Data & Storage · Integration
JDBC
Add JDBC to your product for your customers, and give your AI agents governed access to it.
Embed a JDBC integration so your customers can point your product at their own relational database instead of exporting files to you. Schemas differ per customer and change without notice, so table and column mapping is configuration rather than code and a change on their side does not need a release on yours. fastn handles per-tenant credentials, connection management, pagination, and throttling, so a large read does not overwhelm a customer's database.
In your product
Embedded for your customers. Per-tenant auth, no per-customer code, maintained by fastn.
Let customers connect their own database so your product reads the tables it needs with no export step.
Read table and column metadata at runtime so your product adapts to each customer's schema.
Run parameterised queries per tenant and page through large result sets without custom code.
Write rows back into a customer's table when a record changes in your product.
For your AI agents
Governed, audited access for the agents you build, through the MCP server.
An agent queries a customer's database to ground an answer in their own data.
An agent inspects a schema before it writes, so it maps to the columns that actually exist.
An agent writes or updates rows within governed permissions, with every statement logged per tenant.
Example prompt
List the tables in this schema, then show me rows added to the orders table in the last 24 hours.
Set up JDBC in 4 steps
- 01Open the JDBC connector from your fastn dashboard.
- 02Have each customer supply their own connection details and least-privilege database credentials.
- 03Map the schemas, tables, and columns your product may read or write, then enable actions.
- 04Call them from your product, or expose them to an agent through the MCP server.
Why teams use the JDBC integration
What you get by embedding it with fastn instead of building it yourself.
- Ship a JDBC integration without building it. Your customers point it at their own endpoint and credentials and work their records, datasets and fields through it, 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 JDBC update is not your on-call problem.
- One integration serves your product and your agents. The same governed JDBC 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
PostgreSQL
Snowflake
MongoDBOften used alongside
Tools the same teams tend to run next to JDBC, across other categories.
Anthropic Claude
OpenAI
Azure OpenAI
Hugging FaceJDBC integration FAQ
How do I add a JDBC integration to my product?
Enable the JDBC connector in your fastn dashboard, then let each customer authenticate their own JDBC account. fastn handles the OAuth flow, token storage and refresh per tenant, so there is no JDBC client code in your app and no per-customer branch in your codebase. Setup is 4 steps.
How is a JDBC connection configured per customer?
JDBC is a specification rather than a service you sign up for, so there is no JDBC account. Each customer supplies their own endpoint and credentials, and that connection is scoped to them, so they only ever reach their own records, datasets and fields. The per-tenant isolation works the same way it does for a vendor product.
Can AI agents use this JDBC 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 queries a customer's database to ground an answer in their own data.
Who maintains the JDBC integration?
fastn does. When JDBC 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 JDBC 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 JDBC 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 JDBC integration?
A common starting point: connect their own database so your product reads the tables it needs with no export step. 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 JDBC integration cost?
It is included. Pricing is based on connected accounts, not on how many connectors you enable, so adding JDBC does not change your per-connector cost. You can start free with 3 connected accounts.
Add JDBC to your product
Start free with 3 connected accounts. No sales call required, and no per-customer integration code.