Choose an embedding API by the retrieval or similarity task it must support. Use price to narrow candidates, then evaluate the corpus and query behavior that determine whether the application finds the right evidence.
What matters for embedding apis
OpenAI’s embedding guide describes vector representations used for tasks such as search and similarity. Official documentation.
Start with a corpus fixture and representative queries, including ambiguous wording and a query whose answer is absent. Define what a relevant result looks like before inspecting scores. A vector similarity value is useful only within the retrieval design and evaluation that give it meaning.
Keep document preparation consistent. Chunk boundaries, titles and metadata can change retrieval even when the embedding model is unchanged. Preserve the same preprocessing when comparing candidates, then evaluate preprocessing changes separately. Otherwise the comparison cannot isolate the model choice.
Plan the indexing and query path together. Record model identifier, vector settings and task-specific input conventions. If a model is changed, decide how the stored corpus is rebuilt and how the application avoids mixing incompatible representations during transition.
Ranked candidates
Only active candidates with documented values for this ranking are included. Prices retain the tier and deployment condition shown below.
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The input-price ordering is a starting shortlist. Inspect the supported input types and model configuration, then evaluate retrieval quality. A cheaper vector is not useful when it repeatedly fails to retrieve the evidence the answer stage needs.
Our three picks
Find your starting point
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The cheapest role should pass the corpus retrieval fixture. Balanced can be a candidate with suitable documented features and manageable indexing cost. Strongest needs a relevant retrieval evaluation; a text-generation benchmark does not establish embedding quality.
Keep retrieval outcomes separate from the final answer model. A fluent answer can hide a poor retrieval stage, while a careful generator can still fail when the necessary passage never arrives. Inspect relevant-result coverage and the final supported answer as distinct checks.
When each pick is wrong
A low-input-rate candidate is wrong when poor retrieval creates repeated searches or unsupported answers. A candidate with a larger representation is not automatically better if storage, search latency and observed relevance do not improve the application.
Gemini’s embedding documentation uses model-specific task conventions; the current families do not all accept the same task_type field. Official documentation.
A migration is wrong when it changes model conventions without rebuilding or validating the corpus path. Keep versioned indexes and a rollback plan that respects the representation used by stored vectors.
Compare OpenAI embedding models, Gemini embeddings and Model Studio retrieval models with their official configuration guidance.
Estimate cost
Estimate your API costs
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Use the calculator for the input workload and retain indexing, query and update assumptions separately.
Distinguish the initial corpus build from recurring query and document-update traffic. Include any reranking and answer-generation stages in the whole application estimate, while keeping the embedding component visible enough to evaluate independently.
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Frequently asked questions
What should an embedding evaluation measure?
Can chunking affect the comparison?
Is a text-generation benchmark an embedding benchmark?
Should indexing and query conventions match?
What happens when the embedding model changes?
What should the budget separate?
Sources
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