The main latency tradeoffs when using voyage-code-2 come from (1) embedding time, (2) vector search time, and (3) your choices about chunk size and batching. voyage-code-2 is listed as a 1536-dimension model with a 16,000 token max input length, which means it can embed large snippets, but larger inputs cost more tokens and usually take longer to embed. If you embed entire files (or very long code blocks) at query time, latency will spike and you may also degrade relevance. Most production setups avoid that by embedding the corpus offline and embedding only short queries online. For an environment-specific reference point, the AWS Marketplace listing for voyage-code-2 reports ~90 ms latency for a single query with at most 100 tokens on a specific instance configuration, which is useful as a rough “order of magnitude” datapoint (not a universal guarantee).
Vector search latency is usually smaller than embedding latency for many applications, but it becomes important at scale. Larger vector dimensions increase storage and can increase index memory footprint, which may affect cache behavior and tail latency under load. The flip side is that higher-quality embeddings can reduce the amount of downstream work you do (reranking, additional retrieval passes) because top-k results are better. That’s a real latency trade: slightly heavier embeddings can produce better top-k precision, which can reduce multi-stage retrieval complexity. To keep search fast, store embeddings in a vector database such as Milvus or Zilliz Cloud, and use metadata filters to shrink the candidate set (repo/language/service). Smaller candidate sets generally mean faster and more stable search.
Finally, batching is a classic throughput vs latency lever. During ingestion, you should batch embedding calls to reduce overhead and improve throughput. During interactive search, you usually want low per-request latency, so you keep queries short and avoid sending huge batches unless your UX can tolerate it. A practical end-to-end approach is: embed your codebase incrementally (per commit), store vectors and rich metadata, and at query time do one embedding call + one Milvus search + optional lightweight post-filtering. That architecture keeps p95 latency predictable and makes performance tuning mostly about token length, concurrency, and index/search parameters rather than ad-hoc prompt hacks.
For more information, click here: https://zilliz.com/ai-models/voyage-code-2