Hosted embeddings models

Embeddings models convert text into dense vector representations that capture semantic meaning. This enables systems to compare texts by numerical closeness rather than exact keyword matches—solving problems such as semantic search (finding relevant documents from a query like "cheapest flights to Tokyo" without matching the words "cheap" or "Tokyo"), recommendation clustering, and retrieval-augmented generation (RAG) for large language models. The model intfloat/multilingual-e5-large-instruct, for instance, supports over 100 languages, making it suitable for global search and cross-lingual applications.

In production, embeddings are typically generated for a corpus at indexing time, stored in a vector database (e.g., Pinecone, Qdrant), and queried in real time by embedding the search input and performing approximate nearest neighbor (ANN) search. Choosing between models involves a trade-off among size (latency and memory), quality (retrieval accuracy on domain-specific benchmarks), and speed. Larger models like multilingual-e5-large-instruct offer higher accuracy but require more compute, while smaller or distilled variants trade some quality for faster inference—a decision best guided by your recall requirements and throughput budget.

Calling a hosted API eliminates the infrastructure burden of managing GPU instances, scaling under variable load, and keeping model versions updated—making it the pragmatic choice for most call volumes below millions of requests per month.