BERT Base itself is an open‑source model that you can download and run locally at no direct licensing cost. Because the model weights are freely available, there’s no per‑token or subscription fee charged by a provider for the model itself. This makes BERT Base an attractive choice for organizations that want tight control over infrastructure costs and data privacy, especially when self‑hosting on their own servers or cloud GPUs.

When using BERT Base via a third‑party hosted API or managed inference service, pricing is typically usage‑based, meaning you pay for the compute resources consumed rather than a fixed subscription. Hosted plans commonly charge based on the number of tokens processed or the amount of compute time used. In these environments, input processing tends to be billed at a lower rate than inference time, since generating embeddings or classification results requires more compute cycles.

For example, hosted access to BERT Base might be priced at around $1–$3 per million input tokens for simple embedding or classification tasks, with higher rates if the service also returns detailed output or integrates into larger workflows. Because BERT is optimized for tasks like classification, search ranking, and embedding generation rather than long text generation, total spend in live applications is often lower than with generative models. Additionally, teams routinely use batch processing and caching to reduce redundant inference calls, which helps control costs in high‑volume or repeated‑query scenarios. With this flexible usage‑based pricing from hosted providers, BERT Base remains a cost‑effective choice for many AI workflows.