Data & Storage · Integration
Qdrant
Add Qdrant to your product for your customers, and give your AI agents governed access to it.
A Qdrant integration lets your product run retrieval against a vector database the customer administers themselves, whether that is a self-hosted cluster or Qdrant Cloud. The unit of storage is a point: an id, one or more vectors, and a payload of ordinary JSON you can filter on, all held in a collection. Filtering is the part worth designing around, because a payload filter is applied as part of the search rather than after it, so a query can be narrowed to one team, one document set or one date range before any distance is scored. Vector size and distance metric are fixed when a collection is created, and a collection can carry several named vectors, so which vector a query targets is configuration rather than a guess. fastn holds each cluster's URL and 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 each customer connect their own cluster, self-hosted or on Qdrant Cloud, so vectors your product builds never leave storage they administer.
Upsert points with a payload attached, then search for nearest neighbours with a payload filter applied during the search rather than after it.
Give each tenant its own collection, or a payload field every filter must match, so one customer's points cannot be returned to another.
Record each collection's vector size, distance metric and named vectors, because those are fixed at creation and a mismatched embedding fails at query time.
For your AI agents
Governed, audited access for the agents you build, through the MCP server.
An agent retrieves the closest points to a question from one collection, with the filter it applied recorded.
An agent upserts or deletes points within the permissions granted, so withdrawn content stops coming back in results.
An agent reads a collection's configuration before searching, so it queries the vector the collection was actually built for.
Example prompt
Which points in this collection are nearest my question, and what payload came back with each of them?
Set up Qdrant in 4 steps
- 01Enable the Qdrant connector from your fastn dashboard.
- 02Have each customer supply their own cluster URL and API key, whether the cluster is self-hosted or on Qdrant Cloud.
- 03Map the collections you use, the payload fields you filter on and any named vectors, then confirm your embedding model matches the collection's vector size and distance metric.
- 04Call it from your product and expose it to your agents through the same governed connection.
Why teams use the Qdrant integration
What you get by embedding it with fastn instead of building it yourself.
- Ship a Qdrant integration without building it. Your customers connect their own Qdrant 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 Qdrant update is not your on-call problem.
- One integration serves your product and your agents. The same governed Qdrant 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.
Used by these teams
Compare with
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Hugging FaceQdrant integration FAQ
How do I add a Qdrant integration to my product?
Enable the Qdrant connector in your fastn dashboard, then let each customer authenticate their own Qdrant account. fastn handles the OAuth flow, token storage and refresh per tenant, so there is no Qdrant client code in your app and no per-customer branch in your codebase. Setup is 4 steps.
Do my customers each connect their own Qdrant account?
Yes. Every connection is scoped to the individual customer, so each authorises their own Qdrant 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 Qdrant 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 closest points to a question from one collection, with the filter it applied recorded.
Who maintains the Qdrant integration?
fastn does. When Qdrant 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 Qdrant 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 Qdrant 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 Qdrant integration?
A common starting point: let each customer connect their own cluster, self-hosted or on Qdrant Cloud, so vectors your product builds never leave storage they administer. 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 Qdrant integration cost?
It is included. Pricing is based on connected accounts, not on how many connectors you enable, so adding Qdrant does not change your per-connector cost. You can start free with 3 connected accounts.
Add Qdrant to your product
Start free with 3 connected accounts. No sales call required, and no per-customer integration code.