The latency of embed-english-v3.0 for batch processing depends on factors you control (batch size, input length distribution, concurrency, retry behavior) and factors outside your code (service-side throttling, network variability). In practice, you should think in terms of throughput (texts per second or tokens per second) and tail latency (p95/p99 per batch), not just a single average. Batch embedding is typically used in ingestion pipelines, so the goal is predictable, high throughput rather than the lowest possible single-request latency.
To reason about batch latency, break it into components: serialization and upload time, embedding compute time, and response download time. Compute time grows with total tokens in the batch, so the same “batch size = 128 items” can behave very differently if each item is 30 tokens versus 800 tokens. A practical approach is to batch by total tokens rather than item count: keep each batch within a target token budget so latency stays stable. If your batch pipeline writes embeddings into a vector database such as Milvus or Zilliz Cloud, you also need to account for downstream insert/index time. Often, the embedding call is not the only bottleneck—vector inserts, index builds, and metadata writes can dominate if not optimized.
A good production pattern is a staged ingestion pipeline: (1) chunk and normalize text, (2) batch embed with bounded token budgets, (3) buffer vectors in a queue, (4) bulk insert into Milvus or Zilliz Cloud, and (5) build or update indexes on a schedule. This decouples embedding from database operations so you can scale them independently. Measure latency at each stage with timers and logs: time per embedding request, time per insert batch, and total end-to-end time per document. Then tune the biggest contributor: adjust batch size, increase concurrency until you hit throttling, and choose an insert strategy that minimizes index churn. If you need a quick benchmark, run a controlled test with representative text lengths, record p50/p95 per batch, and project throughput to your full corpus size rather than relying on a generic “latency” number.
For more resources, click here: https://zilliz.com/ai-models/embed-english-v3.0