Some links here are affiliate links — if you buy through them we may earn a commission at no extra cost to you. It never changes what we recommend. Full disclosure.
Disclosure: This article contains affiliate links. If you sign up through them, we may earn a commission at no extra cost to you — thanks for supporting the site.
The intimidating part of "AI infrastructure" usually isn't the AI — it's the infrastructure. The good news with serverless inference is that there isn't much infrastructure to set up in the first place. If you can send an HTTP request, you can be calling a model within a few minutes.
Why This Matters
A lot of people delay building their first AI feature because they picture a whole deployment pipeline: provisioning a GPU, installing drivers, downloading model weights, exposing a port, and setting up monitoring. Serverless inference removes nearly all of that. You're really doing three things: generating a credential, choosing a model, and sending a request.
That simplicity matters because it lowers the cost of experimenting. You can try three different models for the same task in an afternoon instead of provisioning three separate servers.
A Simple Framework
- Create a model access key in the control panel — this is your credential for calling the inference API
- Choose an API style that fits your use case: Chat Completions for existing chat-style code, or the newer Responses API for new integrations and multi-step tool use
- Pick a foundation model appropriate to the task (don't over-provision for a simple job)
- Send a test request and confirm you're getting a response back
- Wire it into your app, keeping your access key out of client-side code
Example
Before: A developer wants to add a "summarize this ticket" button to their support tool. They spend a day researching self-hosted model options, GPU sizing, and Docker images before writing a single line of feature code.
After: They create a model access key, send a summarization prompt to a serverless inference endpoint using the Chat Completions API, and have a working prototype in under an hour — no server provisioning involved.
> Tip: Test in the model playground in the control panel before you write any code. It's the fastest way to confirm a model actually produces the kind of output you want, before you've invested time wiring up the API.
Common Mistakes
- Hardcoding the access key directly into client-side or front-end code
- Skipping the playground step and debugging prompt issues inside application code instead
- Choosing the Chat Completions API out of habit when the Responses API better fits a new project
- Forgetting that serverless inference is prepaid — requests stop working if your balance hits zero
For the exact setup steps, DigitalOcean's Inference Quickstart guide walks through creating a key and testing it in the Model Playground.
> (ad) GPU bills piling up for AI features you barely use? DigitalOcean Serverless Inference is pay-per-token, no idle infrastructure, no minimums. Ship the feature, not the server bill.
Once you've made that first successful call, the rest of this series builds on exactly this setup — the same access key and endpoint, just used more efficiently as your traffic grows.