Coding Tools
Together AI's guide to migrating from closed to open models
Together AI Bloghigh confidence
Why it matters
- →Provides a structured methodology for evaluating open source models against proprietary alternatives using real traffic data.
- →Highlights managed inference services as a key enabler for reducing the operational burden of open source adoption.
- →Emphasizes prompt and parameter tuning as immediate levers before committing to resource-intensive fine-tuning.
Together AI has published a detailed guide on migrating enterprise workloads from closed-source API providers to open source models. The article argues that managed inference services reduce technical complexity, allowing teams to focus on evaluation rather than infrastructure. Key steps include defining specific use cases, using replayed production traffic for accurate benchmarking, and adapting prompts or sampling parameters before attempting fine-tuning. The guide aims to shorten migration timelines from years to weeks by prioritizing real-world performance metrics over leaderboard scores.
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