Deep Dive: High-Level Architecture for Large-Scale API Migration

Source: Dev.to

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

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:

graph TD
    A[Client Layer (Millions of Users)] --> B[API Gateway<br/>(Traffic Routing)]
    B --> C[Legacy API (Monolithic)]
    B --> D[New API (Orchestration)]
    C --> E[Legacy Business Logic]
    D --> F[Microservices<br/>- Booking<br/>- Pricing<br/>- Inventory]

1. API Gateway Layer

Core responsibility: Enable traffic splitting without client‑side changes.

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

graph LR
    G[API Gateway] --> L[Legacy API]
    G -->|Mirror| N[New API (Silent)]
    N --> V[Validation Pipeline]

How it works

• The client always receives the response from the legacy API.
• The request is mirrored to the new API; the client never sees this response.
• Both responses are fed into a validation pipeline.
• Discrepancies are logged, but there is no client impact.

Benefits

• Real production traffic patterns
• Zero risk to users
• Identification of edge cases missed in testing
• Confidence building before the actual cut‑over

3. Validation Pipeline Architecture

This is where the AI validation piece fits in:

graph TD
    LR[Legacy Response] --> SN[Schema Normalization]
    NR[New Response] --> SN
    SN --> SC[Semantic Comparison Engine<br/>(AI‑Generated Test Code)]
    SC --> SEV[Severity Classification]
    SEV --> MON[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
    }
  }
}

Challenges & Solutions

• Field mapping rules – map legacy fields to new fields.
• Type conversions – e.g., string dates → ISO timestamps.
• Null handling – manage missing fields and differing defaults.
• Semantic validation – ensure functional equivalence, not just structural equality.

The Model Context Protocol (MCP) is valuable here; it lets you query specific JSON paths without loading the entire payload 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 services retired after final verification

Takeaways

• Layered approach (gateway → shadow → validation → transformation) isolates risk at each step.
• Feature‑flag‑driven routing provides instant control over traffic flow.
• Shadow testing gives production‑level confidence without user impact.
• AI‑generated validation ensures semantic equivalence while keeping runtime overhead low.
• Phased rollout (strangler‑fig) enables a safe, observable migration path for massive, latency‑sensitive workloads.