Data & Storage · Integration
Databox
Add Databox to your product for your customers, and give your AI agents governed access to it.
Databox splits its API in two: a push endpoint at push.databox.com that accepts custom metric records using HTTP Basic auth where the token is the username and the password is empty, and a separate read API for metrics and data sources. The push call needs an explicit Accept header naming the API version, and requests without it are treated differently from ones that name vnd.databox.v2+json. Each pushed record is a key, a value and an optional date, plus attributes prefixed to act as dimensions, so a metric's shape is defined implicitly by the first records you send rather than by any schema you declare up front. Backdating works, but sending the same key and date again overwrites rather than appends, which surprises people building retry logic. fastn holds each customer's push token, keeps the version header correct and handles upkeep as Databox revises its endpoints.
In your product
Embedded for your customers. Per-tenant auth, no per-customer code, maintained by fastn.
Let a customer connect their own Databox account and choose which of your product's metrics get pushed to it
Push usage, revenue or engagement numbers from your app into a customer's existing Databox dashboards nightly
Read back a customer's connected data sources so your onboarding can show what they already track
Backfill historical metric records when a customer first connects, without duplicating existing dates
For your AI agents
Governed, audited access for the agents you build, through the MCP server.
Let an agent push a corrected metric value for a specific date after a data fix, keeping the metric key stable
Have an agent read a customer's metrics and explain which numbers moved week on week
Audit, per tenant, every metric key an agent has written to Databox and the dates it overwrote
Example prompt
Which metrics did we push to Databox for this customer last week, and did any dates get overwritten?
Set up Databox in 4 steps
- 01Enable the Databox connector from your fastn dashboard.
- 02Have each customer authorise their own Databox account, so calls run under their credentials rather than a shared key.
- 03Decide which records, datasets and fields your product needs, map those fields, then enable the actions and triggers you want.
- 04Call it from your product and expose it to your agents through the same governed connection.
Why teams use the Databox integration
What you get by embedding it with fastn instead of building it yourself.
- Ship a Databox integration without building it. Your customers connect their own Databox 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 Databox update is not your on-call problem.
- One integration serves your product and your agents. The same governed Databox 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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Hugging FaceDatabox integration FAQ
How do I add a Databox integration to my product?
Enable the Databox connector in your fastn dashboard, then let each customer authenticate their own Databox account. fastn handles the OAuth flow, token storage and refresh per tenant, so there is no Databox client code in your app and no per-customer branch in your codebase. Setup is 4 steps.
Do my customers each connect their own Databox account?
Yes. Every connection is scoped to the individual customer, so each authorises their own Databox 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 Databox integration?
Yes. The same connection is exposed to your agents through the fastn MCP gateway, with permissions scoped per tenant and every call audited. Let an agent push a corrected metric value for a specific date after a data fix, keeping the metric key stable
Who maintains the Databox integration?
fastn does. When Databox 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 Databox 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 Databox 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 Databox integration?
A common starting point: let a customer connect their own Databox account and choose which of your product's metrics get pushed to it. 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 Databox integration cost?
It is included. Pricing is based on connected accounts, not on how many connectors you enable, so adding Databox does not change your per-connector cost. You can start free with 3 connected accounts.
Add Databox to your product
Start free with 3 connected accounts. No sales call required, and no per-customer integration code.