The 7-Day Model Migration Plan

Table of Contents

1. Why Model Migration Matters Now
2. The 7-Day Framework at a Glance
3. Day 1: Readiness Assessment and Stakeholder Alignment
4. Day 2: Environment Setup and Baseline Testing
5. Day 3: Data Validation and Migration Planning
6. Day 4: Parallel Run and Validation
7. Day 5: Performance Tuning and Optimisation
8. Day 6: Cutover Preparation and Rollback Planning
9. Day 7: Production Migration and Monitoring
10. Building This Into Your Operating Model
11. Common Pitfalls and How to Avoid Them
12. Making the Plan Repeatable

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Why Model Migration Matters Now

Model releases are no longer quarterly events. They’re monthly. Sometimes weekly. GPT-4 to GPT-4 Turbo. Claude 2 to Claude 3. Llama 2 to Llama 3. Every release brings speed improvements, cost reductions, new capabilities, and sometimes breaking changes.

If you’re building with large language models, you’re facing a choice: migrate methodically or fall behind. Most teams choose neither. They patch. They delay. They run two models in parallel for six months and never quite switch over.

This costs money. It costs velocity. It introduces technical debt that compounds.

The 7-day model migration plan is built for teams that can’t afford to wait. It’s a repeatable framework for engineering teams to re-run on every major model release between now and 2027. It’s built on the assumption that you’ll do this dozens of times, so it needs to be fast, documented, and low-friction.

At PADISO, we’ve run this with seed-stage founders shipping their first AI product, and with enterprise operators managing agentic AI deployments across 50+ internal workflows. The framework works because it separates concerns, runs tests in parallel where possible, and treats rollback as a first-class citizen.

This guide is for engineering leads, fractional CTOs, and platform teams who own AI infrastructure. It assumes you have a working model in production and you’re evaluating whether to upgrade.

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The 7-Day Framework at a Glance

Here’s the shape of the plan:

Day	Focus	Outcome
Day 1	Readiness assessment, stakeholder alignment, decision gate	Clear go/no-go, team assigned, rollback owner named
Day 2	Staging environment setup, baseline metrics captured	New model running in isolated environment, baseline latency/cost/quality recorded
Day 3	Data validation, test case prioritisation	Test suite ready, edge cases documented, migration plan signed off
Day 4	Parallel run, side-by-side validation	Both models running on live traffic, quality metrics compared, decision made
Day 5	Performance tuning, cost optimisation	Latency reduced, token usage optimised, cost per request modelled
Day 6	Cutover prep, runbook finalised, comms drafted	Rollback plan tested, team trained, incident response ready
Day 7	Production migration, monitoring, validation	Old model switched off, new model monitored, success criteria met

The entire plan is designed to run in a single calendar week. It assumes:

• Your team has access to the new model’s API or weights.
• You have a staging environment that mirrors production (or can be spun up in hours).
• You have observability in place (logs, metrics, traces).
• Your stakeholders (product, ops, security) are available for decisions.
• You have a rollback procedure already documented.

If any of these are missing, add 1–3 days to the plan. But the framework itself doesn’t change.

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Day 1: Readiness Assessment and Stakeholder Alignment

The Morning: Gather the Data

Start with a single question: Why are we migrating?

Common answers:

• Cost: New model is 40% cheaper per token.
• Speed: Latency dropped from 2 seconds to 400ms.
• Quality: Benchmark scores improved on your specific task.
• Availability: Old model is being deprecated or rate-limited.
• Capability: New model can do something the old one can’t (e.g., function calling, structured output).
• Compliance: New model meets a security or data residency requirement.

Each reason has different risk tolerances and success criteria. A cost migration is low-risk if quality holds. A capability migration is high-risk if it requires prompt rewrites. A compliance migration is non-negotiable.

Document the reason. Share it with your team. This becomes your north star for the week.

Next, gather baseline metrics on your current model:

• Latency: P50, P95, P99 response times (milliseconds).
• Cost: Cost per request, monthly spend, tokens per request.
• Quality: Accuracy, F1, BLEU, or domain-specific metrics (e.g., classification accuracy, RAG retrieval precision).
• Availability: Uptime, error rate, rate limit headroom.
• Volume: Requests per second, peak concurrent users, monthly API calls.

If you don’t have these metrics, you can’t measure success. Stop and instrument first. This is not optional. Refer to AWS Prescriptive Guidance on migration readiness for a structured approach to baseline assessment.

Mid-Morning: Stakeholder Alignment

Convene a 30-minute synchronous meeting with:

• Engineering lead (you, probably).
• Product owner (who owns success criteria).
• Operations lead (who owns production stability).
• Finance or business lead (who owns cost/revenue impact).
• Security or compliance lead (if relevant).

Walk through:

1. Why we’re migrating (the reason from above).
2. Success criteria (what must be true for this to be a win).
3. Failure criteria (what triggers a rollback).
4. Timeline (we’re doing this in 7 days).
5. Rollback owner (who decides to rollback, and when).
6. Communication plan (who do we tell, when, how).

Success criteria should be specific:

• Not: “The new model should be better.”
• Yes: “Latency stays under 500ms (P95), accuracy stays above 92%, cost drops below $0.02 per request.”

Failure criteria should be equally clear:

• Not: “If something goes wrong.”
• Yes: “If accuracy drops below 90%, we rollback within 30 minutes. If latency exceeds 1 second (P95), we rollback within 1 hour. If error rate exceeds 2%, we rollback immediately.”

Name a single rollback owner. This is the person who can make the call to rollback without consensus. Give them authority and a clear decision tree.

Late Morning: The Decision Gate

Before you commit to the 7-day plan, answer these questions:

1. Do we have a staging environment? (If not, can we spin one up in a few hours?)
2. Do we have access to the new model? (API key, weights, or beta access?)
3. Do we have observability? (Logs, metrics, traces for both models?)
4. Do we have a rollback procedure? (Can we switch back to the old model in under 10 minutes?)
5. Are our stakeholders aligned? (Did everyone in the meeting agree on success criteria?)
6. Do we have a team? (At least 2–3 engineers for the week?)

If you answered “no” to more than one question, extend the plan. Don’t rush.

If you answered “yes” to all six, you’re ready. Document the decision. Send a Slack message to your team: “Model migration greenlit. Plan starts tomorrow. [Link to this guide].” Move to Day 2.

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Day 2: Environment Setup and Baseline Testing

Morning: Spin Up Staging

You need a staging environment that’s as close to production as possible. “Close” means:

• Same infrastructure (AWS, GCP, Azure—same region if possible).
• Same observability stack (same logging, metrics, tracing tools).
• Same data pipeline (same database, cache, message queue).
• Ideally, a recent production data snapshot (last 24 hours, anonymised if needed).

If you have infrastructure-as-code (Terraform, CloudFormation, Pulumi), spin up staging from your existing templates. If you’re manually managing infrastructure, now is a good time to ask why. But for this week, set up staging the same way you set up production.

Cost: Staging should cost 10–20% of production. If it costs more, you’re over-provisioning. If it costs less, you might be under-testing.

Once staging is live, deploy your current model (the baseline) to staging. Confirm it’s working. Run a quick smoke test:

• 10 requests through the staging model.
• Compare outputs to production (should be identical, since it’s the same model).
• Check latency, error rate, and token usage.

If staging isn’t matching production, stop. Debug. Don’t move forward until they’re aligned.

Mid-Morning: Deploy the New Model

Deploy the new model to staging in parallel with the old model. Don’t replace the old one yet. Both should be running.

Your deployment should look like this:

Production:  [Old Model] → [Users]
Staging:     [Old Model] + [New Model] → [Test Traffic]

Configure your staging environment to send traffic to both models. Use a feature flag or environment variable to control which model serves requests. Start with 100% to the old model. You’ll shift traffic gradually.

For infrastructure guidance on setting up parallel deployments, refer to Google Cloud Architecture Framework on migration, which covers deployment strategies including blue-green and canary approaches.

Afternoon: Capture Baseline Metrics

Run a standardised test suite against both models in staging. This is your baseline. You’ll compare all future metrics to this.

Your test suite should include:

1. Happy path tests (10–20 typical requests).

   • Example: “Classify this customer support ticket.”
   • Example: “Summarise this earnings call transcript.”

2. Edge case tests (5–10 tricky requests).

   • Example: “Classify a ticket in a language we rarely see.”
   • Example: “Summarise a 50-page document.”

3. Performance tests (measure latency and token usage).

   • Send 100 requests and measure P50, P95, P99 latency.
   • Count input and output tokens.
   • Calculate cost per request.

4. Stress tests (optional, if you have time).

   • Send 1,000 concurrent requests.
   • Measure throughput and error rate.

Run this test suite against both the old model and the new model. Record everything:

Metric	Old Model	New Model	Difference
P50 Latency (ms)	450	380	-15%
P95 Latency (ms)	1,200	920	-23%
Cost per Request	$0.025	$0.015	-40%
Accuracy	94.2%	95.1%	+0.9pp
Error Rate	0.8%	0.6%	-0.2pp

Store these numbers. You’ll reference them all week.

End of Day 2: Both models are running in staging, and you have baseline metrics. You’re ready for Day 3.

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Day 3: Data Validation and Migration Planning

Morning: Validate Data Compatibility

Not all models accept the same inputs. Some require different prompt formats. Some have different token limits. Some have new parameters.

Validate:

1. Input format: Does the new model accept the same prompt structure?

   • Example: If you’re using system + user messages, does the new model support that?
   • Example: If you’re using function calling, does the new model support it?

2. Token limits: What’s the new model’s context window?

   • If it’s larger, great—you can send longer documents.
   • If it’s smaller, you need to truncate or chunk differently.

3. Output format: Does the new model return the same format?

   • Example: If you’re parsing JSON, does the new model’s JSON match the old model’s schema?
   • Example: If you’re using structured output, does the new model support it?

4. Parameters: Are there new parameters you should use?

   • Temperature, top-p, frequency penalty, etc.
   • Some new models have better defaults. Some require tuning.

Create a data compatibility checklist. For each item, answer: “Does the new model support this?” If the answer is “no”, you need to rewrite prompts or code. Do that now, in staging.

Mid-Morning: Identify Edge Cases

Think about the requests that break your system:

• Very long documents (does the new model’s context window handle them?).
• Non-English text (does the new model support your languages?).
• Rare categories (does the new model misclassify them?).
• Requests with special characters or formatting (does the new model parse them?).
• Requests with contradictory instructions (does the new model handle ambiguity the same way?).

Create a test case for each edge case. Run both models against these test cases. Compare outputs.

If the new model fails on an edge case the old model handled, you have three options:

1. Update your code (e.g., pre-process input differently).
2. Update your prompt (e.g., add a clarification or constraint).
3. Fallback to the old model (for this specific edge case only).

Document your choice. You’ll implement it on Day 4.

Afternoon: Finalise the Migration Plan

Now you have enough information to write a detailed migration plan. It should cover:

1. Traffic shift strategy:

   • Will you shift 10% → 25% → 50% → 100%?
   • Or 1% → 5% → 25% → 100%?
   • How long will you stay at each stage?
   • Who decides when to shift?

2. Monitoring and alerting:

   • What metrics will you watch?
   • What thresholds trigger an alert?
   • What thresholds trigger a rollback?

3. Rollback procedure:

   • How do you switch back to the old model?
   • How long does it take?
   • Who can initiate it?

4. Communication plan:

   • Who do you notify (stakeholders, customers, ops team)?
   • When do you notify them?
   • What’s the message?

5. Success criteria:

   • What must be true for the migration to be successful?
   • How long do you need to run the new model before you’re confident?

Write this down. Share it with your team. Get sign-off from the rollback owner. This is your insurance policy.

For a structured approach to planning, refer to Microsoft Cloud Adoption Framework on migration, which covers strategy, assessment, planning, and execution phases.

End of Day 3: You have a detailed migration plan, data compatibility validated, and edge cases documented. You’re ready for Day 4.

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Day 4: Parallel Run and Validation

Morning: Start Traffic Shift (Low Percentage)

Begin shifting traffic to the new model. Start small—1% or 5%, depending on your traffic volume.

Your setup should look like this:

Production: [Old Model] → [Users]
Staging:    [Old Model (95%) + New Model (5%)] → [Test Traffic]

Use a feature flag or load balancer to control the split. Make it easy to adjust without redeploying.

Let this run for 30 minutes to 1 hour. Watch your metrics:

• Latency: Is the new model faster or slower?
• Error rate: Are there unexpected errors?
• Cost: Is the new model cheaper or more expensive?
• Quality: Are outputs correct?

If everything looks good, shift to 10%. If something looks wrong, shift back to 0% and investigate.

Mid-Morning: Compare Outputs

For a sample of requests that went to both models, compare the outputs side-by-side.

Create a spreadsheet:

Request	Old Model Output	New Model Output	Match?	Notes
”Classify: angry customer”	NEGATIVE	NEGATIVE	✓	Both correct
”Summarise: earnings call”	[summary]	[summary]	✓	New one is shorter, still accurate
”Extract: entities from text”	[entities]	[entities]	✗	New model missed one entity

If outputs don’t match, investigate:

• Is the difference in format (e.g., capitalization, punctuation)?
• Is the difference in content (e.g., the new model produced a wrong answer)?
• Is the difference acceptable for your use case?

Document your findings. If there are unacceptable differences, adjust your prompt or model parameters. Re-test.

Afternoon: Ramp Up Traffic

Once you’re confident, ramp up traffic:

• 5% → 25% (wait 1 hour, monitor)
• 25% → 50% (wait 2 hours, monitor)
• 50% → 75% (wait 2 hours, monitor)
• 75% → 100% (wait 4+ hours, monitor)

At each stage, watch the same metrics:

• Latency (P50, P95, P99)
• Error rate
• Cost
• Quality (if you can measure it in real-time)

If any metric crosses a failure threshold, shift back to the previous stage. Investigate. Fix. Re-test.

The goal of Day 4 is confidence. You want to see the new model handling real traffic, real edge cases, and real volume without breaking anything.

End of Day 4: The new model is handling 50–100% of staging traffic, metrics are good, and you’ve validated outputs. You’re ready for Day 5.

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Day 5: Performance Tuning and Optimisation

Morning: Identify Bottlenecks

Now that the new model is running on real traffic, look for bottlenecks:

1. Latency bottlenecks:

   • Is the model slow because of the API call itself?
   • Or because of pre-processing (tokenisation, validation)?
   • Or because of post-processing (parsing, formatting)?
   • Use distributed tracing (e.g., OpenTelemetry, Datadog) to see where time is spent.

2. Cost bottlenecks:

   • Is the new model using more tokens than expected?
   • Are you sending redundant data to the model?
   • Can you compress prompts or cache results?

3. Quality bottlenecks:

   • Are there specific request types where the new model underperforms?
   • Can you adjust the prompt to improve accuracy?

For each bottleneck, brainstorm optimisations.

Mid-Morning: Implement Optimisations

Common optimisations:

1. Prompt optimisation:

   • Remove unnecessary instructions (shorter prompts = faster, cheaper).
   • Add examples (few-shot prompting improves accuracy).
   • Use structured output (forces the model to return consistent format).

2. Caching:

   • Cache common requests (e.g., “What’s the weather?”).
   • Cache prompt embeddings (if using embedding-based retrieval).
   • Cache model outputs for identical inputs.

3. Batching:

   • Batch requests together (some models are more efficient with batch processing).
   • Process requests asynchronously (if latency isn’t critical).

4. Model parameters:

   • Reduce temperature (if you want more consistent outputs).
   • Reduce max_tokens (if you’re getting verbose outputs).
   • Use different model sizes (smaller models are faster and cheaper).

Implement these optimisations in staging. Re-run your baseline tests. Measure improvement.

Afternoon: Cost Modelling

Now calculate the financial impact of the migration:

Current cost (old model):

• Cost per request: $0.025
• Requests per month: 1,000,000
• Monthly spend: $25,000

Projected cost (new model):

• Cost per request: $0.015
• Requests per month: 1,000,000
• Monthly spend: $15,000

Savings: $10,000 per month ($120,000 per year)

But factor in:

• Engineering time: How many hours did this migration take? (This week: ~40 hours. Cost: $2,000–$4,000 depending on salary.)
• Opportunity cost: What else could this team have built?
• Risk: What’s the cost if something goes wrong?

Payback period: ~1 month. That’s a good migration.

If payback period is longer than 3 months, reconsider. The migration might not be worth it.

If payback period is less than 1 month, prioritise this migration.

Document the cost model. Share it with your finance team. This is how you justify the engineering effort.

End of Day 5: You’ve optimised performance, modelled costs, and you’re confident the new model is ready for production. You’re ready for Day 6.

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Day 6: Cutover Preparation and Rollback Planning

Morning: Finalise the Runbook

A runbook is a step-by-step guide for executing the migration. It should be so detailed that someone who’s never done this before can follow it.

Template:

# Model Migration Runbook: GPT-4 → GPT-4 Turbo

## Pre-Cutover Checklist (Run on Day 6)
- [ ] Staging environment running with 100% traffic on new model
- [ ] All metrics within acceptable range
- [ ] Rollback procedure tested and working
- [ ] Incident response team briefed
- [ ] Monitoring dashboards created
- [ ] Alerts configured
- [ ] Stakeholders notified

## Cutover Steps (Run on Day 7)
1. **Backup** (09:00): Take a snapshot of the old model's state
2. **Deploy** (09:05): Deploy new model to production
3. **Validate** (09:10): Run smoke tests against production
4. **Monitor** (09:15–12:00): Watch metrics every 5 minutes
5. **Communicate** (ongoing): Update stakeholders every 30 minutes
6. **Celebrate** (12:00): Migration successful, send announcement

## Rollback Steps (if needed)
1. **Decide** (immediately): Rollback owner makes the call
2. **Execute** (within 5 min): Switch back to old model
3. **Validate** (within 10 min): Confirm old model is serving traffic
4. **Communicate** (immediately): Notify stakeholders
5. **Investigate** (after stabilisation): Figure out what went wrong

## Escalation Path
- If latency > 1 second (P95): Page on-call engineer
- If error rate > 2%: Page on-call engineer + rollback owner
- If accuracy drops > 5%: Rollback immediately

Fill in the details specific to your setup. Share with your team. Walk through it together.

Mid-Morning: Test the Rollback

Don’t assume your rollback procedure works. Test it.

1. In staging: Shift 100% traffic to the new model. Wait 5 minutes. Then shift 100% back to the old model. Confirm it works.
2. Time it: How long does rollback take? (Should be < 5 minutes.)
3. Validate: After rollback, confirm the old model is serving traffic correctly.

If rollback fails, fix it now. Don’t go to production until you can rollback reliably.

Afternoon: Set Up Monitoring

Create a monitoring dashboard that shows:

• Request volume: Requests per second, by model
• Latency: P50, P95, P99 for each model
• Error rate: Errors per second, by model
• Cost: Cost per request, by model
• Quality (if measurable): Accuracy, F1, or domain-specific metrics

Set up alerts:

• Latency > 1 second (P95): Page engineer
• Error rate > 2%: Page engineer
• Cost per request > $0.02: Alert (don’t page)
• Accuracy < 90%: Page engineer

Test the alerts. Make sure they work.

For guidance on setting up comprehensive monitoring during migrations, refer to NIST Cybersecurity Framework, which emphasises continuous monitoring and incident response.

Late Afternoon: Stakeholder Briefing

Convene your stakeholders (product, ops, finance, security) for a 15-minute briefing:

1. Status: Everything is ready. Cutover is tomorrow at [time].
2. Plan: Here’s what happens, step-by-step.
3. Risks: Here’s what could go wrong, and how we’ll handle it.
4. Communication: You’ll get updates every 30 minutes during cutover.
5. Questions: Any concerns?

Address concerns. Adjust the plan if needed. Get final sign-off.

End of Day 6: Runbook is finalised, rollback is tested, monitoring is set up, and stakeholders are briefed. You’re ready for Day 7.

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Day 7: Production Migration and Monitoring

Morning: Final Checks

Before you start, run through your pre-cutover checklist:

• Staging environment running with 100% traffic on new model for 24+ hours without issues
• All metrics within acceptable range
• Rollback procedure tested and working
• Incident response team briefed and standing by
• Monitoring dashboards live and alerting
• Runbook reviewed by team
• Stakeholders notified and ready

If anything is not checked, stop. Fix it. Don’t proceed.

If everything is checked, proceed.

Cutover (Typically 09:00–12:00)

09:00 — Backup & Announce

Take a snapshot of your current production state. This is your insurance policy.

Send a message to your team and stakeholders:

“Model migration starting now. We’re switching from [old model] to [new model]. Expect no user-facing changes. We’ll provide updates every 30 minutes.”

09:05 — Deploy New Model

Deploy the new model to production. Use your standard deployment process (blue-green, canary, rolling, etc.).

Start with 0% traffic to the new model. Confirm it’s deployed and healthy.

09:10 — Smoke Tests

Run your smoke test suite against production:

• 10 requests through the new model.
• Compare outputs to expected results.
• Check latency, error rate, cost.

If smoke tests pass, proceed. If they fail, rollback immediately.

09:15 — Traffic Shift (1%)

Shift 1% of production traffic to the new model. Watch metrics for 5 minutes.

• Latency: Should be similar to staging.
• Error rate: Should be < 0.5%.
• Cost: Should match staging.

If metrics look good, proceed. If not, rollback.

09:20 — Traffic Shift (10%)

Shift 10% of traffic. Watch for 10 minutes. Same checks as above.

09:30 — Traffic Shift (50%)

Shift 50% of traffic. Watch for 15 minutes.

09:45 — Traffic Shift (100%)

Shift 100% of traffic to the new model. The old model is now idle.

Watch metrics closely for the next 2–4 hours.

10:00–12:00 — Monitor

Stay in the war room. Watch your dashboard. Check metrics every 5 minutes:

• Is latency stable?
• Is error rate stable?
• Is quality stable?
• Are there any unexpected errors?

Every 30 minutes, send an update to stakeholders:

“Migration on track. New model handling 100% of traffic. Latency: 450ms (P95), error rate: 0.6%, accuracy: 95.2%. No issues.”

If a metric crosses a failure threshold, follow your rollback procedure immediately.

Afternoon: Stabilisation (12:00–18:00)

Once you’ve been running at 100% for 2+ hours without issues, you can relax slightly. But keep monitoring.

Watch for:

• Delayed failures: Sometimes issues don’t show up immediately. They show up after 4–8 hours of traffic.
• Edge cases: Rare requests might fail. They might not show up in your test suite.
• Performance degradation: The model might slow down as it handles more load.

If you see any of these, investigate. Fix if possible. Rollback if necessary.

Evening: Validation & Sign-Off (18:00+)

After 8+ hours of production traffic on the new model, you can be confident it’s working.

Run a final validation:

1. Metrics check: Compare production metrics to baseline.

   • Latency: Within 10% of staging? ✓
   • Error rate: < 1%? ✓
   • Cost: Lower than old model? ✓
   • Quality: Meets success criteria? ✓

2. Output check: Spot-check 50 recent requests. Do outputs look correct?

3. Stakeholder check: Confirm product, ops, and finance are satisfied.

If everything checks out, send a final announcement:

“Model migration complete. New model is now live in production. Old model decommissioned. Results: latency -23%, cost -40%, accuracy +0.9%. Migration successful.”

Docuument the results. Share with your team. Celebrate.

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Building This Into Your Operating Model

You’ve now run one model migration in 7 days. But the goal is to make this repeatable. The next migration should be faster.

Here’s how to build this into your operating model:

1. Codify the Process

Turn this 7-day plan into code:

• Infrastructure-as-code: Your staging environment should be defined in Terraform or CloudFormation. Spin it up in minutes, not hours.
• Test automation: Your smoke tests, baseline tests, and edge case tests should be automated. Run them with a single command.
• Monitoring-as-code: Your dashboards and alerts should be defined in code. Deploy them alongside your model.
• Runbook automation: Where possible, automate the runbook steps. Traffic shifts, rollbacks, and validation checks should be one-click.

2. Document Lessons Learned

After each migration, capture:

• What went well?
• What went wrong?
• What would you do differently next time?
• What new edge cases did you discover?

Update your test suite with new edge cases. Update your runbook with lessons learned.

3. Schedule Regular Migrations

Don’t wait for a crisis to migrate models. Schedule migrations every 3–6 months, even if the new model isn’t dramatically better. This keeps your team sharp and your infrastructure up-to-date.

If you’re working with PADISO’s AI & Agents Automation offering or our AI Strategy & Readiness service, we can help you build this into your operating model. We’ve done this with dozens of teams, and the pattern is always the same: the first migration takes 7 days, the second takes 4 days, and by the fifth, you’re doing it in 2 days.

4. Build a Model Registry

Maintain a registry of all models you’re using:

Model	Version	Status	Deployed	Cost/1K Tokens	Latency (P95)	Notes
GPT-4	1106	Production	Yes	$0.03	450ms	Primary
GPT-4	0613	Deprecated	No	$0.03	500ms	Old version
Claude 3	Opus	Staging	Yes	$0.015	380ms	Testing for cost reduction

This gives you a clear view of what’s running where, and makes it easy to plan migrations.

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Common Pitfalls and How to Avoid Them

Pitfall 1: Skipping Baseline Metrics

The problem: You migrate to a new model, but you don’t have baseline metrics from the old model. So you can’t tell if the new model is actually better.

The fix: Capture baseline metrics on Day 1. Don’t skip this. It’s the difference between a successful migration and a guessing game.

Pitfall 2: Testing Only Happy Path

The problem: Your test suite includes only typical requests. When the new model hits real traffic, it fails on edge cases you didn’t anticipate.

The fix: Spend time on Day 3 identifying edge cases. Test them. If the new model fails, adjust your prompt or code before production.

Pitfall 3: No Rollback Procedure

The problem: Something goes wrong in production. You want to rollback, but you don’t have a procedure. So you spend 2 hours debugging instead of 5 minutes rolling back.

The fix: Test your rollback procedure on Day 6. Make sure it works. Make sure it’s fast (< 5 minutes).

Pitfall 4: Shifting Traffic Too Fast

The problem: You shift from 0% to 100% in 10 minutes. If something goes wrong, you don’t catch it until it’s affecting all your users.

The fix: Shift traffic slowly: 1% → 10% → 50% → 100%. Spend time at each stage. Watch metrics. If something looks wrong, stop and investigate.

Pitfall 5: Not Monitoring Quality

The problem: You monitor latency and error rate, but not quality. The new model is fast and cheap, but it’s producing wrong answers. You don’t notice until users complain.

The fix: If quality is important for your use case, measure it during the migration. This might mean manual review (sample 50 outputs from each model), or automated metrics (accuracy, F1, etc.).

Pitfall 6: Migrating Without Stakeholder Alignment

The problem: You migrate to a new model because it’s cheaper. But your product team wanted the new model because it’s faster. Your finance team didn’t want to migrate because they’re worried about risk. Now everyone’s unhappy.

The fix: Align stakeholders on Day 1. Get agreement on success criteria and failure criteria. Make sure everyone knows what you’re optimising for.

Pitfall 7: Deploying Without Testing Rollback

The problem: You’ve tested your rollback procedure in staging. But production is different. When you try to rollback, it doesn’t work. Now you’re stuck.

The fix: Test your rollback procedure in production on Day 6 (before the actual migration). Shift 100% traffic to the new model, then shift back to the old model. Confirm it works.

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Making the Plan Repeatable

The 7-day model migration plan is designed to be repeatable. By 2027, you’ll have run this dozens of times. Here’s how to make it faster each time:

First Migration (7 days)

• Learning curve is steep.
• You’re discovering edge cases.
• You’re building infrastructure and automation.
• Focus on getting it right, not fast.

Second Migration (5 days)

• You’ve already built staging infrastructure.
• You have a test suite.
• You know what edge cases to look for.
• You can skip some exploratory work.

Third Migration (3 days)

• Your test suite is mature.
• Your runbook is battle-tested.
• Your team knows the process.
• You can parallelize work (e.g., Day 1 and Day 2 happen simultaneously).

Fourth+ Migration (2 days)

• You have full automation.
• Staging spins up in minutes.
• Tests run automatically.
• Traffic shifts are one-click.
• You’re just monitoring and validating.

The key to getting faster is automation. Every manual step you can automate saves you a day on the next migration.

If you’re working with PADISO’s Fractional CTO or Platform Engineering teams, we can help you build this automation. We’ve built model migration pipelines for teams running dozens of migrations per year.

Building Automation: A Checklist

• Staging environment spins up from code (Terraform, CloudFormation, etc.)
• Test suite runs automatically (pytest, Jest, etc.)
• Baseline metrics are captured automatically
• Dashboards and alerts are deployed from code
• Traffic shifts are automated (feature flags, load balancer config)
• Rollback is automated (one-click or automatic on threshold)
• Monitoring alerts trigger automatically
• Runbook steps are automated where possible

Each item you check off saves you 30 minutes to 1 hour on the next migration.

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Conclusion: Your 7-Day Roadmap

You now have a repeatable framework for migrating AI models in 7 days. Here’s your roadmap:

Day 1: Align stakeholders, define success criteria, make the go/no-go decision.

Day 2: Set up staging environment, deploy both models, capture baseline metrics.

Day 3: Validate data compatibility, identify edge cases, finalise migration plan.

Day 4: Shift traffic gradually, compare outputs, build confidence.

Day 5: Optimise performance, model costs, prepare for production.

Day 6: Finalise runbook, test rollback, brief stakeholders.

Day 7: Execute cutover, monitor closely, validate success.

The framework works because it separates concerns, runs tests in parallel, and treats rollback as a first-class citizen. It’s built for teams that can’t afford to wait, but also can’t afford to break production.

By the time you’ve run this a few times, you’ll have the infrastructure and automation in place to run migrations in 2–3 days. And more importantly, you’ll have the confidence to migrate quickly, knowing you can rollback safely if something goes wrong.

Model releases will keep coming. New capabilities, better performance, lower costs. With this framework, you’ll be able to take advantage of them without breaking a sweat.

Ready to migrate? Start with Day 1 tomorrow morning.

For more guidance on building AI infrastructure and scaling your engineering team, check out our Services page or book a call with one of our Fractional CTOs in Sydney. We’ve helped dozens of teams build repeatable processes for model migrations, infrastructure scaling, and AI-driven product development.