EMBEDDING API
Vector embeddings, one API call.
Turn text into vectors for RAG, semantic search and clustering. BGE models behind an OpenAI-compatible embeddings endpoint: change the base URL in your pipeline.
Hand this to your agent
Add a Runcrate model to this project: run curl -fsSL"https://runcrate.ai/agents.md?intent=build"and follow it.
Your agent asks for an API key. Create a key
Quick start
Hand it to your agent. Or read the request it sends.
Your agent writes and runs it. The same key works for every model and every GPU.
Available models
Models you can call today. Billed prices, as charged.
| Model | Maker | Price | Detail |
|---|---|---|---|
| BAAI/bge-large-en-v1.5 | BAAI | $0.0105 in, $0 out per 1M tokens | 1024 dims, strong retrieval |
Why Runcrate
Built for production. One key, one balance.
RAG-optimized
BGE models are trained specifically for retrieval tasks. High recall on document retrieval benchmarks like MTEB and BEIR.
Batch processing
Send multiple texts in a single request. Embed entire document collections efficiently with batch support.
OpenAI-compatible
Standard /v1/embeddings endpoint. Works with LangChain, LlamaIndex, and any OpenAI-compatible vector pipeline.
Lightweight models
BGE models are small, so embedding requests are quick and cost little per token. Good for real-time search and retrieval.
FAQ