How Forge compares
Most computer-vision platforms are built for machine-learning teams shipping cloud APIs. Forge is built for inspection and QA teams shipping a model to their crews' phones. Here is an honest look at how it compares to the usual options.
| SkillsBase Forge | Roboflow | LandingLens | Vertex AI / Azure Custom Vision | In-house build | |
|---|---|---|---|---|---|
| Built for | Field inspection & QA teams | ML engineers, edge devices | Manufacturing ML teams | ML / cloud engineers | Your data scientists |
| Code required | None | SDK / code to deploy | Cloud / API-first | Yes | Yes |
| Data to start (per fault) | ~15 images | Hundreds to thousands | Hundreds | Thousands | Thousands |
| Inspector mobile app | Branded iOS & Android, included | No (targets edge devices) | No | No | Build it yourself |
| Runs offline on-device | Yes | Depends on setup | Cloud-first | Cloud-first | Depends |
| Time to first deployed model | Days | Weeks | Weeks | Weeks to months | 3 to 6 months |
Roboflow is a capable, general-purpose computer-vision platform, but it is built for developers: you label and train in the tool, then write code against an SDK to deploy, typically to edge devices rather than to a crew's phone. Forge is end-to-end and no-code, and the inspector mobile app is part of the product, not something you build afterwards.
Best for inspection teams without engineers: ForgeLandingLens is strong industrial visual AI, focused on the manufacturing line and cloud or API-first delivery. Forge covers field inspection more broadly, telecoms, utilities, construction, and the work that happens away from a fixed line, and ships a branded phone app that runs the model offline in tunnels, cabinets, and dead zones.
Best for field work off the production line: ForgeThe hyperscaler platforms are powerful and flexible, and they assume an engineering team: you write code and you bring a lot of data, often thousands of images per class. Forge needs no code and starts from around 15 images per fault, because every model begins from a starting point already adapted to real field-inspection imagery.
Best for no-code, low-data starts: ForgeBuilding it yourself, or hiring a consultancy, can work, but it usually means a 3 to 6 month project and a six-figure budget before the first model reaches the field, plus an app to build and maintain. With Forge, the people who already know your faults build and deploy the model themselves, in days, and retrain in one click as faults change.
Best for speed and cost: ForgeBring the defects your current tools keep missing, and we will show you what a model trained on your own data can catch, no data scientists, no consultants.
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