Core Concepts

Datasets

A dataset is a collection of vector-indexed content published by a seller. Each dataset has:

Name and description — what the data contains
Price per chunk — cost per result returned (e.g., $0.001)
Visibility — public (anyone can query) or private (requires seller-granted access)
Metadata schema — describes what fields are available for filtering
Embedding model — which model was used to vectorize the data (e.g., text-embedding-3-small)

Connectors are seller-side credentials linking a dataset to a vector database. Supported providers:

As a buyer, you never interact with connectors directly. The seller configures them, and Datagate routes queries to the right vector DB.

When you call query():

Access check — Datagate verifies you can access the requested datasets (public = always, private = need access grant)
Balance check — estimates max cost (topK × price_per_chunk per dataset) and checks your balance
Embedding — converts your text query into vector(s) using each dataset’s embedding model
Fan-out — searches each dataset’s vector DB in parallel
Merge & rank — combines results across datasets, ranked by relevance score
Billing — charges per chunk actually returned (not estimated cost)

Same embedding model across all datasets → embed once, merge by raw similarity score
Different embedding models → embed per-model in parallel, merge using Reciprocal Rank Fusion (RRF) since raw scores from different models aren’t comparable

You don’t need to handle this — it’s automatic. Just send text and dataset IDs.

Per-chunk billing: you pay price_per_chunk for each result returned, plus a small platform fee
Deposits: add funds via Stripe Checkout
Auto-deposit: configure a threshold — when your balance drops below it, auto-refill from your saved payment method