# How We Built DigitalOcean Inference Router

DevFeed: [How We Built DigitalOcean Inference Router](<https://devfeed.tech/articles/how-we-built-digitalocean-inference-router-19890.md>)

Original publisher: [Read original article](<https://www.digitalocean.com/blog/inference-router-architecture>)

Author: Adil Hafeez

Published: 2026-05-20T14:57:13Z

Content type: article

Language: en

Sources: [DigitalOcean](<https://devfeed.tech/sources/digitalocean.md>)

Topics: [Model Routing](<https://devfeed.tech/topics/model-routing.md>), [Digital Ocean](<https://devfeed.tech/topics/digital-ocean.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Multi Agent Systems](<https://devfeed.tech/topics/multi-agent-systems.md>)

Tags: [agentic-workflows](<https://devfeed.tech/tags/agentic-workflows.md>), [digitalocean](<https://devfeed.tech/tags/digitalocean.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llm](<https://devfeed.tech/tags/llm.md>), [multi-agent-systems](<https://devfeed.tech/tags/multi-agent-systems.md>), [product-updates](<https://devfeed.tech/tags/product-updates.md>), [routing](<https://devfeed.tech/tags/routing.md>)

## AI overview

DigitalOcean describes its Inference Router, which uses purpose-built models and live metrics to route LLM requests to appropriate models. The article explains how routing can reduce the cost and maintenance burden of applying one frontier model uniformly across coding, analysis, debugging, and other agentic tasks.

## Source excerpt

Most teams building on LLMs today make a single model decision and apply it uniformly across every request. They reach for a frontier model not because every task demands it, but because building the infrastructure to do anything smarter is hard, time-consuming, and easy to get wrong. When the tooling isn't there, the path of least resistance is to use a single model, even if it means that you end up overpaying for most tasks. Let's take an example. If you're a developer building with Cursor, Claude Code, Open Code or any coding agent today, you've already felt this. In a single session, your agent does deep codebase analysis, writes new functions, fixes bugs from test output, explains methods, searches documentation. These tasks are not equivalent but if you're on a single hardcoded model, you're paying frontier rates for all of them, including the ones that don't need it. The stakes are even higher in agentic workflows and multi-agent systems. When multiple agents are running in parallel each planning, executing, and evaluating across long-horizon tasks the cost of uniform model selection compounds with every step. Furthermore, major AI providers are moving toward token-based billing and tighter rate limits. Inference costs are about to get more expensive. The alternative is for hardcoded routing logic in the application layer with an intent classifier with the help of an LLM which adds to your cost and gets brittle fast. Even if you were to use a smaller model like Haiku to keep costs down, you're now paying for a routing call on top of every inference call. Also, accuracy takes a hit as the model is not purpose built for routing, and as your task types evolve or models change, the logic breaks in ways that are hard to catch. You've introduced double taxation: the cost of the classifier plus the cost of maintaining brittle routing code that needs updating with every change to your stack. You've turned model selection into a feature you own and maintain, which is