DigitalOcean GPU Droplet Deployment for Client AI Workloads (Delivery)

DigitalOcean GPU Droplet Deployment for Client AI Workloads (Delivery)

1. 01

   Create a new GPU Droplet from the DigitalOcean control panel

   Navigate to the Droplets section and select a GPU Droplet plan. For production client workloads, choose the NVIDIA H200 or B300 reserved plan if the client commits to 12 months, otherwise use hourly pricing for flexibility.

2. 02

   Configure the Droplet with a snapshot or marketplace image

   Select an image that includes the necessary AI runtime. If you have a custom image from a previous client project, use it to reduce setup time. Otherwise, pick a base Ubuntu image and install the required drivers and libraries.

3. 03

   Set up a serverless inference endpoint via the Inference Engine

   In the AI/ML section, create a new inference endpoint. Choose an open-weight model from the 80+ available, and enable policy-driven routing to manage costs and latency for client traffic.

4. 04

   Integrate the endpoint with a managed database

   Provision a managed PostgreSQL or MongoDB instance from the Databases section. Connect it to your inference endpoint to store client data, chat history, or knowledge base vectors.

5. 05

   Deploy the agent runtime using LangGraph or CrewAI

   Use the managed agent runtime to deploy your client's agent. Configure the runtime to use the inference endpoint and database, and set up any required knowledge base from the Knowledge Bases section.

6. 06

   Test the OpenAI-compatible endpoint

   Use the unified API/CLI to send a test request to the endpoint, verifying that it responds correctly and that the routing policy is working as expected.

7. 07

   Set up monitoring and alerts

   Configure monitoring for the Droplet and inference endpoint. Set up alerts for CPU, GPU, and memory usage, as well as for endpoint latency and error rates, to ensure client workloads stay healthy.

8. 08

   Document the deployment for the client handoff

   Create a runbook that includes the Droplet ID, endpoint URL, database connection strings, and any custom configurations. Provide this to the client or your delivery team for ongoing management.