I’m unhappy with Google Vertex AI embeddings for batch processing

Sorry you’re running into that. Vertex AI embeddings can be a pain for batch workflows if you need:

• predictable throughput
• low per-item latency
• easy batching/retries
• consistent output quality
• simple cost control

If you want, I can help in a few directions:

1. Diagnose the problem
   Tell me what’s going wrong:

   • slow batch jobs
   • rate limits / quota errors
   • inconsistent embeddings
   • high cost
   • bad semantic search quality
   • awkward API/batching behavior

2. Optimize Vertex AI usage
   I can suggest:

   • batching strategy
   • concurrency settings
   • retry/backoff patterns
   • chunk sizing
   • quota tuning
   • storage/queue patterns for large jobs

3. Suggest alternatives
   Depending on your needs, I can compare Vertex AI against:

   • OpenAI embeddings
   • Cohere
   • Hugging Face / local models
   • Jina / Voyage / Mistral embeddings

4. Design a batch pipeline
   I can sketch a robust architecture for:

   • millions of documents
   • incremental updates
   • idempotent retries
   • vector DB ingestion
   • monitoring and failure recovery

If you share:

• your document volume
• embedding model you’re using
• batch size
• throughput target
• what specifically feels bad

…I can give a concrete recommendation.