Windsurf Case Study: How a Fintech Team Migrated 200K Lines of Python to Microservices in 6 Weeks
Executive Summary
A mid-size fintech company with a 200,000-line Python monolith faced a critical challenge: decompose the codebase into microservices before a regulatory compliance deadline. The projected timeline using manual refactoring was four months. By adopting Windsurf—an AI-powered IDE built on the Codeium engine—the team completed the migration in just six weeks, leveraging multi-file refactoring, Cascade AI flows for dependency analysis, and automated test generation. This case study walks through the exact workflow, tooling configuration, and code-level decisions that made this possible.
The Challenge
- Codebase: 200K lines of Python across 1,400+ files in a Django monolith- Team: 12 engineers (8 backend, 2 DevOps, 2 QA)- Target architecture: 14 FastAPI microservices behind an API gateway- Deadline: 6 weeks due to PCI-DSS audit requirements- Manual estimate: 4 months with 2 additional contract hires
Setting Up Windsurf for the Migration
Step 1: Install Windsurf IDE
The team began by installing Windsurf on all engineering workstations:
# Download and install Windsurf (macOS example)
brew install —cask windsurf
Verify installation
windsurf —version
Open the monolith project
cd /path/to/fintech-monolith
windsurf .
Step 2: Configure Workspace for Multi-Service Architecture
Windsurf was configured with a workspace file to handle the monolith and target microservice repositories simultaneously:
# .windsurf/settings.json
{
"ai.provider": "codeium",
"ai.apiKey": "YOUR_API_KEY",
"cascade.contextDepth": "full-repo",
"cascade.maxFiles": 500,
"refactor.multiFile": true,
"refactor.preserveTests": true,
"python.analysis.typeCheckingMode": "strict",
"workspace.folders": [
{ "path": "./monolith" },
{ "path": "./services/payments" },
{ "path": "./services/accounts" },
{ "path": "./services/notifications" },
{ "path": "./services/compliance" }
]
}
### Step 3: Initialize Cascade for Dependency Analysis
The team used Cascade—Windsurf's AI-powered workflow engine—to map the entire dependency graph of the monolith before writing a single line of new code:
# In Windsurf's Cascade terminal
cascade analyze --project ./monolith \
--output dependency-map.json \
--depth full \
--include-imports \
--include-db-models \
--include-api-routes
Cascade produced a structured dependency map identifying 14 bounded contexts, 87 cross-module dependencies, and 23 circular imports that needed resolution.
Phase 1: AI-Powered Dependency Mapping (Week 1)
Using Cascade flows, the team prompted Windsurf to identify service boundaries:
# Cascade Flow prompt (entered in Windsurf AI panel)
Prompt: “Analyze the Django monolith and identify bounded contexts
suitable for microservice extraction. Group models, views, serializers,
and utilities by domain. Flag circular dependencies.”
Cascade output generated a structured extraction plan:
Service 1: payments (42 files, 31K lines)
Service 2: accounts (38 files, 28K lines)
Service 3: compliance (29 files, 22K lines)
… (14 services total)
The AI identified that the payments module had hidden dependencies on accounts.models.UserProfile in 47 locations—something the team had underestimated in manual analysis.
Phase 2: Multi-File Refactoring (Weeks 2–4)
Windsurf’s multi-file refactoring capability was the core accelerator. Rather than manually extracting one file at a time, the team used Cascade to execute bulk operations:
# Example: Extracting the payments service
Cascade Flow command in Windsurf AI panel:
Prompt: “Extract the payments bounded context into a standalone
FastAPI service. Replace Django ORM models with SQLAlchemy. Convert
all Django REST Framework serializers to Pydantic models. Replace
direct database calls to accounts with HTTP client calls to the
accounts service API.”
Windsurf modified 42 files simultaneously, producing:
- services/payments/app/models.py (SQLAlchemy models)
- services/payments/app/schemas.py (Pydantic schemas)
- services/payments/app/routes/ (FastAPI routers)
- services/payments/app/clients/accounts.py (HTTP client)
A concrete example of the transformed code:
# BEFORE: monolith/payments/views.py (Django)
from accounts.models import UserProfile
class ProcessPaymentView(APIView):
def post(self, request):
user = UserProfile.objects.get(id=request.data[‘user_id’])
# … payment logic with direct DB access
AFTER: services/payments/app/routes/process.py (FastAPI)
from app.clients.accounts import AccountsClient
from app.schemas import PaymentRequest, PaymentResponse
router = APIRouter()
accounts_client = AccountsClient(base_url=settings.ACCOUNTS_SERVICE_URL)
@router.post(“/payments/process”, response_model=PaymentResponse)
async def process_payment(payload: PaymentRequest):
user = await accounts_client.get_user(payload.user_id)
# … payment logic with service-to-service HTTP calls
Phase 3: Automated Test Generation (Weeks 4–5)
The monolith had 62% test coverage. The team needed each new microservice to reach 80%+ coverage for the compliance audit. Windsurf's test generation filled the gap:
# Cascade Flow prompt:
# "Generate pytest test suites for the payments service. Include
# unit tests for all Pydantic schemas, integration tests for each
# API route using httpx.AsyncClient, and mock the accounts service
# client. Target 85% branch coverage."
Generated test example:
import pytest
from httpx import AsyncClient, ASGITransport
from unittest.mock import AsyncMock, patch
from app.main import app
@pytest.mark.asyncio
async def test_process_payment_success():
mock_user = {“id”: “usr_123”, “status”: “active”, “tier”: “premium”}
with patch(“app.clients.accounts.AccountsClient.get_user”,
new_callable=AsyncMock, return_value=mock_user):
transport = ASGITransport(app=app)
async with AsyncClient(transport=transport, base_url=“http://test”) as client:
response = await client.post(“/payments/process”, json={
“user_id”: “usr_123”,
“amount”: 150.00,
“currency”: “USD”
})
assert response.status_code == 200
assert response.json()[“status”] == “completed”
Windsurf generated 1,240 test cases across all 14 services, achieving an average of 83% branch coverage.
Results
| Metric | Manual Estimate | With Windsurf | Improvement |
|---|---|---|---|
| Timeline | 16 weeks | 6 weeks | 62% faster |
| Engineers required | 14 (incl. 2 contractors) | 12 (existing team) | No additional hires |
| Test coverage | 62% (legacy) | 83% (new services) | +21 percentage points |
| Files refactored | 1,400+ | 1,400+ | AI handled 78% of edits |
| Circular dependencies resolved | 23 | 23 | All identified by Cascade |
| Production incidents (first 30 days) | N/A | 2 (minor) | Below team average |
@context recall payments-extraction to reload prior Cascade context instead of re-explaining the project.- **Batch refactoring by domain:** Rather than extracting file-by-file, prompt Cascade with the entire bounded context. Multi-file awareness prevents broken imports.- **Pin critical files:** Use @pin monolith/payments/models.py in Cascade flows to ensure the AI always references your source-of-truth data models during extraction.- **Validate with dry runs:** Before applying bulk refactors, use cascade refactor --dry-run to preview all changes in a diff view.- **Custom rules file:** Create .windsurf/rules.md with project-specific conventions (e.g., "Always use async def for route handlers", "Use dependency injection for service clients") to keep AI output consistent with team standards.
## Troubleshooting Common Issues
| Issue | Cause | Solution |
|---|---|---|
| Cascade times out on large files | Context window limit exceeded with files over 5K lines | Split large modules before analysis. Use cascade.maxFiles setting to control batch size. |
| Refactored imports are incorrect | Circular dependencies confuse the import resolver | Run cascade analyze --circular-only first, resolve cycles manually, then proceed with extraction. |
| Generated tests fail with async errors | Missing pytest-asyncio configuration | Add [tool.pytest.ini_options] asyncio_mode = "auto" to pyproject.toml. |
| AI generates Django patterns in FastAPI service | Monolith context bleeding into service generation | Close the monolith workspace folder or add exclusion rules in .windsurf/settings.json. |
| Multi-file refactor produces partial changes | Network interruption during AI generation | Use version control checkpoints. Run git stash before large refactors and verify diffs before committing. |
Can Windsurf handle monoliths larger than 200K lines?
Yes. Windsurf’s Cascade engine processes repositories incrementally by analyzing bounded contexts rather than loading the entire codebase into memory at once. Teams have reported success with codebases exceeding 500K lines by configuring cascade.contextDepth to target specific modules and using the @pin directive to focus the AI on relevant files. For very large projects, a phased approach—analyzing one domain at a time—yields the best results.
How does Windsurf’s automated test generation compare to writing tests manually?
Windsurf generates structurally correct tests that cover happy paths, edge cases, and error conditions based on the function signatures, type hints, and existing patterns in your codebase. In this case study, approximately 85% of generated tests required no modification. The remaining 15% needed minor adjustments—primarily around business-logic assertions that required domain knowledge the AI could not infer from code alone. The key advantage is speed: generating 1,240 tests took hours instead of the weeks manual writing would require.
What is the learning curve for adopting Windsurf on an existing team?
Most engineers in this case study were productive within two to three days. Windsurf’s interface is based on VS Code, so developers familiar with that editor experienced minimal friction. The primary learning curve involves writing effective Cascade prompts—being specific about target frameworks, naming conventions, and architectural patterns produces significantly better output than vague instructions. The team established a shared .windsurf/rules.md file within the first week to standardize prompt conventions across all engineers.