Leveraging DigitalOcean's Serverless Inference for Efficient Coding with the Inference Router

Frontier models such as Claude Opus 4.8 can enhance coding processes across various languages, but their cost can quickly become prohibitive at scale. DigitalOcean’s Inference Router provides a solution by directing tasks to smaller, cost-effective open-source models and only using advanced models when necessary. This was demonstrated by building a complete Godot 4 game, PK Shootout, using OpenCode. In this project, OpenCode executed 596 tasks over 83 interactions, consuming around 4.1 million tokens and costing approximately $8.25. Most tasks were handled by MiMo V2.5 and GLM-5.2, with only two tasks requiring fallback to more expensive models. The same project would have cost an estimated $123 using a frontier model alone.

PK Shootout was developed rapidly using OpenCode connected to DigitalOcean’s Serverless Inference via OAuth, utilizing a custom Inference Router as the backend. This setup allowed for the creation of a comprehensive game without specifying a model in the workflow, as the router automatically selected the appropriate model for each task. The significant discovery was that the router optimizes cost by not always running pre-configured models, but rather making cost-effective decisions autonomously.

The following sections detail the steps taken to create the game, from connecting OpenCode to deployment on App Platform, highlighting both successes and challenges encountered during development.

Connecting OpenCode to DigitalOcean

This step was straightforward. By logging into a DigitalOcean account, accessing the Inference Router section, and creating a router named game-designer, we set up four routes:

• design: Primarily used during coding, featuring GLM-5.2, Deepseek V4 Pro, Kimi K2.6, and MiMo V2.5 Pro.
• repo-qa: For repository question answering, utilizing Gemma 4, MiMo V2.5, Arcee Trinity Large Thinking, and Qwen 3.5 397B A17B.
• chore: Handles trivial edits like commit messages and docstrings, using Qwen3 Coder Flash, Ministral 3 14B, Gemma 4, and MiMo V2.5.
• debug: For error diagnosis and bug fixes, using GLM-5.2, Deepseek 3.2, MiniMax M2.5, and Kimi K2.6.

This router managed all development tasks. To connect it to OpenCode, install OpenCode in your terminal, run /connect, choose the OAuth login with DigitalOcean, authorize, and run /models to see your routers. Select router:game-designer and begin building.

DO-hosted Models for Coding Agents

The critical question is not which model to pick but understanding how the router assigns models to tasks. During the project, 596 tasks were categorized into five types: chore (56.2%), design (34.7%), debug (6.9%), repo-qa (1.8%), and fallbacks (0.3%). This distribution reflects typical development patterns, where design and debugging require more robust models like GLM-5.2, while MiMo V2.5 handled most chore tasks due to its cost-effectiveness over the nominally configured Gemma 4.

Cost and Time Analysis

The project completed with 83 assistant turns compared to 17 user turns, meaning the agent performed most of the work. The total generation time was about 74.5 minutes, with tasks ranging from quick edits to longer, complex generations. The router added minimal latency, maintaining efficiency throughout development.

The cost-effectiveness of using the router was evident, with the total cost at $8.25 compared to a potential $123 using a frontier model. This approach balances workload distribution, utilizing lower-cost models for simpler tasks while reserving advanced models for complex needs.

Intervention Points

Although the router managed most tasks, three instances required human intervention. A bug was effectively diagnosed by the agent, a spatial instruction needed clarification, and a command for asset generation required accurate API details, illustrating the workflow's reliance on human oversight for certain tasks.

When to Use a Router-Backed Setup

This setup is ideal for routine, well-scoped development, where the bulk of tasks do not require advanced reasoning or current external system knowledge. It is less suitable for tasks needing authoritative, up-to-date information from external systems.

By understanding the workflow's strengths and limitations, developers can effectively use a router-backed setup to manage costs and streamline development processes.

Configuration and Setup

To replicate this setup, create a model access key in DigitalOcean's Control Panel and optionally build a router with defined routes. Connect OpenCode using OAuth to access both foundation models and routers. Alternatively, drive Serverless Inference with your own code using OpenAI compatibility.