voyage-large-2 has real limitations developers should plan for: input length limits, cost/latency tradeoffs, and the inherent constraints of embedding-based retrieval. The model supports up to 16,000 input tokens, so very large documents still need chunking; you can’t just throw an entire book at the embedding endpoint and expect a single vector to work well. It also has a fixed 1536-dimensional output, which impacts storage footprint and index size compared to smaller vectors. Finally, the model is listed with a proprietary license, which can matter for procurement, compliance, or redistribution constraints depending on your environment.
There are also “retrieval limitations” that come from embeddings themselves rather than this specific model. Embeddings are strong at capturing topical similarity and paraphrases, but they do not guarantee exact-match behavior for numbers, IDs, version strings, or precise code tokens. If your users search for “error 0x80070005” or “v2.6.1 upgrade steps,” you should chunk so that those identifiers appear in the retrieved text and consider storing additional metadata fields that allow exact filtering (e.g., version="2.6.1"). Another limitation is lifecycle management: if you change your chunking strategy, or if you switch embedding models, you generally need to re-embed your corpus to keep the vector space consistent. Plan for that operationally by making ingestion idempotent and by migrating via “new collection + traffic switch” rather than rewriting vectors in place.
Finally, there are system-level limitations around throughput and latency. Even if voyage-large-2 gives you strong embeddings, your end-to-end performance depends on how you embed (batch vs online), how you index, and how you query. A vector database such as Milvus or Zilliz Cloud can handle large-scale similarity search, but approximate indexes trade recall for speed, and filtering can change query execution patterns. This means you should build a small relevance test set and rerun it whenever you change index parameters, chunk sizes, or metadata filters. The practical pattern is: (1) validate retrieval quality on representative queries, (2) tune index/ANN parameters to hit latency targets, and (3) monitor for drift as your corpus grows. voyage-large-2 is a strong building block, but it still needs an engineered pipeline around it to avoid common pitfalls.
For more information, click here: https://zilliz.com/ai-models/voyage-large-2