Data & Storage · Integration
Pinecone
Add Pinecone to your product for your customers, and give your AI agents governed access to it.
A Pinecone integration lets your product build retrieval on a customer's own vector index rather than a shared one you operate. There are no tables and no SQL here: you upsert vectors with metadata attached into an index, then ask for the nearest k matches to a query vector, narrowing by metadata filter. What makes this interesting for an embedded integration is the namespace, which partitions an index and hands you a per-tenant boundary enforced by the query itself instead of by application code someone has to remember to write. Dimension and embedding model must match the index they were built for, which is configuration per customer. fastn holds each API key per tenant and keeps the client current.
In your product
Embedded for your customers. Per-tenant auth, no per-customer code, maintained by fastn.
Let a customer connect their own index so the embeddings your product generates stay in infrastructure they control.
Write each tenant's vectors into their own namespace so a search cannot cross a customer boundary.
Run top-k similarity search with a metadata filter so retrieval is narrowed before a model ever sees a result.
Record the embedding model and dimension per customer, so an index built with one is never queried with another.
For your AI agents
Governed, audited access for the agents you build, through the MCP server.
An agent retrieves the nearest matches for a question from a single namespace, with the query recorded.
An agent upserts or deletes vectors within the permissions granted, so stale content stops being retrieved.
An agent narrows a search by metadata first, then cites which records it retrieved.
Example prompt
Search this namespace for the passages closest to my question and filter to documents from this year.
Set up Pinecone in 4 steps
- 01Enable the Pinecone connector from your fastn dashboard.
- 02Have each customer authorise their own Pinecone project, so queries run against their index under their credentials.
- 03Map the index, the namespace that scopes each tenant, and the metadata fields you filter on, then confirm your embedding model matches the index dimension.
- 04Call it from your product and expose it to your agents through the same governed connection.
Why teams use the Pinecone integration
What you get by embedding it with fastn instead of building it yourself.
- Ship a Pinecone integration without building it. Your customers connect their own Pinecone account inside your product and work their records, datasets and fields there, with no per-customer code on your side.
- Handle the part that actually costs time: schemas differ per customer and change without notice, and volumes can be large. fastn owns the auth, token refresh, rate limits, pagination and breaking-change fixes, so a Pinecone update is not your on-call problem.
- One integration serves your product and your agents. The same governed Pinecone connection powers in-product features and gives AI agents scoped, audited access, so you read and write your customers' data where it already lives without wiring it twice.
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How do I add a Pinecone integration to my product?
Enable the Pinecone connector in your fastn dashboard, then let each customer authenticate their own Pinecone account. fastn handles the OAuth flow, token storage and refresh per tenant, so there is no Pinecone client code in your app and no per-customer branch in your codebase. Setup is 4 steps.
Do my customers each connect their own Pinecone account?
Yes. Every connection is scoped to the individual customer, so each authorises their own Pinecone account and only ever sees their own records, datasets and fields. That per-tenant isolation is the point of an embedded integration: you support the long tail of customer setups without maintaining an integration per customer.
Can AI agents use this Pinecone integration?
Yes. The same connection is exposed to your agents through the fastn MCP gateway, with permissions scoped per tenant and every call audited. An agent retrieves the nearest matches for a question from a single namespace, with the query recorded.
Who maintains the Pinecone integration?
fastn does. When Pinecone changes an endpoint, deprecates a field or alters its auth, the fix lands in the connector rather than in your backlog, and your customers' connections keep working.
Does the Pinecone integration adapt when a customer's schema changes?
Schema and field mapping is configuration per customer, so a change on their side is a mapping update rather than a code change and a release on yours.
How are large Pinecone reads handled?
Pagination and throttling are handled for you, and initial backfills are rate-limited so a large import does not exhaust a customer's API allowance.
What can I build with the Pinecone integration?
A common starting point: let a customer connect their own index so the embeddings your product generates stay in infrastructure they control. Teams also use it for the other use cases listed above, and expose it to agents for governed reads and writes.
How much does the Pinecone integration cost?
It is included. Pricing is based on connected accounts, not on how many connectors you enable, so adding Pinecone does not change your per-connector cost. You can start free with 3 connected accounts.
Add Pinecone to your product
Start free with 3 connected accounts. No sales call required, and no per-customer integration code.