Step 1: Create a Nebius Account and Add Funds Sign up on the Nebius AI Studio platform and set up billing. Start with a small credit amount that matches your testing budget. Because the platform bills per second rather than per hour, you will not waste money leaving idle resources running on Nebius AI Studio.

Step 2: Choose Managed API or GPUs Decide between two paths. The fastest route is the managed model API, where you call hosted models by name through a familiar HTTP endpoint. If you need custom fine-tuning or full control, go with GPU instances instead. For most developers, starting with the managed API is simpler and cheaper.

Step 3: Call a Managed Model Grab your API key from the dashboard and send your first chat request, specifying a model like Llama 3.1 70B. The response format is OpenAI-compatible, so integrating with existing code requires minimal change. You pay only for the tokens you actually use.

Step 4: Rent a GPU Instance When You Need Power For training or fine-tuning, spin up a GPU instance from the console. Choose the smallest GPU that fits your model to control cost. Run your script, then tear the instance down when done. With per-second billing on Nebius AI Studio, short-lived heavy jobs become surprisingly affordable.

Step 5: Use Kubernetes for Larger Workloads When you manage many jobs, enable Kubernetes integration to orchestrate containers and GPUs automatically. Define your deployment with a GPU request, and Nebius schedules it efficiently. This scales your work without multiplying your engineering overhead.

Step 6: Monitor and Optimize Spending Use the dashboard to track usage and set budget alerts. Compare managed-API cost versus rented GPUs for your workflow and pick whichever is cheaper for your volume. With these practices, Nebius AI Studio keeps your AI ambitions big and your bills small.