Most enterprises evaluating Claude in 2026 are not starting from zero — they have a working integration with another provider and a reason to look around: cost, quality on a specific task, feature availability, or a desire to run inference inside their existing cloud. This article covers what actually changes at the code level, where prompts need adjustment, and how to structure the evaluation so the decision holds up when someone questions it later. It deliberately avoids describing other providers' current APIs in detail; those change, and your team knows your existing integration better than any guide does.
The API shape: similar ideas, different structure
Claude's Messages API follows the same broad pattern as most modern LLM APIs — you send a list of conversation turns and get a response — so the conceptual mapping is straightforward. But the details differ. In Claude's API, the system prompt is a dedicated top-level parameter rather than a message inside the conversation list, so migration usually means lifting your system instructions out of the message array. Response objects, streaming event formats, and error types have their own shapes, so any code that parses responses directly needs rework. Tool use (sometimes called function calling elsewhere) exists on both sides of the migration but with different request and response structures: you define tools with a name, description, and input schema, Claude returns a structured request to call one, your code executes it and returns the result. The concepts transfer; the field names do not.
The practical upshot: if your LLM calls are wrapped behind one internal module, a competent engineer can produce a working Claude backend quickly. If provider-specific request and response handling is scattered across the codebase, budget the migration as a small refactoring project — and consider it an opportunity to add the abstraction layer you will want for every future model decision.
Prompts do not port unchanged
This is the step teams most often skip, and it is the one that most distorts comparisons. Prompts get tuned — often over months — to the quirks of a specific model. Formatting instructions, few-shot examples, phrasing that works around an old model's failure modes: all of it is provider-shaped. Running another provider's battle-tested prompt verbatim against Claude, then concluding from the raw output, tells you very little. Plan a short adaptation pass: restate instructions plainly, move system-level guidance into the system parameter, and re-check your examples still illustrate what you want. Claude generally responds well to clear, direct instructions and explicit structure; elaborate workarounds built for another model can often simply be deleted.
Run the evaluation before you switch anything
Build a small evaluation set from real traffic: fifty to a few hundred actual inputs from your application, each with a definition of what a good output looks like. Run both providers against it — the incumbent as it runs today, Claude with adapted prompts — and score outputs blind, so reviewers do not know which model produced what. Score on the dimensions your business cares about (accuracy, tone, format compliance, refusal behavior), not general impressions. Then compare costs using your own measured token counts, not assumptions: pricing structures and tokenization differ between providers, so the same workload produces different bills, and only a measurement on your traffic settles it. Current Claude list pricing is on the models section of the main guide.
Also decide where you would run Claude before the evaluation, because it affects features: Bedrock and Vertex AI lack the Batch API, Files API, code execution, and web fetch tools, while Claude Platform on AWS has same-day parity with the first-party API. If your current architecture depends on an equivalent of one of those, verify availability first.
Plan the cutover like any dependency change
Treat the switch as a staged rollout, not an event. Keep the old provider integration alive behind a flag, route a small percentage of traffic to Claude, watch quality signals and error rates, and expand gradually. Keep your evaluation set — it becomes the regression suite for every future model upgrade regardless of provider. And keep expectations calibrated: a model switch changes the engine, not the application; monitoring, guardrails, and human review gates carry over and remain your responsibility.
Where to go next
Evaluating LLM features goes deeper on building the eval set this article assumes, and choosing your platform helps you pick where the Claude side of the comparison should run. The quickstart gets you to a first working call.