How do I choose between different LLM APIs for embeddings and semantic search?

A good way to choose is to treat embeddings + semantic search as a product decision, not just a model decision. The best API depends on your data, latency/cost goals, and how much control you need over retrieval quality.

1) Start with your use case

Different needs point to different APIs:

• General semantic search / RAG over text
  Pick a strong, cheap embedding model with stable dimensions and good retrieval quality.
• Multilingual search
  Prefer models known to work well across languages.
• Code search / technical docs
  Use models with good performance on code and mixed text.
• High-stakes retrieval
  Prioritize recall/precision, consistency, and evaluation tooling over raw cost.
• Very large scale
  Cost per 1M tokens/text chunks, batch support, and index size matter a lot.

2) Compare on the right dimensions

A. Retrieval quality

This is the most important factor. Evaluate:

• Recall@k: does the right document show up in the top results?
• MRR / nDCG: how high does the best match rank?
• Domain fit: does it work on your own data, not just public benchmarks?

A model that scores well on generic benchmarks may underperform on your domain.

B. Embedding dimensionality

Lower dimensions:

• smaller index
• faster ANN search
• lower storage cost

Higher dimensions:

• sometimes better retrieval quality
• higher memory cost

Choose based on scale and quality tradeoff.

C. Cost

Consider:

• embedding generation cost
• re-embedding cost when models change
• vector DB storage and query costs
• reranking costs if you use a second-stage model

At scale, the cheapest API can become expensive if it requires more chunks or more reranking.

D. Latency and throughput

Look at:

• embedding API latency
• batch embedding support
• rate limits
• query-time vector search speed

If you need real-time search, latency matters as much as quality.

E. Consistency and versioning

Important questions:

• Does the provider keep embeddings stable across versions?
• Can you pin a model version?
• How often do embeddings change?
• What is the migration path if the model is deprecated?

A “better” model can still be risky if it forces frequent re-indexing.

F. Ecosystem and tooling

Consider:

• easy SDKs
• support for batching
• integration with your vector database
• reranking models
• hybrid search support
• observability and evaluation tools

3) Don’t rely on embeddings alone

For semantic search, the best systems often use:

• Hybrid retrieval: keyword/BM25 + embeddings
• Reranking: retrieve top 50–200, then rerank with a cross-encoder or LLM
• Metadata filtering: use filters for date, author, tenant, permissions, etc.

This often matters more than which embedding API you choose.

4) Build a small benchmark on your own data

The most practical approach is to test 2–4 APIs on a representative set:

1. Collect ~50–200 real queries.
2. Create relevance judgments or use click logs.
3. Compare:
   • top-1 accuracy
   • recall@5 / recall@10
   • latency
   • cost per query
4. Test:
   • short queries
   • long queries
   • multilingual queries
   • noisy queries

If you can’t label data, use proxy tests:

• known question-answer pairs
• document duplicates
• search logs
• human review of top results

5) A simple decision guide

Choose an embedding API that is:

• best quality if retrieval accuracy is the priority
• lowest cost if you have huge volume and can tolerate minor quality loss
• most stable/versionable if reindexing is expensive
• multilingual/specialized if your domain demands it
• fastest/batch-friendly if throughput is the bottleneck

6) Common strategy

A strong default architecture is:

1. Use a solid embedding model for candidate retrieval
2. Store vectors in your preferred vector DB
3. Use hybrid search if keyword relevance matters
4. Rerank top results with a stronger model
5. Measure with your own queries and iterate

This usually beats picking the “best” embedding API in isolation.

7) Questions to ask vendors

Before committing, ask:

• What is the embedding dimension?
• Is the model multilingual?
• Can I pin a version?
• What are the rate limits?
• Is batching supported?
• What is the pricing per input token/chunk?
• How are updates handled?
• Do you support hybrid retrieval or reranking?
• What evaluation benchmarks do you provide?

Bottom line

If you want the safest choice: test a few APIs on your own data, optimize for retrieval quality first, then cost/latency, and use hybrid search + reranking when possible.

If you want, I can also give you:

• a comparison checklist
• a benchmarking template
• or a decision matrix for popular embedding APIs.