Model Migration Workflow

Objective

Switch from one AI model or provider to another — for cost, capability, or reliability reasons — without breaking existing pipelines, prompts, or downstream integrations that depend on the current model’s behavior.

Prerequisites

• API access to both the current and target models.
• A test set of representative prompts and expected outputs from your current pipeline.
• A staging environment or feature flag system to run the new model in parallel before full cutover.

Tools

Your existing application code, API access to the target model, and a way to run A/B or shadow comparisons (can be as simple as logging both models’ outputs side by side for a period).

Workflow steps

1. Audit current usage. List every place the current model is called, what each call does, and any model-specific behavior your prompts or code depend on (specific output formatting, tool-use patterns, context window assumptions).
2. Build a test set. Collect 20-50 representative real requests and their current outputs to use as a regression baseline.
3. Run the target model against the test set. Compare outputs for quality, format compliance, and any breaking differences before touching production.
4. Adjust prompts as needed. Different models can require different prompt phrasing for equivalent results — this is usually the step that takes the most iteration.
5. Shadow-run in production. Send live traffic to both models in parallel (without using the new model’s output yet) to catch edge cases the test set missed.
6. Gradual cutover. Migrate a small percentage of traffic first, monitor for errors or quality regressions, then increase gradually.
7. Decommission the old integration once the new model handles 100% of traffic reliably for at least one full monitoring cycle.

Inputs and outputs

Input: current model integration, test set of representative requests. Output: a validated migration to the new model with no regression in output quality or pipeline reliability.

Automation options

Feature flags or a simple environment variable can control which model handles a given percentage of traffic, making gradual cutover straightforward without a full redeploy at each stage.

Optimization tips

• Keep the old integration code in place (behind a flag) for a full cycle after cutover, so rollback is a config change, not a redeploy.
• Watch for silent format differences — a model that returns slightly different JSON structure or citation formatting can break downstream parsing without throwing an error.
• Re-check pricing and context window assumptions specifically, since these are the most common source of unexpected cost or truncation after migration.

Related resources

See the model comparison guide before choosing a target model, or the API vs Subscription Cost Calculator to estimate cost impact.