When to Move From OpenAI to Open Source

Open-source models have closed the gap dramatically. Llama 3, Mistral, and DeepSeek deliver quality that rivals GPT-4 on many production tasks — at a fraction of the cost. But migration is not free, and not every workload benefits.

When Open Source Wins

High-volume, well-defined tasks — Classification, entity extraction, summarisation, and structured output generation. These tasks are well-suited to smaller models that can be fine-tuned for your specific domain.

Data sensitivity requirements — When data cannot leave your infrastructure due to regulatory or contractual obligations, self-hosted open-source models are the only option.

Cost pressure at scale — When your monthly API bill exceeds the cost of dedicated GPU infrastructure plus engineering overhead.

Need for customisation — When you need to fine-tune the model on your domain-specific data for quality that prompting alone cannot achieve.

When to Stay on APIs

Low volume — If you process fewer than a few million tokens per day, the operational overhead of self-hosting outweighs the savings.

Frontier reasoning — For tasks requiring the absolute highest reasoning capability (complex multi-step analysis, nuanced creative writing), proprietary models still hold an edge.

Rapid iteration — During the prototyping phase, API access to multiple models lets you test quickly without infrastructure decisions.

The Migration Path

Migration is not a flip-the-switch event. The safe approach is gradual: run the open-source model in shadow mode alongside your existing API, compare outputs on real traffic, measure quality differences, and only cut over when you have confidence in parity. Start with your simplest, highest-volume tasks and work toward complexity.