate periodically with your own prompts.