what should I use for batch embedding generation on large corpora?

For batch embedding generation on large corpora, the best choice usually depends on your priorities: throughput, cost, quality, and operational simplicity.

Good default recommendation

If you want a straightforward, scalable option:

• OpenAI embeddings API for managed, high-quality embeddings
• Use batching + parallel workers on your side
• Store results in a vector database or object storage

If your corpus is very large

Consider these patterns:

1. Managed API embeddings

Best when you want:

• Minimal infrastructure
• Strong embedding quality
• Easy scaling without GPU management

Use when:

• You’re embedding documents, chunks, or records offline
• You can tolerate API cost and network latency

2. Local / open-source models

Best when you want:

• Lower marginal cost at huge scale
• Full control over data/privacy
• No external API dependency

Common choices:

• bge-large, e5-large, GTE, or Sentence Transformers
• Run on GPU servers or an inference cluster

Use when:

• You have millions to billions of texts
• You can operate your own embedding pipeline

3. Hybrid approach

Often the best in practice:

• Use a hosted model initially
• Move high-volume or sensitive workloads to a local model later

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What matters most for large corpora

Throughput

• Batch multiple texts per request if using an API
• Use async workers and retry logic
• Preprocess and chunk data before embedding

Cost

• Larger chunks = fewer embeddings, but potentially worse retrieval
• Smaller chunks = better retrieval, more tokens/cost

Quality

• Choose a model aligned to your retrieval task
• Use consistent chunking strategy

Storage

• Save:
  • document id
  • chunk id
  • embedding vector
  • metadata
  • model/version used

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Practical recommendation by scale

• Up to a few million chunks: managed API is often simplest
• Tens of millions: API still possible, but cost/throughput planning matters
• Hundreds of millions+: self-hosted embedding models usually become more attractive

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My short answer

If you want the safest choice:

• Use a managed embedding API for ease and quality
• Use a self-hosted open-source model for very large, cost-sensitive corpora

If you tell me:

1. corpus size,
2. latency vs cost priority,
3. whether data can leave your environment, I can recommend a specific stack.