When companies imagine “building AI,” they often picture expensive bespoke software. Usually the cheaper, faster path is integration, not building from zero. Here’s an honest cost view. (dgm implements osFoundry, a separate company’s platform — dgm is an independent integration partner, not osFoundry. General information, not financial advice; figures vary by scope.)

What actually drives cost

  • Integration — connecting to your systems and data (the biggest driver).
  • Data preparation — cleaning and structuring.
  • Workflow complexity — how intricate the automation is.
  • Maintenance — recurring upkeep (often understated).

Model/token costs are usually modest by comparison — see implementation cost.

Build bespoke vs integrate a platform

For most business use cases, integrating a model-neutral platform is cheaper and faster than bespoke. Building from zero means recreating model routing, retrieval, governance and UI that platforms already provide — plus carrying all the maintenance. Bespoke makes sense mainly for genuinely novel needs (see build vs buy capability).

Maintenance is the silent cost

AI systems aren’t build-once: models change, data evolves, integrations break when systems update, quality needs monitoring. Budgeting only for the build and not the run is a classic reason AI software overruns.

How dgm prices

Rather than open-ended custom quotes, dgm implements on osFoundry for a flat $399 assessment and $3,999/month, including ongoing implementation and upkeep (INR approximate; USD authoritative; 18% GST domestic). Genuinely bespoke needs beyond that are discussed explicitly — making cost predictable versus an open-ended build.

General information, not financial advice. Costs vary by scope — get a scoped estimate.