I’m unhappy with Azure OpenAI embeddings cost

If the main issue is cost, there are a few practical ways to reduce your Azure OpenAI embedding bill, depending on your use case.

1) Reduce how much you embed

• Chunk less aggressively: Use larger chunks only where it makes sense.
• Deduplicate text before embedding.
• Cache embeddings so repeated content isn’t re-embedded.
• Only embed changed documents in incremental pipelines.

2) Use a smaller/cheaper embedding model

If you’re using a higher-capacity embedding model, consider a lighter one if quality is still acceptable for your search/retrieval task.

3) Reduce embedding frequency

• Batch updates instead of embedding every small change immediately.
• Re-embed only on meaningful edits.
• Avoid embedding transient content that won’t be queried.

4) Control vector storage and retrieval costs

Even if embeddings themselves are the concern, downstream vector DB/storage can add cost:

• Store fewer vectors by improving chunking strategy.
• Use metadata filtering to search smaller subsets.
• Remove stale embeddings.

5) Consider alternatives

Depending on your constraints:

• Open-source embeddings hosted on your own infra can be much cheaper at scale.
• Smaller local models may be enough for internal semantic search.
• Some teams use a hybrid approach: local embeddings for most traffic, paid model for higher-value cases.

6) Measure ROI

Sometimes embedding cost is acceptable if it materially improves:

• search relevance,
• customer support retrieval,
• RAG answer quality,
• developer productivity.

If you want, I can help you with one of these:

1. Estimate your current Azure OpenAI embedding cost
2. Compare Azure OpenAI vs open-source embedding alternatives
3. Design a cheaper chunking + caching strategy