Migrating from closed to open source models, Together

Migrating from closed-source AI models to open-source models can be faster and less complex than traditional technology migrations, particularly when managed services reduce operational burdens and existing model gateways and harnesses support multiple providers. A proposed strategy involves discovering candidates by defining the workload, using relevant benchmarks to narrow options, comparing practical measures such as cost per task, token use, latency, and verification time, and testing representative tasks. Evaluation should assess both model accuracy and runtime performance, with replayed production traffic providing more useful evidence than generic benchmarks when available. If a model falls short, organizations can iteratively adapt prompts, inference settings, context and tool integrations, or, when necessary, fine-tune or distill model weights while using modular evaluations to isolate improvements. After technical validation, teams should document migration effort, risks involving compliance, scaling, tooling, and downstream services, and expected return on investment, which may include substantial cost reductions and comparable quality. Production rollout can use a phased roadmap and canary deployments, such as initially routing a small share of traffic to open-source models, to validate real-world performance before broader adoption.

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