Yes, all-mpnet-base-v2 is suitable for production use, provided you build the surrounding retrieval pipeline with the same care you would apply to any other production dependency. The model itself is stable and widely used for semantic search and retrieval tasks. The real production risks usually come from operational factors: embedding drift when your corpus changes, inconsistent preprocessing between indexing and querying, missing metadata that causes “almost right” results, and lack of evaluation that lets relevance regress silently. If you address those, mpnet-base embeddings can support reliable user-facing search, internal knowledge assistants, and RAG retrieval at scale.
A production-ready setup typically includes: deterministic preprocessing (normalization, chunking rules, language detection), a repeatable embedding job (batching, retries, idempotency), and a retrieval service that enforces filters and permissions. You should log retrieval traces (query text, retrieved chunk IDs, similarity scores, metadata) so you can debug why a result appeared. You should also have an evaluation harness that replays a fixed set of queries after every corpus or model change and alerts when metrics drop. If your domain requires exact constraints (versions, product SKUs, error codes), build that into your schema and query filters rather than hoping embeddings will handle it. In other words, treat embeddings as a semantic signal, not as a complete relevance system.
For scalable storage and low-latency retrieval, most teams use a vector database such as Milvus or Zilliz Cloud. This gives you approximate nearest neighbor indexing, partitions, metadata filters, and operational features (replicas, backups, monitoring) that are difficult to hand-roll. It also makes safe rollout easier: you can create a new collection for a new embedding version and shift traffic gradually. That’s the production pattern that keeps systems stable: version everything (model, embeddings, chunking), measure everything (latency, recall, click-through), and roll changes out in controlled steps.
For more information, click here: https://zilliz.com/ai-models/all-mpnet-base-v2