Devin Case Study: How a Series A Fintech Startup Migrated Django to FastAPI Microservices in 3 Weeks
From Two Quarters to Three Weeks: A Fintech Migration Story with Devin
When a Series A fintech startup faced mounting scalability issues with their legacy Django monolith, the engineering team estimated a two-quarter initiative to migrate to FastAPI microservices. By deploying Devin as an autonomous software engineering agent, they completed the migration in just three weeks — with higher test coverage, zero downtime, and no additional headcount.
The Challenge
The startup’s Django monolith served 50,000+ daily active users across payment processing, KYC verification, and ledger management. The team of six engineers faced three critical problems:
- Response latency exceeding 800ms on core payment endpoints under peak load- Test coverage stalled at 40%, making refactoring risky- Monolithic deployments causing 15-minute maintenance windows every releaseThe CTO’s original plan required hiring two additional senior engineers and allocating six months. Instead, they integrated Devin into their workflow.
Setting Up Devin for the Migration
Step 1: Repository Connection and Context Loading
The team connected their GitHub repository and provided Devin with architectural context through a detailed session prompt:
# Initial Devin session setup
Connect repo and define migration scope
Devin Session Prompt:
“Analyze our Django monolith at /src/core. We need to decompose it into
FastAPI microservices following domain-driven design. The domains are:
- payments (models: Transaction, PaymentMethod, Refund)
- identity (models: User, KYCRecord, Verification)
- ledger (models: Account, Entry, Balance)
Constraints:
- Maintain backward-compatible REST API contracts
- Use async/await for all I/O-bound operations
- Implement circuit breakers for inter-service communication
Target Python 3.11+ with Pydantic v2 for validation”
Step 2: Autonomous Service Scaffolding
Devin analyzed the existing codebase and generated the microservice structure autonomously. Each generated service followed this pattern:
# Example: payments-service/app/main.py (Generated by Devin)
from fastapi import FastAPI, Depends, HTTPException
from contextlib import asynccontextmanager
from app.config import settings
from app.routers import transactions, payment_methods, refunds
from app.middleware.circuit_breaker import CircuitBreakerMiddleware
import httpx
@asynccontextmanager
async def lifespan(app: FastAPI):
app.state.http_client = httpx.AsyncClient(
base_url=settings.IDENTITY_SERVICE_URL,
timeout=httpx.Timeout(10.0, connect=5.0)
)
yield
await app.state.http_client.aclose()
app = FastAPI(title=“Payments Service”, lifespan=lifespan)
app.add_middleware(CircuitBreakerMiddleware, failure_threshold=5, recovery_timeout=30)
app.include_router(transactions.router, prefix=“/api/v1/transactions”)
app.include_router(payment_methods.router, prefix=“/api/v1/payment-methods”)
app.include_router(refunds.router, prefix=“/api/v1/refunds”)
Step 3: Automated PR Generation Workflow
Devin generated pull requests in logical, reviewable units. The team configured the workflow via Devin's planning interface:
# Devin task breakdown configuration
# Each task maps to an autonomous PR
Migration Plan:
Phase 1 - Data Layer (PRs #101-#108):
- Extract SQLAlchemy async models per domain
- Generate Alembic migration scripts
- Create repository pattern abstractions
Phase 2 - Business Logic (PRs #109-#120):
- Migrate Django views to FastAPI route handlers
- Convert synchronous ORM calls to async
- Implement Pydantic v2 request/response schemas
Phase 3 - Infrastructure (PRs #121-#126):
- Docker Compose service definitions
- API gateway routing configuration
- Health check and readiness probe endpoints
Test Coverage Expansion: 40% to 85%
Devin autonomously identified untested code paths and generated comprehensive test suites:
# Example: tests/test_transactions.py (Generated by Devin)
import pytest
from httpx import AsyncClient, ASGITransport
from app.main import app
from app.models.transaction import TransactionStatus
@pytest.fixture
async def client():
transport = ASGITransport(app=app)
async with AsyncClient(transport=transport, base_url=“http://test”) as ac:
yield ac
@pytest.mark.asyncio
async def test_create_transaction_success(client, mock_identity_service):
payload = {
“amount”: 2500,
“currency”: “USD”,
“payment_method_id”: “pm_test_visa_4242”,
“idempotency_key”: “txn_unique_key_001”
}
response = await client.post(“/api/v1/transactions”, json=payload,
headers={“Authorization”: “Bearer YOUR_API_KEY”})
assert response.status_code == 201
data = response.json()
assert data[“status”] == TransactionStatus.PENDING.value
assert data[“amount”] == 2500
@pytest.mark.asyncio
async def test_create_transaction_idempotency(client, mock_identity_service):
payload = {“amount”: 1000, “currency”: “USD”,
“payment_method_id”: “pm_test_visa_4242”,
“idempotency_key”: “txn_duplicate_key”}
resp1 = await client.post(“/api/v1/transactions”, json=payload,
headers={“Authorization”: “Bearer YOUR_API_KEY”})
resp2 = await client.post(“/api/v1/transactions”, json=payload,
headers={“Authorization”: “Bearer YOUR_API_KEY”})
assert resp1.json()[“id”] == resp2.json()[“id”]
Zero-Downtime Deployment Orchestration
Devin generated the deployment strategy using a blue-green pattern with an API gateway layer:
# docker-compose.migration.yml (Generated by Devin)
services:
api-gateway:
build: ./gateway
environment:
- ROUTING_MODE=gradual_migration
- LEGACY_UPSTREAM=http://django-monolith:8000
- PAYMENTS_UPSTREAM=http://payments-service:8001
- IDENTITY_UPSTREAM=http://identity-service:8002
- LEDGER_UPSTREAM=http://ledger-service:8003
- TRAFFIC_SPLIT_PAYMENTS=100 # percentage routed to new service
ports:
- "443:443"
payments-service:
build: ./services/payments
environment:
- DATABASE_URL=postgresql+asyncpg://user:pass@payments-db:5432/payments
- IDENTITY_SERVICE_URL=http://identity-service:8002
healthcheck:
test: [“CMD”, “curl”, “-f”, “http://localhost:8001/health”]
interval: 10s
retries: 3
Results Summary
| Metric | Before (Django Monolith) | After (FastAPI Microservices) |
|---|---|---|
| P95 Latency (payments) | 820ms | 95ms |
| Test Coverage | 40% | 85% |
| Deployment Downtime | ~15 min per release | 0 (rolling deploys) |
| Migration Duration | Planned: 6 months | Actual: 3 weeks |
| PRs Generated by Devin | — | 126 (reviewed by team) |
| Engineering Hires Needed | 2 planned | 0 |
Devin generates Django-style patterns in FastAPI code
This happens when the session context still references the legacy codebase too heavily. Solution: Start a new session for each microservice with only the relevant domain models and explicitly state "Do not use Django patterns. Use FastAPI dependency injection and async repository pattern."
Circular import errors in generated microservices
Devin may mirror the monolith’s import structure. Fix by adding to your prompt: “Use lazy imports for inter-module references and define all Pydantic schemas in a dedicated schemas/ directory per service.”
Test failures due to async event loop conflicts
Ensure your pyproject.toml includes the correct pytest-asyncio configuration:
[tool.pytest.ini_options]
asyncio_mode = “auto”
[tool.pytest]
plugins = [“anyio”]
Circuit breaker tripping during deployment transition
During gradual traffic migration, inter-service calls may hit the legacy monolith. Set the circuit breaker recovery timeout higher (60s) during the transition window and reduce it after full cutover. ## Frequently Asked Questions
How does Devin handle sensitive fintech logic like payment processing during autonomous code generation?
Devin generates code based on the patterns and constraints you provide in the session prompt. For sensitive fintech logic, the team provided explicit compliance requirements — PCI DSS token handling rules, idempotency key enforcement, and audit logging specifications. Every PR Devin generated went through mandatory human code review before merging. The autonomous generation handles the boilerplate and structural migration while engineers focus review time on business-critical payment flows.
Can Devin maintain backward API compatibility during a migration like this?
Yes. The team provided Devin with the existing OpenAPI specification exported from the Django monolith. Devin used this as a contract to ensure all FastAPI route handlers matched the existing request and response schemas exactly. The API gateway then performed gradual traffic shifting, validating response parity between old and new services before full cutover.
What is the realistic team involvement required when using Devin for a migration of this scale?
The six-engineer team spent approximately 30% of their time on Devin-related work during the three weeks: writing detailed session prompts, reviewing the 126 generated PRs, running integration tests against staging environments, and tuning deployment configurations. The remaining 70% of their time continued on product feature work. Without Devin, the migration would have consumed 100% of an expanded eight-person team for six months.