So you’ve decided to point AI at your network data. Good. The very next question, and it’s the one that quietly stalls a lot of these projects, isn’t which model or which vendor. It’s where the AI actually runs: on infrastructure you own, or in someone else’s cloud? For network data specifically, that’s not a small detail. It’s the difference between analysis that happens inside your environment and analysis that depends on sending your data somewhere else.
That’s the real hesitation behind a lot of the caution around AI and network data, and it deserves a straight answer instead of a sales pitch. So let’s walk through the two architectures, what actually differs between them, and how to figure out which one fits what you’ve already got.
Why this is the question that stalls projects
Network data is sensitive in a way that’s easy to underestimate. It’s a record of every system talking to every other system: who reached what, when, and how much. Hand that to an AI and you get fast answers. But where that analysis happens, and on whose infrastructure, runs straight into the constraints you already live with — security policies, existing tooling, budget, and in some environments a hard rule that certain data doesn’t leave the building. None of that gets solved by a smarter model. It gets solved by picking the right architecture up front.
The two ways to run AI on network data
Strip away the noise and there are two basic approaches, plus the hybrid that most teams actually land on.
Cloud, or managed, AI. Someone else runs the model for you. You connect your data, their infrastructure does the heavy lifting, and you don’t stand up or maintain anything yourself. It’s the fastest way to get started, and for a lot of teams it’s the right call. The tradeoff is that the analysis runs on infrastructure you don’t control.
On-prem, or self-hosted, AI. You run the model on your own infrastructure. That might be a server in your data center, a private cloud, or a fully air-gapped environment with no outside connection at all. You get maximum control over where everything runs. The tradeoff is that you need the infrastructure, and the people, to run it.
Neither one is simply "better." They’re answers to different situations, and that’s the whole point.
What actually differs between them
Four things separate these in practice, and none of them is the AI being smarter in one place than the other.
- Control over where it runs. Self-hosted keeps the model and the analysis inside your environment, up to fully air-gapped. Managed puts them on the provider’s infrastructure. If you’ve got a policy or a regulator that dictates where things can run, this is usually the deciding factor.
- Infrastructure and effort. Managed is close to turnkey. You’re not maintaining GPUs or model deployments. Self-hosted means you own the stack, the updates, and the capacity planning, and that’s real, ongoing work. It’s worth being honest with yourself about whether you want it.
- Model choice. Self-hosting usually means you pick the model and can swap it later. Managed often means you use what the provider offers. If running a specific or open model matters to you, that pushes toward your own deployment.
- Cost shape. Managed tends to be operating cost that scales with how much you use it. Self-hosted is more up-front investment that you then own. Which one’s cheaper depends entirely on your scale and how long you run it, so don’t let anyone hand you a universal answer here.
How to actually decide
Here’s the short version. If you already run model infrastructure, or you’ve got a requirement that network data stays in your environment, self-host. If you don’t want to stand up and babysit that infrastructure, and you don’t have a hard rule against managed analysis, go managed and get moving. Plenty of teams end up somewhere in between, and the thing that matters most is that you’re not forced into one just because your tool only supports the other.
That last part is where a lot of tools quietly fail you. A good number of AI features are welded to the vendor’s own cloud, so if your situation calls for on-prem, you’re stuck. The real question to put to any vendor isn’t "do you have AI?" It’s "can I run it where I need to?"
Where Plixer fits
This is the part we built for, because our customers land all over that map. Plixer gives you the same investigative engine either way, reading the same vendor-neutral flow record your network already exports. You just choose where the AI runs.
If you’ve got the infrastructure, bring your own model. Point the AI you already run at your own flow data, self-hosted, in a sovereign cloud, or fully air-gapped, and buy no sensors to feed it. Your model, your infrastructure, your network data.
If you don’t have that infrastructure, and plenty of teams don’t and don’t want it, use the Plixer AI Assistant instead. Plixer runs the model for you on managed cloud infrastructure, so you get the same investigation without standing up or maintaining anything of your own. You’re asking questions of your flow data without a GPU budget or a deployment project in front of you.
Either way, it’s the same engine reading the same complete flow record. The choice is only about where it runs, and that choice stays yours instead of the tool’s.
The architecture call should be yours
Most vendors make this decision for you by supporting only one architecture. We think that’s backwards. You know your environment, your policies, and your budget better than any vendor does, so the call belongs to you. Your AI, your model, on your network data, or ours if you’d rather not run your own. It’s the same flow record either way: the one your network team uses to keep things fast, and your security team uses to keep them safe. Same flow record, both jobs, wherever the AI happens to run.
Key Takeaways
The hard part of running AI on network data isn’t the model. It’s the architecture, and whether your tool lets you choose it.
- The real question isn’t which model, it’s where the AI runs: your infrastructure or someone else’s.
- Managed AI is turnkey but runs where you don’t control it. Self-hosted gives you control, up to air-gapped, but you run the stack.
- The tradeoffs that matter are control over location, infrastructure effort, model choice, and cost shape, not one being smarter than the other.
- Decide based on what you already run and any rules about where your data can live. Most teams land on a hybrid.
- Don’t let the tool make the choice for you. Ask any vendor whether you can run their AI where you need to.
Next Steps
If you’re working through where your network AI should live, two related pieces go deeper.
- On why the connector matters less than what’s behind it, including whose model and whose infrastructure, read "MCP is about to be table stakes. The real decision comes next."
- For the basics of how an AI connects to your systems in the first place, "What Is MCP, and Why Is Everyone Suddenly Talking About It?" explains the protocol in plain language.
Frequently Asked Questions
With cloud, or managed AI, a provider runs the model on their infrastructure and you connect your data to it. With on-prem, or self-hosted AI, you run the model on infrastructure you control, which can go all the way to a fully air-gapped setup. The analysis is similar either way. What changes is who runs it and where it happens.
No. If you don’t want to stand up and maintain model infrastructure, a managed option runs it for you, and you’re asking questions of your data without a deployment project. You only need your own infrastructure if you specifically want to self-host, usually for control or for a rule about where data can live.
Yes, if the platform supports your own model on your own infrastructure. That’s the point of a self-hosted deployment: the model and the analysis run inside your environment with no outside connection required. Plixer supports this by letting you bring your own model, self-hosted or fully air-gapped.
Neither is automatically more secure. It comes down to your environment and your policies. Self-hosting gives you more direct control over where everything runs, which matters a great deal in some settings and very little in others. The right question isn’t which is safer in the abstract, it’s which fits the rules you actually operate under.
It depends on your scale and how long you run it. Managed cost usually scales with use and has little up-front spend. Self-hosted is more up-front investment that you then own. At small or bursty usage, managed often wins. At large, steady usage over time, owning it can come out ahead. Model your own numbers rather than trusting a blanket claim.
It means pointing the AI your team already runs at your own network data, on infrastructure you control, instead of using the provider’s model on the provider’s cloud. You choose the model, and you decide where it runs, including self-hosted, in a sovereign cloud, or air-gapped.
That’s the common path. Many teams start managed to move quickly, then bring some or all of it in-house as their needs or policies change. What you want to avoid is a tool that locks you into one architecture, because then switching means switching products, not just settings.
Plixer runs the same investigative engine over the same vendor-neutral flow record regardless of where the AI lives. Bring your own model and run it self-hosted, sovereign, or air-gapped, or use the Plixer AI Assistant and let Plixer run it for you on managed infrastructure. Same analysis, same data underneath, your choice of deployment.
