Hosted embeddings models

Embedding models convert text into dense numerical vectors that capture semantic meaning. This enables systems to compare, search, and cluster content based on conceptual similarity rather than exact keyword matches. Common production use cases include semantic search (finding relevant documents from a query), retrieval-augmented generation (RAG) for grounding LLM outputs, recommendation engines, and anomaly detection in text streams. For example, a customer support platform can embed support tickets and knowledge base articles to surface the most relevant answer for each incoming issue.

In production, embeddings are typically generated at ingestion time and stored in a vector database. At query time, the same model converts the user’s input into a vector, and a nearest-neighbor search retrieves the most similar stored vectors. The choice of model involves a trade-off between vector quality, inference speed, and model size. Larger models (e.g., 300M parameters like google/embeddinggemma-300m) often yield higher accuracy on nuanced tasks but require more compute and latency, while smaller models are faster and cheaper for high-throughput pipelines. The optimal choice depends on the precision requirements and latency budget of the application.

For most call volumes, using a hosted API eliminates the operational overhead of maintaining GPU infrastructure, scaling under load, and updating model versions—while still providing OpenAI-compatible endpoints that integrate with existing codebases.