Windsurf Case Study: Fintech Startup Migrates Flask Monolith to FastAPI Microservices in 2 Weeks
The Challenge: A 78,000-Line Flask Monolith Holding Back Growth
PayStream Labs, a Series A fintech startup processing $12M in monthly transactions, faced a critical bottleneck. Their three-year-old Flask monolith had grown to 78,000 lines of code across 142 route handlers, with tightly coupled payment processing, user management, and compliance modules. A single deployment required a 45-minute maintenance window, and scaling any individual component meant scaling the entire application. Their engineering team of six estimated a full manual rewrite to FastAPI microservices would take 12 to 14 weeks. With a major partnership launch locked in for month-end, they had exactly two weeks. That’s when they turned to Windsurf.
Why Windsurf Was Chosen Over Alternatives
The team evaluated Cursor, GitHub Copilot Workspace, and Windsurf. Windsurf’s Cascade agent won out for three reasons: its ability to reason across an entire codebase (not just single files), its persistent context memory across sessions, and its native terminal integration for running tests and deployments inline. For a migration of this scale, whole-codebase awareness was non-negotiable.
Step 1: Setting Up Windsurf for the Migration
The team installed Windsurf and configured it for the existing monolith repository.
# Install Windsurf IDE (macOS example)
brew install —cask windsurf
Open the project
windsurf ~/projects/paystream-flask-monolith
Configure Windsurf settings in .windsurf/settings.json
{
“cascade.model”: “gpt-4o”,
“cascade.contextScope”: “workspace”,
“cascade.memory”: true,
“cascade.maxFileContext”: 120
}
They created a .windsurfrules file at the project root to enforce migration conventions:
# .windsurfrules
You are assisting with a Flask-to-FastAPI migration.
Rules:
- Convert all Flask route decorators to FastAPI router equivalents
- Replace Flask request/response objects with FastAPI dependency injection
- Convert all SQLAlchemy sessions to async using asyncpg
- Preserve all existing business logic without modification
- Generate Pydantic v2 models for every request/response schema
- Maintain backward-compatible API contracts (same paths, same payloads)
Add OpenAPI tags matching the target microservice domain
Step 2: AI-Assisted Code Decomposition
Rather than manually identifying service boundaries, the team used Windsurf Cascade to analyze the monolith and propose a decomposition strategy. They opened Cascade (Cmd+L) and prompted:
Analyze the entire codebase and identify microservice boundaries.
Group route handlers by domain. Map all cross-module dependencies.
Propose a decomposition into independent services with a shared library for common utilities.
Cascade identified five natural service boundaries: **payments**, **users**, **compliance**, **notifications**, and **reporting**. It generated a dependency graph and flagged 23 circular imports that needed resolution before splitting.
Automated Route Conversion
The team then instructed Cascade to convert services one at a time. Here is an example of a Flask route and its Windsurf-generated FastAPI equivalent:
Original Flask (payments/routes.py):
@app.route(‘/api/v1/payments’, methods=[‘POST’])
@login_required
def create_payment():
data = request.get_json()
schema = PaymentSchema().load(data)
result = PaymentService.process(schema, current_user)
return jsonify(result), 201
Windsurf-Generated FastAPI (payments/router.py):
from fastapi import APIRouter, Depends, status
from sqlalchemy.ext.asyncio import AsyncSession
router = APIRouter(prefix=“/api/v1/payments”, tags=[“payments”])
@router.post(”/”, response_model=PaymentResponse, status_code=status.HTTP_201_CREATED)
async def create_payment(
payload: PaymentCreate,
current_user: User = Depends(get_current_user),
db: AsyncSession = Depends(get_async_session),
):
result = await PaymentService.process(payload, current_user, db)
return result
Cascade converted all 142 routes across five services in under four hours, with the team reviewing each batch before committing.
Step 3: Automatic Test Generation
The legacy codebase had only 34% test coverage. The team used Windsurf to generate comprehensive test suites for every converted endpoint:
# In Cascade terminal
@cascade Generate pytest-asyncio tests for every endpoint in
payments/router.py. Include happy path, validation errors,
authentication failures, and edge cases for currency conversion.
Use httpx.AsyncClient with the FastAPI test client pattern.
Windsurf generated 847 test cases across all five services. Example output:
import pytest
from httpx import AsyncClient, ASGITransport
from app.main import app
@pytest.mark.asyncio
async def test_create_payment_success(auth_headers, sample_payment):
async with AsyncClient(
transport=ASGITransport(app=app), base_url=“http://test”
) as client:
response = await client.post(
“/api/v1/payments/”,
json=sample_payment,
headers=auth_headers,
)
assert response.status_code == 201
assert response.json()[“status”] == “processing”
@pytest.mark.asyncio
async def test_create_payment_invalid_currency(auth_headers):
async with AsyncClient(
transport=ASGITransport(app=app), base_url=“http://test”
) as client:
response = await client.post(
“/api/v1/payments/”,
json={“amount”: 100, “currency”: “INVALID”},
headers=auth_headers,
)
assert response.status_code == 422
Test coverage jumped from 34% to 91% in two days.
Step 4: Zero-Downtime Deployment Workflow
The team used a blue-green deployment strategy orchestrated through Docker Compose and an nginx reverse proxy, with Windsurf generating the infrastructure configuration:
# docker-compose.migration.yml (Windsurf-generated)
services:
payments:
build: ./services/payments
environment:
- DATABASE_URL=${PAYMENTS_DB_URL}
- API_KEY=YOUR_API_KEY
healthcheck:
test: [“CMD”, “curl”, “-f”, “http://localhost:8001/health”]
interval: 10s
retries: 3
users:
build: ./services/users
environment:
- DATABASE_URL=${USERS_DB_URL}
- JWT_SECRET=YOUR_JWT_SECRET
healthcheck:
test: [“CMD”, “curl”, “-f”, “http://localhost:8002/health”]
interval: 10s
gateway:
build: ./gateway
ports:
- “443:443”
depends_on:
payments:
condition: service_healthy
users:
condition: service_healthy
The deployment script performed a rolling cutover: each microservice went live behind the API gateway while the monolith continued handling existing connections. Total cutover time: zero downtime, verified by continuous synthetic transaction monitoring.
Results
| Metric | Before (Flask Monolith) | After (FastAPI Microservices) |
|---|---|---|
| Deployment time | 45 minutes (with downtime) | 3 minutes (zero downtime) |
| API response latency (p95) | 320ms | 85ms |
| Test coverage | 34% | 91% |
| Migration duration | 12–14 weeks (estimated manual) | 13 days (actual with Windsurf) |
| Lines of code refactored | 78,000 | 78,000 (across 5 services) |
| Developer hours saved | — | ~1,800 hours |
pytest directly in the Cascade terminal. Windsurf automatically reads failures and offers fixes without re-prompting.- **Generate OpenAPI diffs:** After conversion, ask Cascade to compare the old Flask Swagger spec against the new FastAPI auto-generated schema to catch contract regressions.
## Troubleshooting Common Issues
| Issue | Cause | Solution |
|---|---|---|
| Cascade loses context on large files | File exceeds max token window | Split files before prompting or increase cascade.maxFileContext in settings |
Generated async code raises RuntimeError: no running event loop | Mixing sync SQLAlchemy with async handlers | Ensure all DB sessions use AsyncSession and create_async_engine |
| Tests pass locally but fail in CI | Missing async test fixtures or event loop policy | Add pytest-asyncio to CI requirements and set asyncio_mode = "auto" in pyproject.toml |
| Pydantic validation errors after migration | Pydantic v2 incompatibilities with v1 schema syntax | Prompt Cascade: "Convert all Pydantic models to v2 syntax using model_validator and field_validator" |
| API gateway returns 502 during cutover | Health check failing before service fully boots | Increase start_period in Docker healthcheck to 30s |
Can Windsurf handle migrations for codebases larger than 100,000 lines?
Yes. Windsurf Cascade can reason across workspaces of any size by indexing the full codebase and pulling relevant context on demand. For codebases exceeding 100K lines, the recommended approach is to define service boundaries first using Cascade’s analysis mode, then migrate domain by domain. Teams have reported successful migrations on codebases up to 300K lines using this batched strategy.
Does Windsurf-generated test code require manual review?
Always. While Windsurf generates structurally correct tests with realistic edge cases, the team should review business logic assertions, especially for financial calculations, compliance rules, and security-critical paths. In the PayStream case, approximately 12% of generated tests required assertion adjustments to match exact business requirements.
How does Windsurf compare to using GitHub Copilot for a full migration project?
GitHub Copilot excels at inline code completion within a single file. Windsurf Cascade operates at a fundamentally different level: it reasons across the entire repository, maintains persistent memory of migration decisions, and can execute terminal commands to validate changes. For isolated code suggestions, Copilot is effective. For coordinated, multi-file, multi-service refactoring projects like a monolith-to-microservices migration, Windsurf’s agentic workflow provides significantly more automation and consistency.