# DigitalOcean Model Evaluations Public Preview for Comparing Inference Strategies

DevFeed: [DigitalOcean Model Evaluations Public Preview for Comparing Inference Strategies](<https://devfeed.tech/articles/model-evaluations-prove-your-routing-policy-actually-works-19910.md>)

Original publisher: [Read original article](<https://www.digitalocean.com/blog/model-evaluation-public-preview>)

Author: Sathish Jothikumar

Published: 2026-06-04T19:52:49Z

Content type: tutorial

Language: en

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

Topics: [Digital Ocean](<https://devfeed.tech/topics/digital-ocean.md>), [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>), [Inference](<https://devfeed.tech/topics/inference.md>)

Tags: [digitalocean](<https://devfeed.tech/tags/digitalocean.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [inference](<https://devfeed.tech/tags/inference.md>), [product-updates](<https://devfeed.tech/tags/product-updates.md>), [routing](<https://devfeed.tech/tags/routing.md>)

## AI overview

A guide to using DigitalOcean Model Evaluations in Public Preview to compare models and routing strategies by output quality, latency, and cost before changing production inference traffic.

## Source excerpt

Most teams running inference at scale do not fail because they cannot find a "good" model. They fail because they ship a routing policy that looks fine in a playground, but drifts the moment it sees real prompts, real latency tails, and real per-token cost. The routing policy breaks on the prompts you never tested and your users find out before you do. Now you can use Model Evaluations, available in Public Preview on the DigitalOcean Inference Engine, to evaluate models available on the platform, or models that you have imported from Hugging Face or DigitalOcean Spaces. Model Evaluations helps you make comparable, reproducible decisions across models, routing strategies, cost, latency, and output quality. In this guide, we walk through setting up, running, and interpreting a Model Evaluation across three inference strategies: using a single frontier model for every request, deploying a task-specific fine-tuned model, or using the Inference Router with a cost- or latency-optimized policy. The goal is simple: determine which approach performs best on your workload before you change production traffic. The scenario Let's say you are running a legal-adjacent assistant (think contract summarization, clause extraction, policy Q&A). You currently call one expensive frontier model for every request as you believe it is the most accurate. Your CFO sees inference as COGS whereas your users see latency and p95 as key metrics on long documents. The Inference Router is attractive: it can send "easy" work to a cheaper or faster model and keep the heavy lifter for edge cases, if the routing policy is aligned with your use case. Your evaluation job is to compare these three candidates on the same dataset, using the same judge and metrics, so the results are directly comparable: Endpoint Candidate What you are really testing Serverless Inference anthropic-claude-4.6-sonnet Single "always frontier" model (your baseline) Inference Router model-eval-blog-legal An Inference Router confi