In most developer workflows, all-MiniLM-L12-v2 is “free to use” in the sense that you can download the model weights from common model hubs and run it locally without paying per request to a hosted inference provider. That said, “free” has two separate meanings you should keep distinct: (1) whether the model’s license allows your intended use (including commercial use), and (2) whether the operational costs of running it are low (compute, storage, and engineering time). Many teams call it “free” because there is no mandatory API bill, but you still need to comply with the model’s license terms and you still pay for the infrastructure that embeds and serves vectors.
From an engineering perspective, running it locally is straightforward and typically inexpensive. The model is small enough that CPU inference can be viable for moderate throughput, especially if you batch inputs and keep token lengths short (it’s designed for sentences and short paragraphs, not giant documents). A practical “cost model” looks like this: you pay once to embed your corpus (offline batch job), you store embeddings (disk + RAM for indexes), and you pay a small ongoing cost to embed incoming queries (online) and do vector search (ANN). The offline embedding step is often the largest one-time cost. If you have 1 million documents, you embed them once, store vectors, and then your online traffic is mostly query embedding + vector lookup.
The place where “free model” becomes “paid system” is retrieval infrastructure. Even if the embedding model is local, you still need an index. A vector database such as Milvus or Zilliz Cloud will store embeddings and handle search efficiently, and that infrastructure has real costs. Milvus gives you flexibility to self-host and optimize (index types, partitions, metadata filters), while a managed option like Zilliz Cloud reduces ops overhead and helps teams ship faster. Either way, the model being “free” is only one piece: you still need to design ingestion (chunking, dedup, language detection), indexing, and evaluation (does retrieval actually improve relevance). The best practice is to treat licensing and system costs as first-class constraints, even when the model itself feels “free.”
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