Migrate from OpenAI to Claude

Sometimes yes, sometimes no — and we do not know until we run the evals on your specific workload. Claude tends to outperform on long context, structured outputs, and codebase-aware tasks; GPT tends to outperform on certain creative-generation and image-input tasks; both are roughly equivalent for many enterprise summarisation and Q&A workloads. Discovery answers the question with data.

Often yes, sometimes no. Claude's prompt caching is unusually efficient for repeated-context workloads; long-context workloads can be cheaper because you do not need RAG. Per-token pricing varies by model tier — the right comparison is per-outcome cost, not per-token. We model both before migration.

8–16 weeks end-to-end for a multi-workload migration. Single-workload migrations can be 4–6 weeks. The variance is driven by how many distinct prompts and use cases need re-engineering.

Tool use in Claude is conceptually similar to OpenAI function calling but structurally different — XML-structured tool definitions, slightly different error semantics, and a different parallel-call model. We have a translation playbook that handles the common cases automatically and manual review for edge cases.

Yes — and this is the default. We run both providers warm during the transition window so workloads can flip provider behind a feature flag, parity can be validated continuously, and rollback is always one flip away. Most migrations run in production dual-mode for at least 4 weeks.

Fine-tuned models complicate migration because the fine-tuning data is the value, not the provider. We extract the training signal (instruction-following examples, classification labels) from your fine-tune and re-create the behaviour in Claude through prompt engineering, RAG with the original training corpus, or — when truly needed — through a small fine-tune with Anthropic's enterprise programme. Most fine-tuned workloads do not need re-fine-tuning.

Then we recommend you stay. Honest migration advice is the basis of long-term client relationships. We have completed Discovery engagements where the recommendation was 'do not migrate' — the client paid for the assessment, kept their existing stack, and came back later for unrelated work.

Yes — and many clients prefer this. A multi-provider abstraction layer routes workloads to whichever model is best for the task. We standardise the interface so future model adoption (next-gen Claude, Llama, Gemini, others) is a one-line config change instead of a re-engineering effort.