The maximum batch size for embed-english-v3.0 is usually constrained by practical service limits such as total tokens per request, maximum items per request, and request payload size, rather than by a single universal “N items” number. In many embedding APIs, you can submit a list of texts in one call, but the true limit is the combined size of those texts after tokenization. That means a batch of 100 short titles might be fine, while a batch of 20 long paragraphs might exceed the limit. For developers, the safest approach is to treat “batch size” as a dynamic value controlled by a token budget.
A practical batching strategy is to batch by total tokens, not by item count. For example, you can implement a simple packer that groups inputs until you hit a target token budget, then sends the batch and starts a new one. This keeps latency predictable and reduces the chance of oversized request failures. When you’re embedding a corpus for storage in a vector database such as Milvus or Zilliz Cloud, token-budget batching also helps you maintain stable throughput: you get consistent embedding times per batch and consistent insert sizes per batch. Combine this with idempotent ingestion (so retries don’t create duplicates) and you’ll have a resilient pipeline.
If you need a number to start with, begin conservatively: pick a batch size that you know is safe for your typical chunk length, then increase gradually while measuring error rates and p95 latency. Add backoff and automatic batch splitting when you receive “payload too large” or “input too long” errors. Also consider downstream costs: if you batch-embed 5,000 vectors at once but then insert them into Milvus or Zilliz Cloud in tiny writes, you’ll lose throughput in the database layer. Aim for balanced batches: large enough to reduce overhead, small enough to retry cheaply and keep tail latency under control.
For more resources, click here: https://zilliz.com/ai-models/embed-english-v3.0