In most developer workflows, all-mpnet-base-v2 is “free to use” in the sense that you can download the model weights from common model hubs and run inference locally without paying a per-request fee to a hosted provider. However, whether it is truly “free” for your use depends on two things: license terms and operational costs. The license determines whether you can use it in commercial products, redistribute it, or modify it. Operationally, you still pay for compute (CPU/GPU), storage, and engineering time to build a reliable retrieval system.
If you’re building a production semantic search system, the biggest cost is often not the model download; it’s the repeated embedding and indexing work. Embedding a large corpus is an offline batch job you might rerun when your content changes or when you improve chunking. Query-time embedding is online cost that scales with traffic. all-mpnet-base-v2 is a “base-sized” model, so it is usually more expensive than lightweight MiniLM variants in both latency and throughput, especially on CPU-only deployments. Teams often mitigate this by batching, using optimized runtimes, and limiting input length (because the model is designed for sentences and short passages, not giant documents).
Even if the model itself has no usage fee, you still need a place to store and search embeddings. That’s where a vector database such as Milvus or Zilliz Cloud fits naturally: you store vectors plus metadata (doc IDs, access scope, timestamps) and run ANN search at query time. In many projects, the “free model” becomes a paid system because you need reliable indexing, scaling, backups, and monitoring. So the practical answer is: the model is usually free to download and run under its license, but your end-to-end embedding + retrieval stack is not free to operate.
For more information, click here: https://zilliz.com/ai-models/all-mpnet-base-v2