Deploying an AI app means putting a model-backed service into production reliably and affordably. The core patterns: separate the model-serving layer…
Choose where the model runs, and see what changes.
Your app can get its model two ways. Calling a hosted API means the provider runs the model; you send requests and pay per token, with no infrastructure to manage and instant access to top models — but ongoing per-use cost and less control. Self-hosting means running an open model on your own GPUs, giving control, privacy, and cheaper high-volume inference, at the cost of operating the serving stack.
The choice shapes cost and scaling, but not the surrounding architecture: the patterns for building a reliable service around the model are the same either way.
Split the model-serving layer from the application layer.
A durable pattern is to keep the model-serving layer separate from your application logic. The app handles routing, business rules, auth, and orchestration; a distinct serving layer (a hosted API or your own GPU service) runs the model. They talk over a network boundary.
This separation lets each scale on its own — the GPU-hungry serving layer independently of the lightweight app layer — and lets you swap the model or provider without rewriting the app. It also isolates failures and makes the expensive GPU resources easier to manage and share.
Deal with slow generations without blocking users or servers.
LLM generations can take seconds, which strains a normal request-response service. For interactive use, stream tokens so the user sees output immediately. For long or batch jobs, go asynchronous: accept the request, put it on a queue, process it with background workers, and let the client poll or receive a callback when done — so no web request is held open for minutes.
Keep the app stateless — no per-request data stored in an instance's memory — so any instance can handle any request and you can add or remove instances freely. Put shared state (sessions, job status) in a database or cache.
Scale to demand, release safely, and avoid the common errors.
Because a stateless app scales horizontally, autoscale it to demand — but GPU serving scales differently: GPU instances are expensive and slower to start, so plan capacity, use queues to smooth spikes, and scale serving deliberately. For releases, use a canary or staged rollout: send a small slice of traffic to the new model or prompt, watch quality and cost, and expand only if it holds.
Keep the ability to roll back fast, since a model or prompt change can shift behavior in ways tests miss.
Watch for: holding a web request open for a long generation instead of streaming or queuing (it ties up servers and times out); storing session state in app memory so instances can't scale; treating GPU serving like a cheap stateless web tier (it isn't — plan capacity and cost); and shipping a model or prompt change to everyone at once with no canary or rollback.
Deploying an AI app uses steady patterns regardless of whether you call a hosted API or self-host on GPUs. Separate the model-serving layer from application logic so each scales independently and the model is swappable. Keep the app stateless for horizontal scaling, and handle slow generations by streaming or async queues rather than holding requests open. Autoscale the app, plan GPU-serving capacity deliberately, and release with canaries and fast rollback since model changes shift behavior.
Design the deployment for an app that generates long reports from prompts. Decide hosted vs self-hosted, how you'd handle the multi-second generations without timing out, how you'd keep it scalable, and how you'd roll out a new prompt safely.
What is the difference between API-based and self-hosted AI deployment?
The choice shifts cost, control, and privacy, but the surrounding deployment patterns are the same for both.
Why separate the model-serving layer from the application logic?
Separating layers lets the GPU-heavy serving scale apart from the lightweight app, isolates failures, and decouples the model from app code.
How should a service handle a long-running generation?
Streaming or async queues avoid tying up servers and timing out; keeping the app stateless lets it scale to handle load.
What is a canary release for an AI app?
Because model and prompt changes can shift behavior unpredictably, a canary limits blast radius and rollback keeps you safe.