Which AI model should we use — a major vendor API or an open-source model we host ourselves?

Direct Answer

The vendor API versus open-source decision is not a technical preference — it is a risk allocation decision. Managed APIs from major vendors (OpenAI, Anthropic, Google) give you faster deployment, lower infrastructure burden, and continuous model improvement, at the cost of data leaving your environment and pricing that scales with usage. Open-source models hosted internally give you data residency, cost predictability at scale, and model control, at the cost of significant infrastructure and ML operations investment. The right answer depends on what your actual risk surface is, not on which approach sounds more sophisticated.

Deeper Answer

Data residency is the most common reason organizations choose open-source self-hosting, and it is often a legitimate one. If you process regulated data — protected health information, financial account data, classified material — many vendor API agreements do not meet your compliance requirements regardless of what the sales team says. Read the data processing agreement, not the marketing materials. If your data cannot leave your environment under any circumstances, managed APIs are not an option and the decision is made for you.

For most other workloads, the managed API path is faster and more reliable than it appears from the outside. Frontier model providers have invested billions in reliability, security, and safety infrastructure that would cost years to replicate internally. The productivity argument for using a managed API is not laziness — it is resource allocation. Every engineer hour spent on ML infrastructure is an hour not spent on the application layer where your actual competitive advantage lives.

Cost predictability at scale is where the open-source case becomes strongest. Managed APIs are priced per token. For high-volume production workloads — processing millions of documents, running continuous background pipelines, generating outputs at scale — the per-token cost compounds quickly and can exceed the total cost of ownership for internal infrastructure within 12 to 18 months. Model the usage curve before committing either direction. The break-even point varies significantly by workload type and volume.

The hybrid approach is increasingly standard: use managed APIs for development, prototyping, and lower-volume production workloads; migrate to self-hosted open-source for high-volume production workloads once the use case is validated and the volume justifies the infrastructure investment. This lets you move fast early without over-committing infrastructure, then optimize costs once you have real production data on usage patterns.

Capability gap is the final consideration. The performance difference between frontier models and leading open-source alternatives (Llama, Mistral, Qwen) has narrowed significantly but is not zero. For complex reasoning, multi-step analysis, nuanced judgment, and tasks requiring deep world knowledge, frontier models still lead. For well-defined, structured tasks — classification, extraction, templated generation — open-source models often perform comparably at a fraction of the cost. Match the model tier to the task complexity, not to a blanket enterprise standard.