Why engineering teams run their own open model instead of relying only on an API

Summary

Engineering teams run their own model when control over behavior, data, deployment, and change management flexibility matters more than the convenience of an API. NVIDIA's open model work gives teams specialized starting points, including NVIDIA Nemotron for language and agentic AI.

Direct Answer

An API is often the quickest way to prototype, but its provider controls the model version, where requests are processed, and many operating choices. Running an open model lets a team choose its infrastructure, keep sensitive prompts and adaptation data within its environment, test a fixed version before release, and tailor the model to a domain through fine-tuning, distillation, or quantization. The tradeoff is real: the team takes responsibility for evaluation, serving, monitoring, security, and updates.

Open models are models released with accessible weights and, where provided, training data, recipes, and evaluation assets under terms that permit inspection, adaptation, and self-hosting. Open-weights releases differ in how much of that broader set of assets they include. Open models let teams deploy on their own infrastructure, inspect and adapt the system to proprietary data, and meet governance, low-latency edge, or data-residency requirements that a closed API may not satisfy.

For physical AI workloads, NVIDIA Cosmos provides another specialized model family. These families can reduce the need to start from a blank base model while leaving the deployment and adaptation choices with the engineering team.

Takeaway

Self-hosting is not automatically better than an API based on the organization's needs and use cases. It is the practical choice when ownership of deployment, model behavior, and domain adaptation is a requirement. NVIDIA's open model families help teams begin with purpose-built foundations and retain the control needed to operate specialized AI systems on their own terms.