Use the Embedding API via RunAPI with a model skill, unified auth, and pay-as-you-go pricing.
# Base URL
https://runapi.ai
# Endpoints
POST /v1/embeddings
curl https://runapi.ai/v1/chat/completions \
-H "Authorization: Bearer $RUNAPI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "text-embedding-ada-002",
"input": [
"Embed these support articles and a customer query so I can rank the closest matches."
],
"encoding_format": "float"
}'from openai import OpenAI
client = OpenAI(
base_url="https://runapi.ai/v1",
api_key="your-runapi-key"
)
response = client.chat.completions.create(
model="text-embedding-ada-002",
messages=[{"role": "user", "content": "Embed these support articles and a customer query so I can rank the closest matches."}]
)import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://runapi.ai/v1",
apiKey: "your-runapi-key"
});
const response = await client.chat.completions.create({
model: "text-embedding-ada-002",
messages: [{ role: "user", content: "Embed these support articles and a customer query so I can rank the closest matches." }]
});OpenAI Embedding models convert text into dense vectors for semantic search, retrieval-augmented generation, clustering, classification, and ranking. The text-embedding-3 family supports efficient production retrieval with optional vector dimensionality control.
| Endpoint | Protocol |
|---|---|
POST /v1/embeddings | OpenAI Embeddings |
| Endpoint | Resolution | Duration | Price | |
|---|---|---|---|---|
| embedding | — | — | Input $0.10 / 1M tokens | Output Free / 1M tokens |
Prices shown for text-embedding-3-large. Other variants retain their own endpoint pricing.
Sign up and generate an API key from the dashboard.
Choose the version that best matches your quality and cost requirements.
Submit your prompt and parameters to the model's documented endpoint.
Poll the task or set a webhook, then download the completed output.
OpenAI Embedding models expose the standard OpenAI Embeddings API through RunAPI, so vector search and RAG pipelines can use the same RunAPI key as GPT chat and Responses traffic.
A single RunAPI API key unlocks the whole model catalog across all providers. No separate accounts to create, no API keys to rotate per integration, and no credential management overhead. Add a new model to your app by changing one parameter.
Per-call pricing in USD, billed monthly into a single invoice. No subscription tiers, no minimum spend, and failed generations are never charged. The pricing page and check_pricing API show exact costs before you commit to a model.
Typed schemas, parameter constraints, and setup notes are packaged in the model skill so your implementation starts from the right contract. The skill loads into Claude Code, Codex, Gemini CLI, Cursor, and VS Code — your agent knows the correct request shape before you write a line of code.
Use text-embedding-3-small for efficient production retrieval and text-embedding-3-large when recall quality matters more than vector size.
Yes — pass the dimensions parameter with text-embedding-3-large or text-embedding-3-small when you need smaller vectors.
Yes — /v1/embeddings returns an OpenAI-compatible list response with embedding data, model, and usage.
No — embedding models are only available on /v1/embeddings.
Use a string, an array of strings, or token arrays following the OpenAI Embeddings API format.
Pick the cheapest variant that meets your quality bar. Most teams start on the fast variant and graduate to pro for production.