BonrockBuilding an AI Application With Next.js
Next.js can keep a React interface and model-facing routes in one project. Streaming, credentials and long-running jobs still need explicit design.
A document assistant needs more than a model call. It needs sign-in, a place to upload a file, a way to show progress and a screen for checking the answer against its sources. For a team already using React, keeping those pieces in one Next.js application can save a separate frontend and backend deployment.
That convenience is the reason to consider it. Streaming is useful, but it is a web capability available in other frameworks too.
Put the model call behind a server boundary
A browser should not receive the application's model-provider key. In a Next.js application, a server-side route can authenticate the request, check access to the document and call the provider. The browser receives the response it is allowed to see.
Server Components can load initial data and render parts of the page. Interactive components handle input and display updates. A Route Handler is a useful boundary for a streamed answer: it accepts a request and returns a response using the web Request and Response interfaces.
Keep that boundary explicit. Sharing a repository does not make every module safe to import into client code, and a hidden API key does not by itself protect an endpoint from unauthorized use.
Show progress that means something
Streaming lets the reader see output before the response is complete. For document analysis, it can also be useful to show that the file was received, extraction finished and source passages were found. Those events tell the user whether the request is progressing or waiting.
Do not confuse a running progress indicator with completed work. If retrieval fails, say that. If the connection closes, keep enough task state to determine whether the work failed or is still running. Repeating a request blindly can create duplicate charges or actions.
An SDK can help manage the stream and UI state. Check its version and the provider features it supports. The application still owns authentication, cancellation, error handling and what gets saved.
A stream has a time limit too
A hosting platform can terminate a function while it is streaming. Vercel's duration documentation counts elapsed time for the invocation, including time spent waiting for a streamed response. Limits depend on configuration and plan.
Before shipping, test the slowest expected request on the actual deployment. Set provider timeouts, handle client disconnects and check proxy buffering. A stream that works locally can behave differently behind the production network.
Jobs involving large uploads or many tool calls may belong in a durable worker. The web route accepts the job, returns an identifier and lets the page follow its progress. That adds infrastructure, but gives the job a lifetime separate from one browser connection.
Share the code that benefits from being shared
A single TypeScript project can share data types, validation and interface components. That is helpful while the product is changing quickly. Keep provider-specific calls behind a small module and give the job logic a boundary that can move to a worker later.
A separate backend can be the simpler choice when the work depends on Python libraries, serves several clients or has little user interface. There is no benefit in carrying a React framework just to expose an API.
For an AI application with substantial screens and modest request durations, Next.js is a reasonable starting point. Prove the deployment path with one complete task: authenticate, load permitted data, stream a response, record the outcome and recover from a failed call. Then build the next feature on a path you have already tested.
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