Guide 23 mins

GPT-5 Successor: Production Migration Checklist

Repeatable framework for migrating to GPT-5 successors. Engineering checklist for model upgrades, evaluation, cost control, and production safety through 2027.

The PADISO Team ·2026-05-31

GPT-5 Successor: Production Migration Checklist

Why This Checklist Exists
Pre-Migration Planning
Model Selection and Evaluation
Cost and Performance Benchmarking
Testing and Validation Framework
Production Rollout Strategy
Monitoring, Rollback, and Observability
Security and Compliance Considerations
Documentation and Knowledge Transfer
Post-Migration Optimisation

Why This Checklist Exists

OpenAI, Anthropic, Google, and other major model providers release new versions every 6–18 months. Each release brings faster inference, lower latency, better reasoning, and improved cost per token. But moving from a stable production model to its successor is not a simple API call swap. It requires evaluation, validation, cost modelling, safety testing, and a rollout plan.

This checklist is built for engineering teams at startups, mid-market companies, and enterprises who need to repeat this process reliably between now and 2027. It covers:

Model selection — How to choose the right successor and avoid vendor lock-in
Evaluation and testing — Concrete metrics and frameworks to prove the new model works
Cost control — Benchmarking token usage, latency, and per-request spend
Production safety — Canary rollouts, rollback plans, and observability
Compliance — Audit trails and security considerations for regulated industries

At PADISO, we’ve guided teams through dozens of these migrations. This checklist is the framework we use.

Pre-Migration Planning

Establish Your Migration Trigger

Don’t migrate just because a new model exists. Define clear triggers:

Cost pressure — Current model costs exceed threshold (e.g., >$X per month)
Latency SLA breach — Response time exceeds acceptable limits for users
Capability gap — New model solves a problem the current model cannot (e.g., vision, structured output, longer context)
Security or compliance requirement — Current model is deprecated or lacks required audit trails
Vendor roadmap — Current model is moving to legacy pricing or reduced support

Document the business driver. If you’re migrating because a new model is 10% cheaper, quantify it: “Current spend $50K/month, projected savings $5K/month, payback period 4 weeks.”

Audit Your Current Deployment

Before you migrate, you must know what you’re migrating from.

Collect these metrics for your current production model:

Daily/weekly/monthly token usage (input + output, separately)
Average latency per request (p50, p95, p99)
Error rate and timeout frequency
Cost per request and total monthly spend
User satisfaction or quality metrics (if available)
Upstream dependencies (e.g., prompt templates, function calling schemas, output parsers)

Store this in a spreadsheet or monitoring dashboard. You’ll use it to compare against the successor.

Map Your Integration Points

List every place the model is used in your product:

API endpoints — Which endpoints call the model? Which are user-facing vs. internal?
Batch jobs — Do you use batch processing, and if so, which workflows?
Prompt templates — How many distinct prompts or system messages are in use?
Function calling — Are you using tool use / function calling? Document the schema.
Streaming vs. non-streaming — Which requests expect streaming responses?
Context and memory — Do you maintain conversation history or RAG context?

For each integration point, note:

Who owns it (team, product area)
How critical it is (user-facing, revenue-impacting, internal tool)
Current error handling and fallback logic

This map will guide your rollout strategy later.

Assign Ownership and Timeline

Migrations are not fire-and-forget. Assign:

Migration lead — Single owner accountable for timeline and success
Engineering team — Who builds and tests the integration
Product/data — Who defines success metrics and evaluates quality
Ops/infra — Who handles deployment, monitoring, and rollback

Set a target timeline. A typical migration takes 2–4 weeks from evaluation to production. Longer timelines often signal scope creep or unclear success criteria.

Model Selection and Evaluation

Check Official Model Availability and Roadmaps

Start with the canonical sources. OpenAI publishes model availability on the Platform Docs, which lists current, deprecated, and upcoming models. Anthropic shares model deprecations with advance notice. Google Cloud publishes Gemini model availability and lifecycle.

Check each provider’s timeline:

Sunset date — When will your current model stop accepting new requests?
Replacement recommendation — Does the provider recommend a specific successor?
Pricing change — Will you move to a new pricing tier or billing model?
API changes — Are there breaking changes to the API, response format, or function calling schema?

For GPT-5 successors specifically, watch OpenAI’s announcements for:

Release date and general availability date
Pricing per 1M tokens (input/output)
Context window (tokens)
Rate limits and quota availability
Deprecation timeline for GPT-4 variants

Define Your Evaluation Criteria

Not all models are created equal. Define what “better” means for your use case:

Quality metrics:

Accuracy on your domain (e.g., classification accuracy, BLEU score, human rating)
Consistency (same prompt, same model, same output?)
Hallucination rate (for RAG or fact-based tasks)
Structured output correctness (if using JSON mode or function calling)

Performance metrics:

Time to first token (TTFT) — Important for user-facing chat
End-to-end latency (p50, p95, p99)
Throughput — Requests per second at acceptable latency

Cost metrics:

Cost per 1K input tokens
Cost per 1K output tokens
Estimated monthly spend at current usage

Operational metrics:

Availability and SLA uptime
Support response time (critical for production)
Rate limit headroom for your peak load

Create a weighted scorecard. For example:

Quality: 40% weight
Cost: 30% weight
Latency: 20% weight
Availability: 10% weight

Run a Small-Scale Proof of Concept

Before committing to production, test the successor on a representative sample of your data.

For chat/generation tasks:

Sample 100–500 real user prompts from your current production logs
Run them against both the current model and the successor
Compare outputs qualitatively (does it make sense?) and quantitatively (if you have a metric)
Measure latency and cost for each

For classification or structured tasks:

Use a labelled test set (gold standard)
Run both models on the same inputs
Calculate accuracy, precision, recall, F1 — whatever applies
Flag any regressions

For RAG or fact-based tasks:

Use a set of retrieval queries with known correct answers
Compare how often each model returns the correct answer
Measure hallucination rate (claims not in the retrieved context)

Document the results in a spreadsheet:

Metric	Current Model	Successor	Change	Status
Accuracy	94%	96%	+2%	✓ Pass
Avg Latency (ms)	450	380	-70ms	✓ Pass
Cost per 1K tokens	$0.03	$0.02	-33%	✓ Pass
Hallucination rate	2.1%	1.8%	-0.3%	✓ Pass

If the successor fails on any critical metric, investigate why before proceeding.

Cost and Performance Benchmarking

Build a Cost Model

Token pricing is the largest variable in LLM cost. Model the full picture:

Inputs:

Daily/monthly user requests
Average input tokens per request (including context, RAG, history)
Input cost per 1M tokens (from provider pricing page)

Outputs:

Average output tokens per request
Output cost per 1M tokens

Formula:

Monthly Cost = (
  (Daily Requests × Days × Avg Input Tokens × Input $/1M) +
  (Daily Requests × Days × Avg Output Tokens × Output $/1M)
) / 1,000,000

Example:

10,000 requests/day
200 input tokens/request (including context)
100 output tokens/request
Current model: $0.03 input, $0.06 output per 1M tokens
Successor: $0.02 input, $0.04 output per 1M tokens

Current: (10K × 30 × 200 × 0.03 + 10K × 30 × 100 × 0.06) / 1M
       = (18M × 0.03 + 9M × 0.06) / 1M
       = (540K + 540K) / 1M
       = $1,080/month

Successor: (10K × 30 × 200 × 0.02 + 10K × 30 × 100 × 0.04) / 1M
         = (18M × 0.02 + 9M × 0.04) / 1M
         = (360K + 360K) / 1M
         = $720/month

Savings: $360/month (33%)

Build this model in a spreadsheet. Update it with actual token usage from your POC.

Account for Hidden Costs

Token pricing is not the whole story:

Latency cost — Slower responses = longer server resources in use = higher infrastructure cost. If latency increases by 100ms per request, model the extra compute cost.
Error rate cost — If the successor has a higher error rate, you’ll retry more often, using more tokens. Quantify it.
Rate limit cost — Some models have lower rate limits. If you hit the limit, you queue requests, which delays user experience. Model the business impact.
Context length cost — A longer context window might tempt you to include more history or RAG results, increasing input tokens. Don’t assume you’ll use the same input size.
Batch processing discount — If you use batch APIs (e.g., OpenAI Batch), the discount is typically 50%. Separate batch and real-time costs.

Measure Latency Under Load

Latency in a POC (1–10 concurrent requests) is not the same as latency in production (100+ concurrent requests).

Run a load test:

Spin up a test environment
Send 50, 100, 200, 500 concurrent requests to the successor
Measure p50, p95, p99 latency at each concurrency level
Compare against your current model’s load profile
Identify the concurrency level at which latency starts to degrade

Example results:

Concurrency	Current Model (p95)	Successor (p95)	Status
50	450ms	380ms	✓ Better
100	520ms	420ms	✓ Better
200	680ms	580ms	✓ Better
500	1,200ms	950ms	✓ Better

If the successor degrades faster under load, investigate:

Is the model provider rate-limiting you?
Is your client code queuing requests efficiently?
Do you need to increase batch size or connection pooling?

Testing and Validation Framework

Build a Comprehensive Test Suite

Your test suite must cover:

Functional tests:

Does the model return valid responses (not truncated, not errors)?
Does JSON mode output valid JSON?
Does function calling return valid tool calls?
Does streaming work end-to-end?

Quality tests:

Run your POC sample again; ensure quality metrics pass
Test edge cases (very long inputs, unusual characters, non-English text)
Test adversarial inputs (prompt injection attempts, if applicable)

Regression tests:

Compare outputs on a fixed set of prompts; flag any major changes
If you have user feedback or ratings, re-run those same prompts and compare ratings

Integration tests:

Test the successor in your actual application code (not just via API playground)
Test with your real prompt templates and function calling schemas
Test with your actual data pipeline (RAG, context, history)

Performance tests:

Measure latency on your test suite (should match POC results)
Measure token usage (input and output) on your test suite
Verify cost per request matches your model

Use Evaluation Frameworks

For complex or domain-specific tasks, use structured evaluation:

LangSmith (by LangChain) — Log, evaluate, and compare LLM outputs
Weights & Biases Prompts — Track prompt changes and model outputs over time
OpenAI Evals — Open-source evaluation framework for testing model behavior
Custom scoring — For domain-specific tasks, write custom scoring functions (e.g., accuracy against a gold standard)

Run your evaluation suite on both models. Document:

Pass/fail for each test
Quantitative scores (accuracy, latency, cost)
Any qualitative observations

Define Your Quality Gate

Set a clear threshold for “go/no-go” to production:

Example gate for a customer support chatbot:

Accuracy ≥ 94% (vs. 95% on current model; acceptable 1% regression)
Latency p95 ≤ 500ms (vs. 520ms on current; acceptable improvement)
Hallucination rate ≤ 2% (vs. 2.1% on current; acceptable improvement)
Cost per request ≤ $0.005 (vs. $0.006 on current; required savings)
Availability ≥ 99.5% (over 7-day test period)

If the successor meets all gates, proceed to production. If not, investigate:

Can you adjust prompts to improve quality?
Can you adjust your evaluation criteria (are they too strict?)?
Should you wait for a different model?
Should you stick with the current model?

Production Rollout Strategy

Plan a Canary Rollout

Don’t flip a switch and move 100% of traffic to the successor on day one. Use a canary rollout:

Phase 1: Internal only (1–2 days)

Route 100% of internal/admin requests to the successor
Monitor for errors, latency, and cost
Catch obvious bugs before users see them

Phase 2: Small percentage of users (3–5 days)

Route 5–10% of production requests to the successor
Monitor quality metrics, latency, and error rate
If all looks good, increase to 25%

Phase 3: Majority of users (5–7 days)

Route 50–75% of requests to the successor
Continue monitoring
If any issues, drop back to Phase 2 or Phase 1

Phase 4: Full rollout (1–2 days)

Route 100% of requests to the successor
Keep the current model as a fallback for 1–2 weeks

Total timeline: 2–3 weeks from canary start to full rollout.

Implement Feature Flags

Use feature flags to control which model is used:

# Pseudocode
if feature_flag.is_enabled('use_gpt5_successor', user_id=user_id):
    model = 'gpt-5-successor'
else:
    model = 'gpt-4-turbo'

response = openai.ChatCompletion.create(
    model=model,
    messages=messages,
    ...
)

This allows you to:

Roll back instantly if issues arise
A/B test the two models on the same traffic
Control the rollout percentage without code changes

Use a feature flag service like LaunchDarkly, Unleash, or Statsig.

Set Up Alerts and Dashboards

Before you start the rollout, configure monitoring:

Metrics to track:

Error rate (% of requests that fail or time out)
Latency (p50, p95, p99)
Cost per request
Quality metric (if you have an automated one, e.g., classification accuracy)
User satisfaction (if you have ratings or feedback)

Alerts:

Error rate > 1% for 5 minutes → page on-call
Latency p95 > 1,000ms for 5 minutes → page on-call
Cost per request > 2x baseline → alert (not page)

Dashboard:

Live view of current model traffic split (% on successor vs. current)
Side-by-side comparison of latency, error rate, cost
Drill-down by user segment, endpoint, or feature

At PADISO, we help teams build these dashboards using observability tools like Datadog, New Relic, or custom stacks. For platform engineering in Sydney or other regions, we’ve guided teams through dozens of these migrations.

Monitoring, Rollback, and Observability

Define Your Rollback Criteria

Before you start the rollout, decide when you’ll roll back:

Automatic rollback triggers:

Error rate > 2% for 10 minutes
Latency p95 > 1,500ms for 10 minutes
Cost per request > 3x baseline

Manual rollback triggers:

User complaints about quality (e.g., “the chatbot is giving wrong answers”)
Security issue discovered in the successor
Unexpected behaviour in a critical workflow

Document the rollback procedure:

Page on-call and incident commander
Disable feature flag for successor (instant rollback)
Monitor error rate and latency for 5 minutes
If stable, declare incident resolved
Post-mortem: What went wrong? What did we miss in testing?

Rollback should take < 5 minutes. If it takes longer, you’re not ready for production.

Log Requests and Responses

For every request during the rollout, log:

Timestamp
User ID or session ID
Model used (current or successor)
Input tokens, output tokens
Latency (ms)
Cost (estimated)
Error (if any)
Response (or hash of response, for privacy)

Store these logs in a queryable database (e.g., BigQuery, Snowflake, ClickHouse). You’ll use them to:

Compare quality between models
Identify which users or workflows have issues
Calculate actual cost and ROI
Debug unexpected behaviour

Implement Observability for LLM-Specific Issues

LLMs have unique failure modes. Monitor for:

Token limit exceeded:

Input tokens > context window
Log these requests separately; they’ll fail or get truncated

Streaming interruption:

Stream stops mid-response
Log the partial response and reason (timeout, network, rate limit)

Function calling errors:

Model returns invalid JSON for function calls
Log the invalid JSON and the prompt that caused it

Hallucination (for RAG tasks):

Model claims a fact not in the retrieved context
If you have a way to detect this (e.g., fact-checking), log it

Cost anomalies:

Requests with unusually high token counts
Log these for investigation

Use NIST’s AI Risk Management Framework as a reference for governance and monitoring. For teams building platform engineering solutions, observability is non-negotiable.

Track Actual vs. Projected Costs

During the rollout, compare your cost model against actual spend:

Daily cost tracking:

Date	Requests	Avg Input Tokens	Avg Output Tokens	Actual Cost	Projected Cost	Variance
Day 1	10,000	195	98	$710	$720	-1.4%
Day 2	10,200	198	102	$745	$740	+0.7%
Day 3	10,100	201	105	$760	$750	+1.3%

If actual cost is consistently higher than projected, investigate:

Are users including longer context or history?
Is the model generating longer outputs than expected?
Are there retry loops inflating token usage?

Adjust your model and alert thresholds accordingly.

Security and Compliance Considerations

Audit Trail and Compliance Logging

If you’re in a regulated industry (financial services, healthcare, legal), model changes require audit trails.

Document:

When the migration started and ended
Which model was used for which requests (with timestamps)
Any changes to prompts, system messages, or function calling schemas
Any rollbacks or incidents
Sign-off from compliance or security team

Store audit logs immutably (e.g., in a write-once S3 bucket, or in a compliance-specific tool like Vanta).

For teams pursuing SOC 2 or ISO 27001 compliance, model migrations are a control point. We help teams document these migrations via Vanta implementation, which integrates with your existing infrastructure.

Data Privacy and Model Training

When you migrate to a new model, clarify:

Does the new model provider use your data for training? (Typically no for paid APIs, yes for free tiers)
Can you use the new model in a regulated context? (Check the provider’s terms for HIPAA, PCI-DSS, etc.)
What is the data retention policy? (How long does the provider keep your requests?)

For sensitive data:

Use self-hosted or fine-tuned models if available
Use API endpoints that explicitly disable training (e.g., OpenAI’s API with training_disabled)
Mask or redact PII before sending to the API

Prompt Injection and Security Testing

When you change models, re-test for prompt injection and adversarial inputs:

Collect a set of known prompt injection attacks (e.g., “Ignore the above instructions and…”)
Run them against both the current and successor models
Compare how each model handles them
If the successor is more vulnerable, investigate why and consider mitigations (e.g., input validation, instruction hierarchy)

For teams building AI solutions with security-first architecture, prompt security is part of the baseline.

Vendor Concentration Risk

If you’re migrating from GPT-4 to a GPT-5 successor, you’re increasing your dependence on OpenAI. Consider:

Multi-model architecture — Keep fallback logic to switch to Anthropic Claude or Google Gemini if needed
Fine-tuning or distillation — Can you fine-tune a smaller, open-source model to match the performance of the larger model?
API abstraction — Use a wrapper that abstracts the model provider, so you can swap providers with minimal code changes

At PADISO, we help teams design AI strategy and readiness plans that reduce vendor lock-in.

Documentation and Knowledge Transfer

Document the Migration Process

Write a post-migration document that includes:

Pre-migration state:

Current model, version, and pricing
Current performance metrics (latency, cost, quality)
Identified issues or limitations

Migration decision:

Why you migrated (cost, latency, capability)
Evaluation results and comparison
Cost-benefit analysis

Migration process:

Timeline and phases
Rollout percentage by date
Any issues encountered and how they were resolved
Final rollback or decision to keep the new model

Post-migration state:

New model, version, and pricing
New performance metrics
Actual cost savings or improvements
Lessons learned

Store this document in your wiki or knowledge base. It becomes the template for the next migration.

Update Your Runbooks

Update your operational runbooks:

Incident response — If the model fails, which runbook do you follow?
Rollback procedure — How do you roll back to the previous model?
Performance tuning — How do you adjust latency or cost if needed?
Cost forecasting — How do you project next month’s spend?

Make sure every on-call engineer has access to and understands these runbooks.

Train Your Team

Ensure your team understands:

Why you migrated (business case)
How the new model differs from the old one (capability, latency, cost)
How to monitor and debug issues
When and how to roll back

Run a brief training session (30 minutes) with engineers, product, and ops. Answer questions. Distribute the documentation.

Production Rollout Strategy (Detailed)

Week 1: Preparation

Days 1–3:

Finalise your test suite and quality gates
Set up monitoring dashboards and alerts
Brief the team on the rollout plan and rollback criteria
Ensure on-call rotation is aware

Days 4–7:

Run a final POC with latest model version
Validate cost model against actual POC usage
Confirm feature flag is working correctly
Do a dry-run of the rollback procedure

Week 2: Canary Rollout

Days 1–2 (Phase 1: Internal):

Enable feature flag for 100% of internal requests
Monitor error rate, latency, and cost
Check logs for any unusual patterns
If all looks good, proceed to Phase 2

Days 3–5 (Phase 2: 5–10% of users):

Enable feature flag for 5% of production requests
Monitor quality metrics closely
If error rate or latency is high, roll back immediately
If all looks good, increase to 10%, then 25%

Days 6–7 (Phase 3: 25–50% of users):

Continue increasing traffic to the successor
Monitor for any user-reported issues
Compare quality metrics between models

Week 3: Full Rollout

Days 1–2 (Phase 4: 75–100%):

Increase to 75%, then 100%
Keep the current model as a fallback in code
Monitor closely for 48 hours

Days 3–7 (Stabilisation):

Verify all metrics are stable
Remove feature flag for current model (or keep as emergency fallback)
Close the migration ticket
Schedule post-mortem if there were any issues

Post-Migration Optimisation

Analyse and Optimise Token Usage

Now that the successor is in production, optimise for cost:

Analyse token distribution — Which prompts or features use the most tokens?
Identify optimisation opportunities:
- Shorten system prompts (every token counts)
- Reduce context window (use only relevant history or RAG results)
- Use prompt compression or summarisation
- Switch to a cheaper model for simple tasks (e.g., classification)
Implement optimisations — A/B test each change to ensure quality doesn’t regress
Measure impact — Track monthly cost reduction

For example, if you reduce average input tokens from 200 to 150 (25% reduction), you save 25% on input cost.

Benchmark Against Competitors

Now that you’ve migrated, check if competitors have migrated to the same model or a different one:

Are they using GPT-5 successor, Claude 4, Gemini 3, or something else?
What are their reported latencies and costs?
Are they achieving better quality than you?

This informs your next migration decision.

Plan for the Next Migration

The next model release is 6–18 months away. Start planning:

Set a migration trigger — When will you consider the next model?
Monitor announcements — Subscribe to OpenAI, Anthropic, Google, and other provider newsletters
Run quarterly POCs — Every quarter, test the latest model on a small sample of your data
Update your cost model — As pricing and your usage patterns change, keep your model current
Refine your playbook — Use what you learned from this migration to improve the next one

At PADISO, we work with teams to build repeatable AI strategy and readiness processes that make each migration faster and lower-risk. We’ve guided teams through migrations on platform engineering in San Francisco, Los Angeles, Chicago, Boston, Seattle, Austin, Dallas, Houston, Atlanta, Denver, and Sydney.

Summary and Next Steps

Migrating to a GPT-5 successor is not a one-time event. It’s a repeatable process that you’ll execute every 6–18 months as new models arrive. This checklist gives you a framework to do it safely, measurably, and with minimal risk.

The Checklist at a Glance

Pre-Migration (1 week):

Define your migration trigger (cost, latency, capability)
Audit current model usage and performance
Map integration points and owners
Assign migration lead and timeline

Evaluation (1–2 weeks):

Check official model availability and roadmaps
Define evaluation criteria (quality, latency, cost)
Run POC on representative sample
Build cost model and compare

Testing (1 week):

Build comprehensive test suite
Run quality, regression, and performance tests
Define quality gate and sign-off criteria

Rollout (2–3 weeks):

Set up monitoring and alerts
Implement feature flags
Run canary rollout (internal → 5% → 25% → 100%)
Monitor and rollback if needed

Post-Migration (ongoing):

Analyse token usage and optimise
Document lessons learned
Plan for next migration

Getting Help

If you’re building a production AI application and need guidance on model migrations, security, or compliance, PADISO offers:

CTO as a Service — Fractional technical leadership for migration planning and execution
AI & Agents Automation — Help designing and testing AI-powered workflows
AI Strategy & Readiness — Roadmapping and vendor evaluation
Security Audit (SOC 2 / ISO 27001) — Compliance readiness for regulated deployments
AI Quickstart Audit — Two-week diagnostic to assess your AI readiness and migration priorities

We’ve guided teams through dozens of model migrations and platform modernisations. Book a call to discuss your specific situation.

Resources and Further Reading

For deeper dives, check:

OpenAI Cookbook — Practical examples for building and testing with OpenAI models
Model deployment versioning - Azure AI Foundry — Best practices for versioned deployments
Databricks Blog — Articles on model evaluation, MLOps, and production AI

The GPT-5 successor era is here. With this checklist, you’re ready to migrate safely, measure impact, and repeat the process reliably through 2027 and beyond.

Want to talk through your situation?

Book a 30-minute call with Kevin (Founder/CEO). No pitch — direct advice on what to do next.

Book a 30-min call

GPT-5 Successor: Production Migration Checklist

GPT-5 Successor: Production Migration Checklist

Table of Contents

Why This Checklist Exists

Pre-Migration Planning

Establish Your Migration Trigger

Audit Your Current Deployment

Map Your Integration Points

Assign Ownership and Timeline

Model Selection and Evaluation

Check Official Model Availability and Roadmaps

Define Your Evaluation Criteria

Run a Small-Scale Proof of Concept

Cost and Performance Benchmarking

Build a Cost Model

Account for Hidden Costs

Measure Latency Under Load

Testing and Validation Framework

Build a Comprehensive Test Suite

Use Evaluation Frameworks

Define Your Quality Gate

Production Rollout Strategy

Plan a Canary Rollout

Implement Feature Flags

Set Up Alerts and Dashboards

Monitoring, Rollback, and Observability

Define Your Rollback Criteria

Log Requests and Responses

Implement Observability for LLM-Specific Issues

Track Actual vs. Projected Costs

Security and Compliance Considerations

Audit Trail and Compliance Logging

Data Privacy and Model Training

Prompt Injection and Security Testing

Vendor Concentration Risk

Documentation and Knowledge Transfer

Document the Migration Process

Update Your Runbooks

Train Your Team

Production Rollout Strategy (Detailed)

Week 1: Preparation

Week 2: Canary Rollout

Week 3: Full Rollout

Post-Migration Optimisation

Analyse and Optimise Token Usage

Benchmark Against Competitors

Plan for the Next Migration

Summary and Next Steps

The Checklist at a Glance

Getting Help

Resources and Further Reading

Want to talk through your situation?