Move a production AI application to a new model in controlled stages: preserve a known-good version, compare the candidate on the same representative tests, verify compatibility and capacity, then expose users gradually behind measurable promotion gates and a rehearsed rollback. A model change can alter answers, formatting, latency, and cost even when the application code stays the same.
1. Make the current release reproducible
Before changing traffic, record enough information to rerun and compare both versions. Treat the model configuration as part of the release, not as an interchangeable endpoint name.
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- Record the current model identifier, provider and serving configuration, including relevant parameters.
- Version the prompts, application code, tool definitions, structured-output assumptions, and evaluation dataset.
- Link evaluation runs and production traces to the application’s Git commit, as AWS operational guidance recommends.
- Keep the current implementation addressable as the control and known-good deployment; do not overwrite it during the migration.
Build a versioned test set from the application’s actual task mix and known failure cases. Include routine requests, long or ambiguous inputs, edge cases, tool and integration paths, safety or refusal cases, and user-reported failures. Score the candidate and control on the same cases using application-relevant dimensions such as correctness, faithfulness, relevance, format compliance, task completion, and safety. Set acceptance thresholds before reviewing the candidate’s results; use human review where automated checks cannot reliably judge an answer. AWS guidance recommends versioned evaluation data and automated evaluation gates, with human review for harder-to-automate aspects.
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2. Check compatibility and capacity before sending traffic
A model identifier alone does not establish that the replacement can serve the same application. Confirm its support for the required API, modalities, tools, structured responses, context size, region, and account access in the actual deployment environment. Recheck provider and platform documentation for current endpoint availability, quotas, and retirement dates.
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For Amazon Bedrock specifically, AWS’s model lifecycle page says lifecycle dates are Bedrock-specific and can differ from a model provider’s dates; migration to an active model does not happen automatically when a model reaches end of life. Confirm the lifecycle status for the exact model and deployment region rather than assuming a provider announcement describes Bedrock availability.
Load-test realistic input and output token distributions, concurrency, and latency. Requests per minute alone may not predict capacity: AWS Bedrock quota guidance notes that request size and response length affect token consumption. For Bedrock, its guidance recommends bounded concurrency, queues, token-aware limits, and gradual ramping. Treat those quota mechanics as service-specific; measuring resource use under representative load is relevant regardless of provider.
3. Choose how the candidate will meet production traffic
Offline evaluation establishes a repeatable pre-release comparison, but it cannot reproduce every live interaction. A live method should match the question you need to answer: how outputs compare, how users respond, or whether a controlled switch is operationally safe.
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| Method | Candidate exposure | Best comparison | Main consideration |
|---|---|---|---|
| Offline evaluation | None | Repeatable quality on a fixed dataset | May miss live behavior or distribution shifts. |
| Shadow | Candidate output is hidden from users | Output quality, latency, cost, and failures on copied live requests | Adds inference load and requires controls for copied inputs; the current model still serves users. |
| Canary | Limited, then increased | Real user experience with a constrained initial blast radius | Needs live monitoring and a fast rollback path. |
| A/B test | Traffic is split between variants | Comparative user or business outcomes | Needs comparable cohorts and a suitable test design; a traffic share alone does not establish significance. |
| Blue/green | Traffic switches after validation | Readiness of parallel deployments and controlled cutover | Requires both environments to be available during the transition. |
Offline evaluation: compare before exposure
Run the candidate and control on the same held-out, versioned cases. This is the first promotion gate, not proof that the candidate will behave identically in production. A failed critical case should be investigated before proceeding, even if an aggregate score passes.
Shadow: compare without showing candidate answers
Copy eligible live requests to the candidate, log its outputs, and continue returning only the control’s answers to users. This helps assess output and operational behavior against live input patterns without exposing candidate responses. Before duplicating production inputs, review privacy, retention, and side-effect controls; the AWS rollout descriptions establish the shadow pattern but do not prescribe those safeguards.
Canary: expose a small share, then ramp
Route an initially limited portion of eligible traffic to the candidate, check the agreed gates, and increase exposure only while they remain healthy. AWS Prescriptive Guidance gives 1–5% of traffic as an illustrative canary group; the publication year is not stated, and this is an example rather than a universal threshold.
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A/B test: compare outcomes across variants
Split eligible users or requests between variants to compare a defined outcome, such as task completion, feedback, or conversion. AWS Prescriptive Guidance gives 5% of traffic as an example of a small A/B share; its publication year is not stated. That example does not define a sufficient sample size or test duration. Choose the design and observation period for the application’s traffic, outcome, and risk.
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Stand up and validate the candidate environment alongside production, then move traffic across while preserving the prior environment for recovery. AWS describes parallel environments as a way to avoid downtime. This pattern is useful when a clear traffic switch matters; the two environments must remain available during the transition.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.4. Define promotion gates and recovery before launch
Assign an owner to every gate and write down its success threshold, abort threshold, observation window, and action before the candidate receives traffic. Monitor user outcomes and system behavior together; a model can appear healthy in infrastructure metrics while producing worse answers, or preserve quality while exceeding latency or cost limits.
- Quality and user outcomes: task completion, application-specific quality checks, user feedback, and relevant safety signals.
- Reliability: errors, timeouts, and latency percentiles.
- Resource use: cost or token consumption and capacity signals.
Choose thresholds from the application’s risk, traffic, latency budget, and the cost of a bad answer. AWS guidance describes increasing canary traffic while metrics remain within service-level objectives and rolling back automatically when a critical metric degrades. That is an operational pattern, not a guarantee that a platform enables automatic rollback by default; teams must define their own SLOs and thresholds.
Keep the last stable deployment available and make restoring it a traffic switch or feature-flag action, not an improvised code change. Rehearse the runbook and identify who can trigger it. Also define a fallback response or heuristic for the case where the previous model is unavailable: rollback restores an earlier deployment, while fallback provides an alternate behavior. AWS Prescriptive Guidance distinguishes these approaches and recommends runbooks for them.
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- Pass the offline gate: the candidate meets the pre-agreed evaluation thresholds on the versioned test set.
- Compare safely: use shadow traffic or a limited live rollout to look for critical regressions in quality and operations.
- Hold at the initial exposure: observe the agreed metrics for the time needed to detect relevant failures; do not increase traffic merely because no alert has fired immediately.
- Expand deliberately: increase exposure in controlled increments only while the gates remain healthy. The increments and hold times are application-specific; AWS guidance does not set universal values.
- Declare the new stable release: after full cutover and review, designate the candidate as the current stable version while retaining the previous deployment for the recovery period your runbook specifies.
Continue monitoring after full cutover. Turn newly observed failures and user feedback into versioned evaluation cases so the next model change is tested against real failure modes. AWS preproduction guidance recommends growing evaluation datasets with real-world examples and user-reported failures, while versioning them to keep comparisons fair.
What this process cannot guarantee
No evaluation suite can prove that two models will behave identically or anticipate every user interaction. AWS guidance does not prescribe a universal test set, canary share, observation period, statistical-power calculation, SLO, or rollback threshold. Those choices depend on the application’s risk and operating conditions. Likewise, Bedrock lifecycle dates and quota behavior must be verified for the particular model, account, endpoint, and region.
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