Open-weights LLMs vs frontier APIs: when to rent, when to own

Developers often begin building AI products by leveraging frontier APIs, only to encounter issues like high usage costs, slow performance, or legal concerns over data residency. This prompts the decision to either rent the API or own the model. Renting a frontier API means utilizing closed weights, where the vendor handles training, serving, and pricing. Owning a model involves selecting a model, post-training it, and running inference on either self-hosted or third-party infrastructure.

The trade-offs between closed (frontier API) and open (open-weights) models include capability, infrastructure, cost, latency, data handling, and lock-in. Closed models provide strong capabilities out-of-the-box but require no infrastructure. Open models, while requiring infrastructure, offer tunable performance. In terms of cost, renting appears affordable initially, but as usage increases, the API's pricing for work can become prohibitively expensive.

Owning a model shifts costs upfront but results in lower per-token costs after initial investment, making it a more economical choice for high-volume usage.

Additional factors like latency, data residency, and vendor lock-in favor owning a model. Users often notice the speed difference, and regulatory requirements may mandate data to stay within specific networks. Vendor lock-in is mitigated by owning the model, giving control over the inference stack and weights. When deciding between open-weights and a frontier API, consider high volume, narrow tasks, latency concerns, data constraints, and the desire for a competitive edge.

Renting is suitable for low or spiky volume, broad tasks, acceptable latency, unrestricted data usage, and a focus on raw frontier capabilities.