Two assistants can look identical on screen, take the same prompt and return the same shape of answer. What separates them sits underneath the window. Buyers put the challenge to us without decoration: we already use ChatGPT, so are we not already doing AI? The reply concedes the screens really do look alike, then names the three things that are not.
Private AI vs Public Cloud AI
How Is Private, Locally Run AI Different
From ChatGPT and Other Public Cloud Assistants?
Three differences decide the comparison. Here they are, with the work a hosted assistant still does better and the page that settles each contest underneath the decision.
Built from real buyer questions in our sales meetings
Direct Answer
Three differences decide it. Where the inference happens, on your own device or in a datacenter somebody else operates. What the answer is grounded in, a corpus you curated and govern or public training data plus whatever a connector reaches. How the cost behaves, a one-time perpetual license against a recurring per-seat subscription. Iternal builds AirgapAI on the first side of all three: it runs 100% local on an AI PC, answers from a data set you prepared, and is licensed per device rather than per seat. The two sides are not substitutes for every task. The private option wins where the material cannot leave and where every employee needs access.
The limit: the two sides are not products of the same kind. Iternal is candid about its own shape. The core technology is hard to sell as a thing at all unless it is wrapped in services, and the individual products each solve one specific problem or run too technical for a general audience. A hosted assistant arrives as a subscription somebody in your building already knows how to buy. The private option arrives as a project with a services line under it. Compare the two totals rather than the two licenses.
What to settle in writing first. The license shape for the configuration you would actually buy, a list of what leaves the machine, and a working session run on your own documents rather than on a curated sample. The targeted questions below put each on paper.
Four contests sit inside this decision, each with a page of its own. A suite your organization already pays for. Building it yourself on open weights. Scoring one product against another. What any of it costs. The routing table below names where each one is settled.
Three Differences That Decide the Comparison
Feature lists flatter whichever product wrote them. Three structural questions do not, because each has a physical answer: where does the math happen, what does the answer draw on, what does the invoice do next year.
| What differs | A public cloud assistant | A private, locally run assistant |
|---|---|---|
| Where inference happens | In the provider’s datacenter. Your document travels to their servers to be answered, and the service cannot report on local commands or files touched. | On the machine in front of the user. Iternal states AirgapAI runs 100% local across the NPU, CPU and GPU of an AI PC and keeps working with the network card off. |
| What grounds the answer | Public training data, plus whatever a connector may reach in your tenant. Coverage is broad; the corpus is not yours to curate. | A data set you chose and prepared. Iternal runs the documents through Blockify, its patented ingestion and cleansing step, before any model sees them. |
| How the cost behaves | A recurring per-seat subscription. Moving to a different hosted service changes the logo on the invoice, not the shape of the bill. | A one-time perpetual license tied to the device, good for its life with updates included. Iternal also publishes a monthly subscription option. |
Read the third row as a coverage question rather than a discount. A per-seat subscription is normally licensed to a slice of the organization, because the recurring line stops being fundable long before it reaches everybody. Iternal treats price as an adoption ceiling: covering the whole workforce is what changes the outcome. The argument is about who gets the tool.
License shapes differ across the range and across the configuration you would buy, so take the numbers from your quote. Three written answers make it auditable:
Pin it down: questions for your evaluation
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For the exact configuration we would deploy, is the license a one-time perpetual purchase, a monthly subscription, or a choice between them, and what is the term?Which cost behavior you are buying, before anyone models three years of spend.
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What is included for the life of the device, and what carries a separate maintenance, upgrade or services charge?The gap between a license total and a deployment total, where the two sides stop comparing.
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Which data ever moves off the endpoint during install, licensing, updating or ordinary use, and will you list it for us?Row one of the table above, converted into something a security reviewer can file.
Where a Hosted Assistant Is Still the Better Answer
A comparison that never names the other side’s wins is an advertisement. Four categories of work belong to the hosted assistant.
Work that lives inside the productivity suite. Mail, calendar, meeting artifacts and documents already in the tenant. The integration is the product there, and a local client reaching in from outside cannot match it. Iternal sees the two blend rather than replace one another: prove the local client on your own machine, then keep the seats that earn their keep on suite-native work.
Frontier-scale reasoning on material that is allowed to leave. Iternal is direct about the ceiling. A model small enough to run on a laptop sits closer to a 2024-generation hosted assistant than to a top-tier model in a large datacenter, and the local chat experience is very close to, rather than equal to, the hosted one.
Long autonomous agent runs. Iternal makes no claim that AirgapAI is an equivalent to a leading agentic coding harness, and does not present it as a full agentic application. Chained, tool-using work over many steps is hosted territory today.
Anything the answer needs that your corpus does not hold. A governed data set is a boundary as well as an asset. Ask it a question it was never given the material to answer and it will say so — correct behavior, and still not the answer you wanted.
The hosted assistant is broader. The private one is narrower and yours. Breadth loses where the material may not travel, or where the subscription stops short.
The Strongest Case Against the Private Option
Put the challenge at full strength, because beating a weak version proves nothing. It runs like this. We already use a public assistant, so we are already using AI. The hosted model is larger than anything that fits on a laptop, and it improves every few months without us lifting a finger. Our people drop documents into it and it works. An engineer could chain a few skills together and reproduce most of what you sell. And we already pay for the subscription, so a second assistant buys a weaker model in exchange for a rollout, a training burden and another line item.
Two of those points are true and stay true. The hosted model is larger, and it does improve without you. Iternal concedes both above.
The rest breaks on scale, on ownership and on what the service can report afterwards. Dropping documents into a hosted assistant works once. It does not work at the volume an organization runs, it does not work on an ongoing basis, and the metered bill stops being tolerable well before it does. Hosted collaboration tools keep the model in their own cloud, transmit the files to their servers, and cannot report on local commands or files touched — precisely the report a security reviewer asks for. Accuracy moves too: cleansing a corpus before the model sees it is what lifts the answer, and a public service leaves you no corpus to cleanse.
What the challenge misses. A second assistant does cost a rollout, and that much is fair. It leaves out that the subscription already owned reaches a fraction of the workforce, and that the material the best people most want help with is often what policy forbids them to paste anywhere. The gap is the case, measured in people and documents rather than benchmarks.
Which Contest Are You Actually In?
The comparison is one question with one verdict, and every argument after it belongs to a specialist. Name the contest, and the next page picks itself.
| What the room is arguing about | What it settles | Where it is answered |
|---|---|---|
| A suite you already fund — why a second assistant at all. | Which share of the workforce the seats reach, and what a local client adds. | the suite-and-local-client page |
| An engineer with a weekend — it runs on open weights, so why buy. | What a license replaces once a prototype has to reach a fleet. | the build-or-buy page |
| Two products that look alike from a distance — somebody has to score them. | The axes, their weighting, and the acceptance test to set beforehand. | the option-scoring page |
| Finance wanting the number first — before anyone commits to an architecture. | The total either side carries, license and everything around it. | the cost page |
| A claim on a slide — a benchmark or a percentage nobody sourced. | Which figures survive being asked how they were measured, ours included. | the claims-audit page |
| A need to watch it run on your own material — not a prepared sample. | What a working session proves, and what only your full corpus can. | the demonstration page |
| A team that wants hands on it — before any paperwork moves. | The no-cost routes, who registers, and each term. | the trials page |
| A board asking who else has done this — and who survives to renewal. | What can be shown by sector, and what stays private. | the references page |
Answered elsewhere
- Whether your material is permitted to leave the boundary that governs it — see the confidential-data page.
- Which hardware a deployment sits on, from endpoint to server room — see the deployment placement page.
- Which model to run once the category decision is made — see the model-selection page.
- What a law firm should put on its own shortlist — see the legal tools shortlist.
- What an agency with a modest IT budget can afford to run — see the small-agency shortlist.
FAQ
FAQ: Private AI and Public Cloud Assistants
Three structural differences, each with a physical answer somebody can check. Inference runs on your own device rather than in a datacenter somebody else operates. The answer is grounded in a corpus you curated and govern rather than in public training data plus whatever a connector reaches. The cost is a one-time perpetual license tied to the device rather than a recurring per-seat subscription. Iternal builds AirgapAI on the first side of all three, and the two sides are not substitutes for every task.
You are using some of it. A hosted assistant covers general work on material that is allowed to leave, and covers it well. It stops at two edges. The first is the material your own policy forbids anyone to paste anywhere, usually what your best people most want help with. The second is headcount: a per-seat subscription reaches a slice of the organization. Coverage rather than capability is what a second tool buys.
Three things change at once. The document travels to servers you do not operate. The answer draws on a corpus you did not curate. And the service cannot report on local commands or files touched, so the report your security reviewer wants does not exist. Running inference on the device removes all three. For more information visit the confidential-data page.
Three conditions, and the case strengthens as they stack. The material cannot leave the boundary that governs it. Every employee needs access rather than a licensed slice, which a per-device perpetual license reaches and a per-seat subscription usually does not. And you hold documents worth curating, because a private assistant earns its accuracy from the corpus behind it. Where none applies, a hosted assistant is the better buy.
A search engine is generic by design: everyone typing the same words gets the same options back. A prompt is tailored to you and goes beyond what a search returns, which is why the input has to carry more. Write a full paragraph of context rather than the three to five keywords a search box trained you to use. On a private deployment it also runs against your own governed data set.
On your own documents it can, and the reason is the corpus rather than the model. Iternal runs source material through Blockify, its patented ingestion and cleansing step, which restructures content before any model sees it. The limit runs the other way on raw capability: a model small enough for a laptop sits closer to a 2024-generation hosted assistant than to a top-tier datacenter model. For more information visit the answers you can trust page.
Start With the Three Differences
Decide where the inference has to happen, what the answer has to be grounded in, and how the cost has to behave. Those three settle the category. Every argument left standing — the suite you fund, the engineer with a weekend, the scorecard, the invoice — has a page above it.