Cohere embeddings feel too slow for my app

If Cohere embeddings feel too slow, there are a few common fixes depending on where the latency is coming from:

Quick things to try

• Batch requests: Embed multiple texts per API call instead of one-by-one.
• Shorten input text: Trim boilerplate, headers, and duplicated content before embedding.
• Use async / parallelism: If you’re embedding many independent items, pipeline them.
• Cache embeddings: Reuse vectors for repeated or unchanged text.
• Move embedding generation offline: Precompute embeddings for your corpus instead of doing it at request time.

If you need lower latency

• Use a smaller/faster embedding model if Cohere offers one that fits your quality needs.
• Reduce request payload size and avoid unnecessary metadata in the embedding call.
• Check network distance: If your app servers are far from the API region, latency can dominate.
• Warm up your pipeline: Keep connections alive and avoid cold-start overhead.

If the app is user-facing

A common pattern is:

1. Serve the user immediately with cached or approximate results.
2. Run embedding + retrieval in the background.
3. Update results once ready.

Alternatives

If your latency budget is very tight, you might consider:

• Local embedding models for on-device or self-hosted inference
• Dedicated vector pipelines where embeddings are precomputed
• Smaller open-source models tuned for speed

If you want, I can help you profile where the slowdown is and suggest the best optimization based on your setup:

• real-time search
• RAG
• document ingestion
• chat memory
• mobile / edge app