DRF is mature, batteries-included, and synchronous by default. FastAPI is async-first and considerably faster under I/O-bound load — the reason most teams consider the migration isn't "FastAPI is newer," it's a specific throughput ceiling they've hit with sync workers under concurrent load.
Strangle, don't rewrite
A full rewrite of a 200-endpoint API is how migrations die halfway through. We run FastAPI and Django side by side behind the same reverse proxy, routing endpoints over one at a time as each is verified. Django keeps owning the ORM and migrations throughout — FastAPI talks to the same PostgreSQL database via SQLAlchemy Core (not the ORM, to avoid a second migration system) or by calling into Django's ORM directly with sync_to_async for read-heavy endpoints that aren't yet worth a full async rewrite.
Serializers become Pydantic models, mechanically
DRF serializers and Pydantic models solve the same problem — validate input, shape output — with different syntax. The migration is mostly mechanical:
# DRF
class OrderSerializer(serializers.ModelSerializer):
class Meta:
model = Order
fields = ["id", "status", "total"]
# FastAPI
class OrderOut(BaseModel):
id: int
status: str
total: Decimal
model_config = ConfigDict(from_attributes=True)
The parts that aren't mechanical: DRF's SerializerMethodField and nested writable serializers don't map 1:1 — those became explicit Pydantic field_validators and separate read/write model pairs, which is more verbose but considerably easier to reason about once written.
Auth is the riskiest part
We kept Django's session and token auth as the source of truth and had FastAPI validate the same JWTs / session cookies against Django's auth backend, rather than standing up a parallel auth system. This is the step we'd tell any team to over-invest in testing — an auth regression during a migration is the failure mode that erodes trust in the whole project, even if everything else goes smoothly.
What actually got faster
The throughput gain came almost entirely from I/O-bound endpoints — anything doing multiple sequential external API calls or database queries that could now run concurrently under asyncio instead of blocking a sync worker thread. CPU-bound endpoints (heavy serialization, report generation) saw no meaningful change, which matches the theory: async doesn't make Python compute faster, it makes waiting cheaper.
Eight weeks, zero downtime, and — critically — nobody outside the backend team noticed the migration happened until we told them. That's the actual success metric for this kind of work.
