ERP Customization vs Configuration: Knowing the Line

Deciding between an open-source and a proprietary language model comes down to a tradeoff between raw capability, control over data, and cost at your actual usage volume. Proprietary models from major providers typically lead on raw capability and require no infrastructure to run, while open-source models offer more control over data handling and can be self-hosted for sensitive workloads.

Cost also flips depending on volume: proprietary APIs are cheaper at low usage, while self-hosted open-source models can be more economical at very high, sustained volume.

We help clients model both scenarios against their expected usage before committing, since the right answer changes significantly with scale and data sensitivity requirements.

Capability gaps between proprietary and open-source models have narrowed considerably, but proprietary providers still tend to lead on the hardest reasoning and multi-step tasks, and they ship new capabilities faster since they control the entire stack. Open-source models close the gap fastest on narrower, well-defined tasks where fine-tuning on domain-specific data closes most of the difference anyway.

Data control is the deciding factor for many regulated clients regardless of the cost math: self-hosting an open-source model means data never leaves your own infrastructure, which simplifies compliance conversations that would otherwise require negotiating data processing agreements with an external API provider.

The hidden cost of self-hosting is operational, not just financial: running inference infrastructure reliably requires GPU capacity planning, monitoring, and the engineering time to keep the serving stack updated as new model versions release, which is a real ongoing commitment that a managed API abstracts away entirely.

Our usual approach is to prototype against a proprietary API first, since it gets a working system in front of users fastest, then evaluate a migration to a self-hosted open-source model only once usage volume and data sensitivity requirements make a strong enough case to justify the added operational overhead.