On-prem or cloud ML for predictive maintenance? It depends. Here’s on what.
The short answer, as of 2026: choose cloud ML when your data is already allowed to leave the network and a data-science team will operate the platform. Choose on-prem when process data sits under ITAR, CMMC, or HIPAA controls, when lines run air-gapped, or when the engineers who own the problem, not data scientists, will run the models.
Most regulated manufacturers are in the second group. That is why Cogentic ships as a local-first desktop app: it profiles, trains, and predicts on your machine, inside your network, offline if it has to be.
Cogentic vs. cloud AutoML
Cloud AutoML platforms (the DataRobot archetype) are hosted suites built for data-science teams: broad MLOps tooling, elastic compute, and enterprise licensing that SaaS procurement data (Vendr, 2026) puts at a median of roughly $212,000 per year.
Cogentic is a zero-code desktop application: it profiles, cleans, trains, and predicts on tabular data entirely on your own hardware, deployable within your ITAR · CMMC · HIPAA · SOC 2 · GDPR controls, and it is running inside one of the hundred largest US manufacturers today.
If your data is unrestricted and you staff data scientists, cloud AutoML is a legitimate choice. If your data can't leave the building, it isn't one.
Three ways to get a model. Side by side, including where we lose.
Five things buyers actually weigh: where the data lives, who runs the tool, how fast a model ships, whether an auditor can re-run it, and what the bill looks like. Read the whole table. The other two columns win real rows.
On the workstation it was installed on. Offline and air-gap capable: nothing uploads.
In the vendor's cloud by default. VPC and on-prem editions exist, but each is its own infrastructure project.
On your network, spread across the scripts, notebooks, and laptops one person works from.
The quality, process, or reliability engineer who owns the problem. Zero code, no data scientist in the loop.
A data-science team. The platforms are genuinely good for them, and assume you have one.
The specialist you hired. Deep skill, concentrated in a single person.
Days. Train on a CSV or Excel export you already produce, on the machine it's sitting on.
Weeks to quarters: procurement, security review, and connectors before the first model.
Months to recruit, then more months learning your process before the first dependable model.
Reproducible by default: same data in, same model out, every run documented for audit.
Strong governance and registry tooling, once your team configures and maintains it.
As disciplined as the individual. Notebooks are hard to re-run after the author moves on.
Flat and predictable: $12,500 pilot, then $36,000–$90,000/yr plus a scoped implementation. No compute metering.
Enterprise licensing at a median of ~$212K/yr per Vendr procurement data, plus cloud compute and integration services.
One fully loaded salary often exceeds $150K/yr before tooling, and the capability can resign.
Cogentic is deployable within your ITAR · CMMC · HIPAA · SOC 2 · GDPR controls. Competitor and salary figures are analyst-reported context, not quotes. Full Cogentic pricing on /pricing.
Elastic compute and MLOps breadth. Training hundreds of models with a mature data-science team on unrestricted data? Their pipelines, registries, and monitoring go further than any desktop app should claim.
Unlimited custom scope. Deep learning on images or raw sensor streams, bespoke research, ML inside your own product: that's a person, not a tool. If ML is your product, hire the team.
Cogentic covers the middle deliberately: the tabular prediction work most plants actually need (scrap, defects, downtime), run by the engineers already on the floor, on data that never leaves the building, tailored to your operation when we deploy it.
The fair comparison is your own numbers. Let’s run them.
Bring a dataset you already export. We’ll train a model on it, on your machine, and put the result next to the scrap, yield, or downtime numbers you track today.