Handling Dual Systems During AI Migration

— AI migration means running two systems at once for months. This article covers dual-system architecture patterns, data synchronization, cost management, and knowing when it is safe to retire the old system.

level: advanced topics: migration, architecture tags: migration, architecture, operations, legacy-systems

The Reality of AI Migration

You cannot flip a switch and replace an existing system with AI overnight.

The reality: Months of running both systems in parallel.

During migration, you will:

  • Run legacy logic for most users
  • Run AI logic for test users
  • Compare outputs between systems
  • Gradually shift traffic from old to new
  • Keep old system as fallback

This creates operational complexity you must manage deliberately.

Why Dual Systems Are Necessary

Risk Mitigation

Cannot trust AI immediately:

  • AI might have bugs legacy system does not have
  • AI might fail on edge cases you have not seen yet
  • AI might regress on metrics you care about

Old system is safety net:

  • If AI fails, route back to legacy
  • If AI has outage, old system keeps running
  • If AI is slow, old system maintains latency SLA

Gradual Validation

You need time to prove AI works:

  • Shadow mode: Compare outputs without user impact
  • A/B test: Measure impact on real users
  • Progressive rollout: Expand AI slowly from 1% to 100%

Cannot do this without running both systems.

Business Continuity

Stakeholders require safety:

  • Finance wants gradual cost shift, not sudden spike
  • Product wants no user-facing regressions
  • Engineering wants rollback capability

Dual systems provide continuity during transition.

Dual System Architecture Patterns

Pattern 1: Parallel Execution, Log-and-Compare

Both systems run on every request, but only one result is returned to user.

User request
  ↓
├─> Legacy system ─> Return to user ✓
└─> AI system ─────> Log output (do not show user)

Background job:
  Compare legacy output vs AI output
  Log differences
  Compute agreement metrics

When to use: Shadow mode phase, early validation

Pros:

  • Zero user impact from AI
  • Gather real comparison data
  • Find edge cases safely

Cons:

  • Double the processing cost
  • Double the latency (unless async)
  • High infrastructure cost during testing

Pattern 2: Traffic Splitting (A/B Test)

Route fraction of users to AI, rest to legacy.

User request
  ↓
Route based on user ID hash:
├─> 90% → Legacy system → Return to user
└─> 10% → AI system → Return to user

Monitor metrics per group

When to use: A/B testing phase, controlled rollout

Pros:

  • Real user feedback on AI
  • Statistical comparison
  • Gradual risk exposure

Cons:

  • Need feature flag infrastructure
  • Must maintain both systems in prod
  • Users in A get different experience than B

Pattern 3: Fallback Hierarchy

Try AI first, fall back to legacy on failure.

User request
  ↓
Try AI system
  ↓
If AI succeeds → Return AI result
If AI fails → Fallback to legacy system → Return legacy result

When to use: Late-stage migration, when AI is usually reliable

Pros:

  • Use AI when it works
  • Graceful degradation
  • Users never see failures

Cons:

  • Latency spike when AI fails (wait for timeout, then run legacy)
  • Legacy system must stay always-available
  • Failure detection must be fast

Pattern 4: Feature-Based Routing

Route to AI or legacy based on request characteristics.

User request
  ↓
If simple_request(input):
  Legacy system (fast, reliable)
Else if complex_request(input):
  AI system (better quality for hard tasks)
Else:
  Legacy system (default)

When to use: When AI is better for specific use cases, legacy better for others

Pros:

  • Best tool for each task
  • Optimize cost and quality
  • No need to retire either system

Cons:

  • Complex routing logic
  • Must maintain both systems indefinitely
  • Routing logic itself can fail

Data Synchronization Challenges

If AI and legacy systems write data, synchronization is critical.

Read-Only AI (Simplest)

AI only reads data, does not write:

Legacy system writes to database
AI system reads from same database
No synchronization needed

When possible, use this pattern. Avoids entire class of consistency problems.

Write-Through to Both Systems

AI writes, legacy also writes:

User action
  ↓
Write to AI system
Write to legacy system
Both writes must succeed

Challenges:

  • What if one write succeeds and other fails?
  • Need transaction coordination
  • Slower (waiting for both writes)

Solution: Dual-write with reconciliation

Write to AI system (primary)
Write to legacy system (async)
Background job checks for inconsistencies

Event Sourcing

Both systems consume same event stream:

User action → Event published to queue
  ↓
├─> Legacy system consumes event
└─> AI system consumes event

Both rebuild state from events

When to use: If you have event-driven architecture

Pros:

  • No dual-write coordination needed
  • Easy to replay events to test AI
  • Can run AI in parallel without risk

Cons:

  • Requires event sourcing infrastructure
  • Complex to retrofit into existing systems

Monitoring Dual Systems

You cannot monitor both systems the same way.

Metrics to Track for Each System

Per-system metrics:

  • Request volume (how much traffic each system handles)
  • Success rate
  • Latency (p50, p95, p99)
  • Error rate
  • Cost per request

Comparison metrics:

  • Agreement rate (% of time outputs match)
  • Quality delta (AI quality - legacy quality)
  • Latency delta (AI latency - legacy latency)
  • Cost delta (AI cost - legacy cost)

User experience metrics (per group):

  • Task success rate
  • User satisfaction
  • Abandonment rate

Dashboards You Need

System health dashboard:

Legacy: 95% of traffic, 99.9% uptime, 200ms p95 latency
AI: 5% of traffic, 99.5% uptime, 800ms p95 latency

Comparison dashboard:

Agreement rate: 92%
Quality: AI +5% better
Latency: AI +600ms slower
Cost: AI +300% more expensive

Migration progress dashboard:

Week 1: AI 1% traffic
Week 2: AI 5% traffic
Week 3: AI 10% traffic
...
Target: AI 100% traffic by Week 12

Alerts to Set

Critical:

  • Either system availability <99%
  • Error rate >5% on either system
  • Agreement rate <80% (systems diverging badly)

Warning:

  • Traffic routing not as expected (should be 90/10, actually 80/20)
  • Latency degradation on either system
  • Cost exceeding budget

Info:

  • New disagreement patterns detected
  • Migration milestone reached (10% → 25% traffic)

Cost Management for Dual Systems

Running two systems costs more than one. Manage costs carefully.

Infrastructure Costs

Legacy system:

  • Must stay fully operational
  • Cannot downsize yet
  • Existing costs continue

AI system:

  • New API costs or GPU infrastructure
  • Initially small (1-10% traffic)
  • Grows as traffic shifts

Total cost during migration:

Month 1: 100% legacy + 1% AI = 101% of baseline
Month 3: 100% legacy + 25% AI = 125% of baseline
Month 6: 100% legacy + 75% AI = 175% of baseline

Peak cost happens mid-migration when both systems are heavily used.

Cost Optimization Strategies

1. Shadow mode only on sample of requests

Run AI on 10% of traffic (not 100%)
Still get statistically significant comparison data
Save 90% of AI costs during shadow phase

2. Asymmetric infrastructure

Legacy: Full production capacity (handles failover)
AI: Right-sized for actual traffic (5% of total)
Scale AI up as traffic shifts

3. Sunset legacy aggressively

As AI reaches 90% traffic:
  Start downsizing legacy infrastructure
  Keep minimal capacity for emergencies
  Reduces cost overlap

4. Use cheaper AI for low-risk traffic

High-value users: Premium AI model
Low-value users: Cheaper AI model or legacy
Reduces average AI cost

Budget Planning

Example migration budget (6 month timeline):

Baseline: $50,000/month (legacy system)

Month 1: $55,000 (shadow mode, 10% sampling)
Month 2: $60,000 (A/B test, 5% AI traffic)
Month 3: $75,000 (25% AI traffic)
Month 4: $100,000 (50% AI traffic) ← Peak cost
Month 5: $90,000 (75% AI traffic, start downsizing legacy)
Month 6: $70,000 (100% AI traffic, legacy retired)

Total 6-month cost: $450,000
6-month baseline cost: $300,000
Migration premium: $150,000 (50% over baseline)

Plan for 50-100% cost increase during peak migration.

Team Coordination During Dual Systems

Running two systems means coordinating more work.

Ownership Model

Option 1: Same team owns both

  • Pros: Full context, easy to coordinate
  • Cons: Team is stretched, hard to focus

Option 2: Separate teams (legacy vs AI)

  • Pros: Clear focus, specialized skills
  • Cons: Coordination overhead, knowledge silos

Recommended: Hybrid

  • Core team owns both
  • Dedicated AI specialist on team
  • Share on-call rotation

Development Workflow

For new features:

Should we build in AI or legacy?

If AI is &lt;50% traffic: Build in legacy (most users)
If AI is &gt;50% traffic: Build in AI (future)
If critical: Build in both (ensure consistency)

For bug fixes:

Does bug affect both systems?

If legacy-only: Fix in legacy only
If AI-only: Fix in AI only
If both: Fix in both systems

Prioritize system serving more traffic

On-Call and Incidents

Dual systems = dual failure modes:

Legacy system outage: → Shift 100% traffic to AI (if AI is proven) → Or show error page (if AI not ready)

AI system outage: → Shift 100% traffic to legacy (always safe)

Both systems down: → Disaster scenario, all hands on deck → Restore legacy first (known entity)

On-call needs to know both systems.

Deciding When to Retire Legacy System

You cannot run both systems forever. When is it safe to retire legacy?

Retirement Readiness Checklist

AI system maturity:

  • AI handles 100% of traffic successfully for 4+ weeks
  • AI error rate ≤ legacy error rate
  • AI latency meets SLA
  • No critical bugs in AI system
  • Team has deep operational knowledge of AI system

Business validation:

  • Key metrics equal or better than legacy baseline
  • User satisfaction maintained or improved
  • Cost is acceptable
  • Stakeholder buy-in to retire legacy

Operational readiness:

  • AI system has proven disaster recovery
  • Team trained on AI system operations
  • Monitoring and alerts are comprehensive
  • Documentation is complete

Risk mitigation:

  • Legacy code archived and accessible
  • Can rebuild legacy from source in <1 day if needed
  • Data backups exist
  • Rollback procedure is documented and tested

If any item is unchecked, do not retire legacy yet.

Graceful Legacy Retirement

Do not delete legacy system immediately.

Week 1-2:

  • Stop sending traffic to legacy
  • Keep legacy infrastructure running (warm standby)
  • Monitor AI system for regressions

Week 3-4:

  • Downsize legacy infrastructure to minimal capacity
  • Can still spin up quickly if needed

Month 2-3:

  • Shut down legacy infrastructure
  • Archive code and documentation
  • Keep database backups

Month 6+:

  • Fully decomission legacy systems
  • Reclaim infrastructure budget

Keep rollback capability for at least 1 month after 100% migration.

Rollback Strategy

Even after migrating to 100% AI, be ready to rollback.

Trigger Conditions for Rollback

Immediate rollback:

  • AI error rate >10%
  • AI availability <95%
  • Security incident
  • Data loss

Planned rollback:

  • Key metrics degrade >10%
  • User complaints spike
  • Cost exceeds budget by >50%
  • Critical bug discovered

Rollback Execution

Fast rollback (minutes):

1. Flip feature flag to route 100% to legacy
2. Verify legacy system is healthy
3. Announce rollback to team

Full rollback (hours):

1. Scale up legacy infrastructure (if downsized)
2. Route traffic back to legacy
3. Investigate AI system issues
4. Fix or decide to abandon AI

Post-rollback:

1. Root cause analysis
2. Decide: Fix and retry, or abandon AI migration?
3. Communicate to stakeholders

Rollback is not failure. It is risk management.

Common Dual-System Pitfalls

Pitfall 1: Undersizing Legacy During Migration

Mistake: Downsize legacy as AI ramps up

Problem: When AI fails, legacy cannot handle 100% traffic spike

Fix: Keep legacy at full capacity until AI is fully proven

Pitfall 2: No Clear Migration Timeline

Mistake: “We will run both systems until AI is ready” (no deadline)

Problem: Dual systems run forever, costs stay high, team never commits to retiring legacy

Fix: Set target timeline (e.g., 6 months), create milestones, hold team accountable

Pitfall 3: Diverging Data Models

Mistake: AI system uses different data schema than legacy

Problem: Synchronization becomes impossible, systems drift apart

Fix: Use same data models, or explicit translation layer

Pitfall 4: Neglecting Legacy Maintenance

Mistake: Focus all effort on AI, let legacy bitrot

Problem: Legacy system starts failing, cannot use as fallback

Fix: Budget time for legacy maintenance during migration

Pitfall 5: Premature Legacy Retirement

Mistake: Retire legacy after 2 weeks of 100% AI traffic

Problem: AI has latent bug, no rollback option available

Fix: Keep legacy for at least 4 weeks after full migration

Key Takeaways

  1. Dual systems are inevitable during migration – plan for months, not weeks
  2. Start with shadow mode – run AI in parallel without user impact
  3. Use traffic splitting for A/B tests – gradual exposure reduces risk
  4. Keep legacy as fallback – until AI is proven at 100% traffic
  5. Monitor both systems independently – cannot assume same behavior
  6. Budget for 50-100% cost increase during peak migration period
  7. Coordinate team carefully – both systems need attention
  8. Set clear retirement criteria – know when it is safe to sunset legacy
  9. Retire legacy gracefully – warm standby for weeks before full shutdown
  10. Always have rollback plan – even after 100% migration

Running dual systems is expensive and complex, but it is the only safe way to migrate to AI.