Should you deploy the agent platform in your own data center, or use someone else’s first?

Over the past two years, the question I’ve been asked most often is this: our company wants to get into AI agents—should we buy an account on a public platform, or build our own setup?

The people asking are all in similar positions: companies with dozens to hundreds of employees, either the boss or the IT/digitalization lead reporting to the top executive. It’s not that they don’t understand technology—in fact, it’s precisely because they understand it a little that they know there are pitfalls.

I don’t want to fob you off with “it depends.” To really make a judgment, we first have to acknowledge one thing: these two types of products don’t solve the same problem at all.

Public agent platforms like WorkBuddy are selling “the right to use capabilities.” You register an account, and by this afternoon you can have AI organize your meeting notes, write your weekly report, or dig up competitor intelligence.

A privately deployed agent platform like AgentSteamer is selling “the ability to produce capabilities.” It doesn’t promise what you’ll be able to use tomorrow; it promises that what you build yourself belongs to you, stays in your data center, and can be governed.

So “which is better” is a false question. The real question is: what are you missing right now—a car, or a garage? Once you’re clear on that, the math becomes much easier. I’ll follow that order in this article: first, where private deployment genuinely beats public platforms; then, fairly, where public platforms are strong; and finally, a practical decision line you can act on.

01The most valuable thing about private deployment isn’t actually “security”

Mention private deployment, and most people’s first reaction is data security. That’s true, but security is the least valuable part. It’s the baseline—and baselines don’t create competitive advantage.

What’s truly valuable about private deployment is this: who owns what you build.

On a public platform, that set of prompts, that workflow, those repeatedly refined lessons you spent three months tuning—strictly speaking, they’re just a configuration on that platform. When the platform changes its rules, you have to change with it. When it raises prices, you have to pay. And if one day you want to move elsewhere, chances are you can’t take it with you.

Private deployment is different. Take AgentSteamer’s approach: it treats agents, skills, workflows, prompts, memory, and artifacts as a set of assets that need unified governance. That sounds a bit convoluted—in plain terms, every component of every agent in the company is registered: it has a name, an owner, a version, and outputs.

Look one level deeper, and these components are managed as “asset objects”—applications, skills, workflows, prompts, instructions, tools, and memory each have their own models and interfaces, rather than being scattered across someone’s personal account. The significance of this is that when people leave, the assets don’t leave with them.

Another easily overlooked point: AgentSteamer breaks agent production into a complete pipeline—creation, visual orchestration, testing, publishing, and operational governance. It sounds like corporate speak, but it solves a very practical problem: in many companies, agents are “ad-hoc configurations”—whoever has time sets one up, and once it’s done it’s left there, with no one knowing whether it’s still running. With this pipeline in place, agents go from “ad-hoc configurations” to “standardized production,” treated the same as any other serious system in the company.

The artifacts piece deserves special mention. When an agent does work, it doesn’t just spit out a paragraph of text—it generates documents, PPTs, reports, and images. In AgentSteamer, these are stored in a dedicated place, officially described as “manageable, controllable, previewable, and traceable.” Translated into management language: three months from now, when the boss asks “who had AI produce that customer analysis report, and which version of the data did it use?”, you can answer.

On public platforms, by contrast, this kind of thing usually lives in chat history—finding it is luck; not finding it is the norm.

As for version management and one-click rollback, AgentSteamer supports part of this, but it hasn’t been fully rolled out yet. I’d rather be clear about that here than have you discover after going live that it doesn’t match what the article says.

02Can what one department builds be reused by another?

This is the most underrated aspect of private deployment—and the biggest money-waster I’ve seen in enterprises.

The current state of most companies is like this: Marketing builds its own set of copywriting prompts on a public platform, Sales builds another set, and Customer Service builds yet another. The three sets don’t know each other exists; the same pit is stepped in three times by three departments, and the money is spent three times over.

A private platform can plug this gap—but only if it genuinely has “shared-use” mechanisms, not just software installed in a data center and left at that.

Here’s roughly how AgentSteamer handles this.

Prompts have their own library, and everything in it is shared across the enterprise, with version control and parameterization. Parameterization means the sales team’s “client visit summary template” turns company name, industry, and visit target into fill-in-the-blanks — the customer service team just changes two fields and it’s ready to use. The library also supports one-click test runs, so you don’t have to wire something into a workflow just to try it out.

Workflows can be exported as JSON and imported back in. This sounds minor, but it’s genuinely useful: a workflow one department has gotten working can be copied wholesale to another department, so they don’t have to drag every node from scratch. Combined with application templates, you can start a new build directly from a ready-made template and skip the scaffolding work.

On the skills side, it’s compatible with the Agent Skills spec and the mainstream MCP protocol. You don’t need to remember those two names — just the outcome: existing tools and skills from the broader ecosystem can be plugged in and used directly, without writing them from zero. This effectively brings over part of what public platforms tout as their “rich ecosystem” advantage — private deployment doesn’t mean building in isolation.

One more detail I think matters. Memory policies inherit across multiple levels: node, application, and organization. In plain terms, rules set at the company level — like “AI is never allowed to make the call on anything involving pricing” — are automatically inherited by every agent underneath, with no need to configure them one by one. This kind of “company rules cascade downward” capability is basically impossible on public platforms, because they don’t even have the concept of “your company.”

A quick note on the built-in scenario templates: there are currently 4, which isn’t many. The mechanism for accumulating and reusing them works, but the template pool needs time to grow. Expecting a hundred ready-made industry templates out of the box isn’t realistic.

03Where your data lives is something you only think about after something goes wrong

I’ve seen too many companies treat data security as a nice-to-have during selection, only to discover it’s a veto-level requirement once something happens.

With public platforms, you first have to accept the data logic: your conversations, the documents you upload, the questions you ask — all of it goes through their servers. Most platforms promise in their terms that they won’t train models on your data, and I believe that. But “we promise not to train on it” and “your data never left your own data center” are two guarantees of completely different strength. The former is a contractual constraint; the latter is a physical fact.

For companies in consumer goods, foreign trade, or manufacturing, that gap is exactly the layer that matters. Customer lists, pricing structures, process parameters, supplier payment terms — if any of that shows up in someone else’s server logs, even briefly, in many industries that’s already something you need to explain to your clients.

AgentSteamer’s approach here is straightforward: all capabilities and data are deployed inside the enterprise. Core data — knowledge bases, artifacts, sessions — stays in the company’s own data center the entire time. Data never leaves the domain.

The model layer is covered too. It uses an OpenAI-compatible interface, with BGE-M3 for embeddings and BGE-Reranker for reranking — you don’t need to remember those names, just understand them as “one turns documents into a searchable index, the other re-sorts search results by relevance.” The key point is that both can be installed in your own data center, managed centrally by the model management module. In other words, not only does your data not leave the building — the inference process doesn’t either. For industries sensitive to data flows, like finance, healthcare, and manufacturing, this matters more than any number of adjectives.

I should also be honest here: capabilities like data backup and recovery aren’t on the delivered-features list yet. When you’re evaluating, ask about it directly — don’t just assume.

The accompanying traceability features, though, are in place: audit logs, review logs, and webhook delivery logs. Audit logs record “who did what and when”; review logs record “whether this thing the AI said got blocked.” Only when you have both can you truly trace back. With only the first, that’s an activity record, not an audit.

04Just how fine-grained can permissions actually get?

Every salesperson loves to say this word, but few explain it clearly.

The coarsest granularity is account-level: this account can use it, that account can’t. Public platforms basically stop at this layer, because their user model is just isolated individuals.

What enterprises actually need is finer than that. Within the same sales department: Zhang San can view the customer analysis agent but can only use it, not modify it; Li Si can modify it but can’t publish it; Wang Wu can publish it but it has to go through review first. This isn’t about distrusting employees — it’s because an agent that can auto-generate external-facing copy, given the wrong permissions, causes real damage.

AgentSteamer implements a permission matrix of 11 resource types × 10 operation types, plus department-level inheritance and reviewer configuration. In plain terms: it doesn’t manage “whether you can get in the door” — it manages “once you’re in, which things you can touch and what actions you can perform on each.” The underlying user model is multi-tenant with organizations, departments, and users, so departments are naturally isolated from each other — not relying on people to police themselves.

One layer above that is the publishing stage. There’s a quality gate that automatically runs checks before an agent is published; the review process has three levels of routing — department, company, and application. Department-level tools are reviewed by the department itself, cross-department tools get escalated, and external-facing ones go through company-level review.

Once it’s running, the AgentOps side is observable, measurable, and auditable, with a model usage data dashboard that shows multi-dimensional usage rankings and session tracking. For operators, the value is very tangible: you can see at a glance which department or which agent the money is going to, rather than staring at a lump sum at the end of the month and guessing.

05The public platform’s ledger isn’t just about being cheap

It wouldn’t be objective to dismiss public platforms as worthless. They have several advantages that private deployment genuinely can’t match in the short term.

Ecosystem is the first. Plugins, skills, models, templates—hundreds or thousands of them, continuously updated, and you can basically find a ready-made one for whatever you’re missing. The second is instant availability: sign up and you can get to work, no waiting for IT scheduling, no procurement process—a department manager can try it in the morning and be using it by the afternoon. The third is zero maintenance: servers, models, upgrades, outages—none of it is your concern. The fourth is low unit price: a few dozen yuan per person per month, making the cost of trial and error almost zero.

These advantages are overwhelming for small teams, personal productivity scenarios, and single-point tools. If all you’re doing is writing an industry research report, there’s really no need to build a private deployment for it.

But you have to do the full math. The cost of a public platform is ongoing and grows with usage: the more you use, the more you pay, and that growth is often tied to your business growth. The better business gets, the higher the AI bill. Most of these platforms charge by Token or credits—a Token is the unit of text that AI reads and writes—so it’s hard to estimate a given month’s cost in advance; you can only watch it as you go.

Private deployment’s billing works differently. AgentSteamer offers two paths: a License subscription and a one-time package deal. The subscription is like an annual fee; the one-time package is a buyout—which to choose depends on how you plan your budget. The advantage is that you can calculate clearly before spending, rather than guessing against a pile of usage data. There’s a usage threshold in between; past that point, the conclusions of the two approaches flip.

More importantly, there are costs that never show up on the financial statements: data lives outside, assets can’t be moved, sessions can’t be retained, permissions only go as far as the account level, and when the platform’s policies change, you have no choice but to follow. Some agent platforms built by major vendors in the market are essentially “whatever the platform provides is what you use,” selling Tokens or packaging them as credits. This model is friendly to enterprises, but your autonomy is genuinely limited. None of this shows up on the bill—it shows up years later when you look back and realize you can’t take a single thing you’ve built over these years with you.

06If you really have to choose, here’s my decision line

No need for a complex evaluation model—just ask yourself three questions.

Customer lists, financial data, HR records, process parameters, unreleased product plans—if even one of these falls into the category of “would require an explanation to the client if it showed up in someone else’s logs,” then stop deliberating and go private.

Conversely, for organizing public materials, scraping industry information, or internal learning materials, a public platform is entirely sufficient.

For one person, use a public platform.

If the scenario is “the marketing department finishes it and the sales department picks it up, this year’s version gets revised again next year,” then what you actually need isn’t a tool—it’s a system that accumulates. Things need to stay, be modifiable, and have someone managing them. At that point, the structure of a public platform is no longer enough.

What costs a few hundred a month now could become tens of thousands in three years. If your agents truly become part of your business processes, usage will only go up, never down. Do a rough calculation: add up three years of public platform bills and compare that with the investment in privatization. You will most likely find that the question is not “is it expensive,” but “after all that money is spent, do you actually own anything?”

Finally, one softer point, but an especially important one: does anyone in your company actually want to take charge of this?

This is the most common reason I have seen privatization fail—the software gets installed, nobody operates it, and six months later it becomes a system in the corner of the server room that nobody logs into. The value of a privatized platform is not in the moment of installation; it is in continuous operation. That is why vendors like AgentSteamer bundle deployment and implementation, skill customization, and operations support into their services. The logic is exactly this: software is only the starting point; actually getting things running is the finish line.

07A final honest word

“Start with the public platform to test the waters, and once you understand it, move to privatization”—this is a completely reasonable path, and I do not think it is a waste.

What you spend on the public platform buys you the answer to “which of our company’s tasks are actually suitable to hand over to AI.” That answer itself is very valuable—far more valuable than the subscription fees you save.

There is just one thing you need to understand in advance: what you accumulate during the testing phase most likely cannot be moved. So do not count on building up assets while testing the waters—treat it as tuition, and your mindset will be much better.

Conversely, if you have already figured out “which tasks to hand to AI, who manages them, and who is responsible when problems arise,” then stop delaying. At that point, whatever you keep accumulating on the public platform is not yours.

If you lack a car, rent one first; if you lack a garage, build one first. What you should fear is renting forever while thinking you are building assets.