Batch Embedding

Batch embedding is the operation of generating embeddings for many input texts in a single API call or inference pass, dramatically improving throughput compared to processing one input at a time. Most embedding APIs support batches of 100 to 2,048 inputs per request, with the optimal batch size depending on input length, model capacity, and provider rate limits. OpenAI's batch API offers 50% pricing discounts on batches processed within 24 hours, attractive for offline ingestion of large corpora. Self-hosted embedding services like vLLM, Text Embeddings Inference (TEI), and Hugging Face Inference Endpoints achieve much higher throughput in batch mode than streaming because GPUs amortize the per-call overhead across many inputs. Batch embedding is essential for initial cold start indexing of large corpora and for periodic reembedding after model upgrades. AI governance teams document batch parameters (size, parallelism, retry strategy) in their embedding pipeline configuration and monitor for failed batches that would otherwise produce silent gaps in the indexed corpus. Modern frameworks like LangChain, LlamaIndex, and Haystack abstract batch embedding behind clean ingestion APIs.

Batch embedding through Centralpoint: Centralpoint coordinates batch embedding operations across whatever provider you use — OpenAI, Cohere, Voyage, self-hosted TEI — with token metering, budget enforcement, and per-batch audit logging. The model-agnostic platform keeps prompts local, supports both generative and embedded models, and deploys retrieval chatbots through one line of JavaScript on any portal.


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