---
title: "Migrating from closed to open source models, Together"
description: "The post presents a five-stage playbook for moving production workloads from closed-source models to open-source models: discover, evaluate, adapt, decide, and take the system to production. It argues that managed services, model-agnostic harnesses, gateways, and tooling can reduce migration complexity, potentially making the effort weeks to months rather than months to years. Discovery combines workload definition, relevant benchmark filtering, cost-per-task and runtime analysis, playground testing, and narrowing candidates to two or three models. Evaluation separates accuracy from performance and recommends replaying real traffic against explicit goals for cost, speed, and quality, while adaptation may involve prompts, inference settings, context, and the surrounding harness. The excerpt frames migration as iterative rather than assuming a model passes unchanged."
---

# Migrating from closed to open source models, Together

[Together AI](https://yomu.fyi/company/together-ai) · Sep 16, 2026

**Type:** Tutorial

## Summary

The post presents a five-stage playbook for moving production workloads from closed-source models to open-source models: discover, evaluate, adapt, decide, and take the system to production. It argues that managed services, model-agnostic harnesses, gateways, and tooling can reduce migration complexity, potentially making the effort weeks to months rather than months to years. Discovery combines workload definition, relevant benchmark filtering, cost-per-task and runtime analysis, playground testing, and narrowing candidates to two or three models. Evaluation separates accuracy from performance and recommends replaying real traffic against explicit goals for cost, speed, and quality, while adaptation may involve prompts, inference settings, context, and the surrounding harness. The excerpt frames migration as iterative rather than assuming a model passes unchanged.

## Context

The post addresses organizations considering a move from closed-source models to open-source models. It focuses on reducing migration risk and effort by using managed services and existing model infrastructure while validating candidates against real workloads.

## Approach / What changed

The proposed process has five stages: discover candidate models, evaluate accuracy and performance, adapt prompts or inference configurations and the surrounding harness when needed, decide based on explicit goals, and move the validated system into production. Discovery uses workload definition, relevant benchmarks, playground testing, and operational measures such as cost per task, token usage, runtime, and verification speed. Evaluation relies on replaying existing traffic where possible.

## Takeaways

- Model selection should consider cost per task, tokens used, end-to-end runtime, and verification speed alongside benchmark scores; a slightly lower-scoring model may be a better fit if it uses half the tokens and time.
- Evaluation should separate accuracy from performance and use real workload data when available. Replaying existing traffic tests the model against the application’s actual traffic shape, request structures, and response requirements.
- A failed evaluation does not necessarily disqualify an open-source model. Differences in system prompts, sampling parameters, reasoning settings, context engineering, tools, and the surrounding harness may require adaptation.

## FAQ

### Why does the post recommend replaying existing traffic for model evaluation?

Real workloads contain application-specific traffic shapes, request structures, and response requirements that generic benchmarks cannot represent. Replaying existing traffic also provides a direct comparison against goals for cost, speed, and quality.

### What can be changed if an open-source model fails the initial evaluation?

The post identifies several adaptation levers, including system prompts, inference settings such as temperature and top\_k, preserved thinking, reasoning effort, context engineering, and the surrounding harness. A failure may reflect tuning carried over from the closed-source model rather than the model's capabilities alone.

**Tags:** [Inference](https://yomu.fyi/topic/inference), [LLMs](https://yomu.fyi/topic/llm), [Migrations](https://yomu.fyi/topic/migration), [Model Evaluation](https://yomu.fyi/topic/model-evaluation), [Open Source](https://yomu.fyi/topic/open-source)

- Source: [Together AI](https://www.together.ai/blog/migrating-from-closed-to-open-source-models)
- Source URL: https://www.together.ai/blog/migrating-from-closed-to-open-source-models
- Ingested by Yomu: 2026-09-19T10:13:48.654Z

[Read original post](https://www.together.ai/blog/migrating-from-closed-to-open-source-models)