Are there any embeddings APIs that handle rate limits well for semantic search at scale?

Yes — but “handle rate limits well” usually means one of two things:

1. The provider has high throughput / generous limits, and/or
2. Your client can batch, retry, and back off cleanly when limits are hit.

For semantic search at scale, the best options are usually:

Good API choices

1) OpenAI Embeddings API

• Strong quality and widely used.
• Works well if you:
  • batch inputs
  • cache embeddings
  • implement exponential backoff with jitter
  • use parallelism carefully
• Good fit if you want managed infra and don’t mind engineering around rate limits.

2) Cohere Embed

• Also good for semantic retrieval/search.
• Often used for enterprise search and retrieval workflows.
• Generally a solid option if you want strong search-oriented embeddings.

3) Voyage AI

• Known for high-quality embeddings for retrieval/search.
• Often a strong choice for semantic search use cases specifically.

4) Google Vertex AI embeddings

• Good if you’re already on GCP and want integration with cloud infrastructure and IAM.
• Can scale well if you use the broader GCP ecosystem.

5) Azure OpenAI embeddings

• Same general model family as OpenAI, with Azure’s enterprise/cloud controls.
• Useful if you need Microsoft cloud integration and compliance controls.

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What matters more than the provider

At scale, the winning approach is usually:

• Batch requests: send many texts per request where supported
• Throttle client-side: don’t let many workers hammer the API simultaneously
• Retry with exponential backoff + jitter
• Queue jobs: smooth bursty traffic
• Cache embeddings: especially for repeated content
• Use async workers + concurrency limits
• Precompute offline: for corpus indexing, generate embeddings in bulk rather than on-demand

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If you expect very high volume

If you’re embedding millions/billions of documents, API rate limits can become painful. In that case consider:

• Self-hosted open-source embeddings via:
  • sentence-transformers
  • e5
  • bge
  • gte
• Run them on your own GPUs or inference cluster.

This gives you:

• no external API rate limits
• lower marginal cost at scale
• more control over throughput

Tradeoff:

• you own infra, scaling, monitoring, model updates

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Practical recommendation

• For fastest time-to-production: OpenAI, Cohere, or Voyage AI
• For enterprise cloud integration: Azure OpenAI or Vertex AI
• For maximum scale/control: self-host embeddings

If you want, I can also give you:

• a rate-limit-safe embedding pipeline design
• a provider comparison table
• or a sample implementation with retries/batching in Python or Node.js