I recently attended a talk at API Days Paris about AI-validated API migration for a major European mobility platform. The speakers focused on how AI helped validate semantic equivalence between old and new APIs—brilliant stuff around MCP patterns, generated code, and iterative learning.
As a Solutions Architect, I wanted to explore a complementary angle: the high-level architecture that enables safe migration at this scale.
This article dives into the infrastructure patterns, design decisions, and architectural components that make large-scale API migrations possible when you're handling hundreds of millions of transactions with zero tolerance for downtime or data loss.
The Migration Challenge
Picture this scenario:
- Current state: Monolithic API, battle-tested, tightly coupled
- Target state: Orchestration-based API, microservices architecture
- Requirements: Zero downtime, zero data loss, zero regression
- Scale: Hundreds of millions of annual requests
- Constraint: Can't do a "big bang" cutover
How do you architect this?
High-Level Architecture
A migration at this scale requires several architectural layers working together:
CLIENT LAYER
(Millions of Users)
|
v
┌──────────────────────┐
│ API Gateway │
│ (Traffic Routing) │
│ - Feature Flags │
│ - Canary Release │
│ - Shadow Testing │
└──────────┬───────────┘
|
┌──────────┴──────────┐
| |
v v
┌─────────────────┐ ┌─────────────────┐
│ Legacy API │ │ New API │
│ (Monolithic) │ │ (Orchestration) │
└────────┬────────┘ └────────┬────────┘
| |
v v
┌─────────────────┐ ┌─────────────────┐
│ Legacy Business │ │ Microservices │
│ Logic │ │ - Booking │
│ │ │ - Pricing │
└─────────────────┘ │ - Inventory │
└─────────────────┘
Let's explore each component and the patterns that make this work.
1. API Gateway Layer
Core responsibility: Enable traffic splitting without client-side changes.
The gateway handles:
Progressive Traffic Routing:
Phase 1: 0% new (Shadow testing)
Phase 2: 5% new (Initial canary)
Phase 3: 20% new (Expanded rollout)
Phase 4: 50% new (Major transition)
Phase 5: 100% new (Complete migration)
Key capabilities:
- Feature flags for instant rollback (<30 seconds)
- Request mirroring to send traffic to both APIs simultaneously
- Smart routing based on tenant, region, or user segment
- Circuit breakers to protect against cascading failures
Implementation considerations:
// Simplified routing logic
function routeRequest(request) {
const userSegment = getUserSegment(request);
const rolloutPercentage = getFeatureFlag('new-api-rollout');
if (isInRollout(userSegment, rolloutPercentage)) {
return routeToNewAPI(request);
}
return routeToLegacyAPI(request);
}
Why this matters:
One config change can shift traffic instantly. No client deployments, no DNS changes, no waiting.
2. Shadow Testing Architecture
Purpose: Validate the new API in production without impacting users.
┌──────────┐
│ Client │
└────┬─────┘
│ Request
v
┌────────────────┐
│ API Gateway │
└────┬───────────┘
│
├─────────────────┐
│ │ (Mirror)
v v
┌──────────┐ ┌──────────┐
│ Legacy │ │ New │
│ API │ │ API │
│ (Return) │ │ (Silent) │
└──────────┘ └────┬─────┘
│
v
┌──────────────┐
│ Validation │
│ Pipeline │
└──────────────┘
How it works:
- Client gets response from legacy API (always)
- Request is mirrored to new API (client never sees this)
- Both responses feed into validation pipeline
- Discrepancies logged, but no client impact
Benefits:
- Real production traffic patterns
- Zero risk to users
- Identifies edge cases missed in testing
- Builds confidence before actual migration
3. Validation Pipeline Architecture
This is where the AI validation piece fits in:
┌─────────────────────────────────────────┐
│ VALIDATION ARCHITECTURE │
└─────────────────────────────────────────┘
Legacy Response New Response
| |
v v
┌──────────────────────────────┐
│ Schema Normalization │
└──────────┬───────────────────┘
|
v
┌──────────────────────────────┐
│ Semantic Comparison Engine │
│ (AI-Generated Test Code) │
└──────────┬───────────────────┘
|
v
┌──────────────────────────────┐
│ Severity Classification │
│ - CRITICAL (block rollout) │
│ - HIGH (alert team) │
│ - LOW (log only) │
└──────────┬───────────────────┘
|
v
┌──────────────────────────────┐
│ Monitoring & Alerting │
└──────────────────────────────┘
Key insight: The validation code is generated once by AI, then runs deterministically. This avoids the cost and latency of live AI comparisons.
4. Data Transformation Layer
Different API contracts mean different data structures:
Legacy format:
{
"ticket_id": "TKT-123",
"passenger": {
"first_name": "John",
"last_name": "Doe"
},
"pricing": {
"total": 94.50
}
}
New format:
{
"id": "TKT-123",
"passenger_info": {
"name": {
"given": "John",
"family": "Doe"
}
},
"payment": {
"amount": {
"total": 94.50
}
}
}
The challenge: These are semantically equivalent but structurally different. You need:
- Field mapping rules (which legacy field → which new field)
- Type conversions (string dates → ISO timestamps)
- Null handling (missing fields, different defaults)
- Semantic validation (not just structural equality)
This is where Model Context Protocol (MCP) becomes valuable—you can query specific paths in large JSON without loading everything into memory.
5. Phased Migration Strategy
The strangler fig pattern in action:
PHASE 1: Shadow Mode (Weeks 1-4)
├─ 0% live traffic to new API
├─ All traffic mirrored for validation
└─ Goal: Identify and fix discrepancies
PHASE 2: Canary (Weeks 5-8)
├─ 5% live traffic to new API
├─ Monitor error rates, latency, validation
└─ Goal: Prove stability with real users
PHASE 3: Progressive Rollout (Weeks 9-16)
├─ 20% → 50% → 80% → 100%
├─ Gradual increase based on metrics
└─ Goal: Complete migration
PHASE 4: Legacy Decommission (Week 17+)
├─ New API handles 100% traffic
├─ Legacy on standby (30-90 days)
└─ Goal: Safe shutdown
Why phased?
- Limits blast radius of issues
- Allows learning and adjustment
- Builds team confidence
- Enables fast rollback
6. Rollback Architecture
Critical requirement: Rollback in < 30 seconds.
Trigger Conditions:
├─ Error rate > 1% → Immediate auto-rollback
├─ Latency > 200% baseline → Alert + manual review
├─ Validation failures > 5% → Alert
└─ Circuit breaker open → Automatic failover
Rollback Mechanism:
Feature Flag Flip → Traffic routes to legacy → Done
Implementation:
// Monitoring triggers
if (errorRate > 0.01 || latencyIncrease > 2.0) {
featureFlags.set('new-api-rollout', 0);
alert.notify('AUTO-ROLLBACK TRIGGERED');
}
The beauty: No code deployments needed. Just flip a switch.
7. Observability Stack
You can't migrate what you can't measure:
Metrics to track:
- Request latency (p50, p95, p99)
- Error rates (by endpoint, by tenant)
- Validation pass/fail rates
- Traffic distribution percentages
- Resource utilization
Logging strategy:
- All validation discrepancies
- All rollback events
- Performance anomalies
- Client-impacting errors
Dashboards needed:
- Real-time migration progress
- Comparison: legacy vs new performance
- Validation health
- Alert history
Tools:
- Prometheus (metrics)
- ELK Stack (logs)
- Grafana (dashboards)
- PagerDuty (alerts)
Key Architectural Decisions
Decision 1: API Gateway vs. Client-Side Migration
Chosen: API Gateway pattern
Why:
- Zero client changes required
- Instant traffic control
- Centralized rollback
- Enables shadow testing
Alternative considered: Ask all clients to migrate
- Rejected: Too slow, too risky, no central control
Decision 2: Strangler Fig vs. Big Bang
Chosen: Strangler fig (gradual migration)
Why:
- Limits blast radius
- Enables learning
- Reversible at each step
Alternative considered: Build new system, cutover weekend
- Rejected: Too risky for this scale
Decision 3: Shadow Testing vs. Synthetic Tests Only
Chosen: Shadow testing with real traffic
Why:
- Catches real edge cases
- Validates at production scale
- No synthetic data bias
Alternative considered: Only synthetic/staged tests
- Rejected: Doesn't catch real-world patterns
Decision 4: Generated Code vs. Live AI Validation
Chosen: AI generates test code, code runs deterministically
Why:
- Cost: $2 vs. $1000 for 1000 tests
- Speed: 0.1s vs. 2min per test
- Reliability: Deterministic vs. variable
Alternative considered: Live AI for each comparison
- Rejected: Too slow, too expensive, unreliable
Missing Pieces (Real-World Considerations)
While this covers the high-level architecture, production systems need to address:
1. Data Layer Migration
- How do database schemas evolve?
- How is data synchronized during dual-run?
- What's the eventual consistency strategy?
2. Authentication & Authorization
- Token format changes?
- Session migration?
- Permission model differences?
3. Rate Limiting
- Different limits on old vs. new?
- How to prevent abuse during transition?
4. Backwards Compatibility
- Support timeline for legacy clients?
- API versioning strategy?
5. Cost Analysis
- Infrastructure costs during dual-run period?
- Validation infrastructure expenses?
- TCO comparison: legacy vs. new?
Solutions Architect's Checklist
Before Migration:
- [ ] Traffic analysis and patterns documented
- [ ] All client integrations mapped
- [ ] Dependency graph complete
- [ ] Rollback procedure tested
- [ ] Monitoring baselines established
- [ ] Validation rules defined
- [ ] Team runbooks created
During Migration:
- [ ] Metrics dashboards active
- [ ] On-call rotation established
- [ ] Stakeholder communication plan
- [ ] Error budgets tracked
- [ ] Weekly migration reviews
- [ ] Rollback drills conducted
After Migration:
- [ ] Performance optimization
- [ ] Cost analysis vs. projections
- [ ] Legacy decommission plan
- [ ] Lessons learned documented
- [ ] Team retro completed
Key Takeaways
- Gateway pattern is essential for safe, controlled migration at scale
- Shadow testing validates with real traffic, zero user impact
- Phased rollout limits risk and enables learning
- Sub-30-second rollback is non-negotiable
- Generated validation code beats live AI for cost/speed/reliability
- Observability must be in place before migration starts
Conclusion
Large-scale API migrations are as much about architecture as they are about testing strategy. The patterns discussed here—API gateway, shadow testing, strangler fig, feature flags—form the foundation that makes AI-validated migration possible.
The AI piece solves: "Are these responses equivalent?"
The architecture solves: "How do we migrate safely?"
Both are essential. You can't do one without the other.
About this article:
This is a companion piece to the API Days Paris 2025 talk by Cyrille Martraire and Thomas Nansot. While their talk focused on AI validation strategies, this article explores the underlying architecture. For the AI/validation deep dive, see.
Discussion Questions:
- What migration patterns have worked (or failed) for you?
- How do you balance migration speed vs. safety?
- What observability metrics matter most during API migrations?
Drop your experiences in the comments!
Author: Soumia Ghalim
Role: Solutions Architect | AI • Cloud • Security
LinkedIn
Inspired by API Days Paris 2025 - "AI and APIs: Ensuring Platform Migration Reliability with AI" by Cyrille Martraire and Thomas Nansot
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