AI & Models · Integration
Deep-Image.ai
Add Deep-Image.ai to your product for your customers, and give your AI agents governed access to it.
Deep-Image.ai takes work through POST calls to deep-image.ai/rest_api/process_result with the key in an x-api-key header, and the same endpoint behaves both ways: small jobs come back with a finished result URL, larger ones return a job hash with a processing status that you poll on the same path. The request body names a source image by URL or base64 and then chains operations such as upscaling, background removal, denoise and light or colour correction in one payload, so ordering the operations wrongly changes the output rather than erroring. Output URLs are temporary, which means anything you want to keep has to be fetched and stored on your side before the link expires. Requests are billed per processed image regardless of whether you download the result. fastn holds each customer's key, manages the polling loop and keeps the integration working as the operation set changes.
In your product
Embedded for your customers. Per-tenant auth, no per-customer code, maintained by fastn.
Let a customer connect their own Deep-Image.ai key so image processing is billed to their account
Upscale product photographs a customer uploads before they are published to a storefront
Strip backgrounds from user-supplied images and store the transparent output in your own asset library
Run a batch enhancement pass over a customer's existing image set and report which files changed
For your AI agents
Governed, audited access for the agents you build, through the MCP server.
Let an agent apply a fixed enhancement recipe to newly uploaded images and store the output before the URL expires
Have an agent retry only the jobs that came back with a failed status rather than reprocessing a whole batch
Audit, per tenant, every image an agent submitted and which operations were applied to it
Example prompt
How many images did we run through Deep-Image.ai for this customer this month, and how many failed?
Set up Deep-Image.ai in 4 steps
- 01Enable the Deep-Image.ai connector from your fastn dashboard.
- 02Have each customer authorise their own Deep-Image.ai account, so calls run under their credentials rather than a shared key.
- 03Decide which models, requests and outputs 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 Deep-Image.ai integration
What you get by embedding it with fastn instead of building it yourself.
- Ship a Deep-Image.ai integration without building it. Your customers connect their own Deep-Image.ai account inside your product and work their models, requests and outputs there, with no per-customer code on your side.
- Handle the part that actually costs time: per-customer keys, quotas and cost attribution matter more than schema here, because every call is billed. fastn owns the auth, token refresh, rate limits, pagination and breaking-change fixes, so a Deep-Image.ai update is not your on-call problem.
- One integration serves your product and your agents. The same governed Deep-Image.ai connection powers in-product features and gives AI agents scoped, audited access, so you give your product and your agents a model call each customer pays for themselves without wiring it twice.
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AmplitudeDeep-Image.ai integration FAQ
How do I add a Deep-Image.ai integration to my product?
Enable the Deep-Image.ai connector in your fastn dashboard, then let each customer authenticate their own Deep-Image.ai account. fastn handles the OAuth flow, token storage and refresh per tenant, so there is no Deep-Image.ai client code in your app and no per-customer branch in your codebase. Setup is 4 steps.
Do my customers each connect their own Deep-Image.ai account?
Yes. Every connection is scoped to the individual customer, so each authorises their own Deep-Image.ai account and only ever sees their own models, requests and outputs. 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 Deep-Image.ai 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 apply a fixed enhancement recipe to newly uploaded images and store the output before the URL expires
Who maintains the Deep-Image.ai integration?
fastn does. When Deep-Image.ai 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.
Whose Deep-Image.ai API key and quota does each call use?
Each customer authorises their own Deep-Image.ai account, so usage, rate limits and cost land on the customer that caused them. You are not metering a shared key and re-billing it, and one heavy customer cannot exhaust another's quota.
Are inputs and outputs auditable?
Yes. Every call is logged per tenant with the call, the input and the result, so an output can be traced back to what produced it. That matters more here than in most integrations, because a generated answer or an extracted field cannot be reconstructed from the request alone.
What can I build with the Deep-Image.ai integration?
A common starting point: let a customer connect their own Deep-Image.ai key so image processing is billed to their account. 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 Deep-Image.ai integration cost?
It is included. Pricing is based on connected accounts, not on how many connectors you enable, so adding Deep-Image.ai does not change your per-connector cost. You can start free with 3 connected accounts.
Add Deep-Image.ai to your product
Start free with 3 connected accounts. No sales call required, and no per-customer integration code.