Dependency Injection

FastAPI Depends composes per-request providers for settings, databases, and auth.

Recipe

Quick-reference recipe card - copy-paste ready.

from collections.abc import Generator
from fastapi import Depends, FastAPI
 
app = FastAPI()
 
def get_db() -> Generator[dict, None, None]:
    db = {"items": []}
    yield db
 
@app.get("/items")
def list_items(db: dict = Depends(get_db)):
    return db["items"]

When to reach for this:

Share DB sessions
Compose auth dependencies
Override providers in tests

Working Example

from fastapi import Depends, FastAPI, Header, HTTPException
 
app = FastAPI()
 
def get_settings():
    return {"api_key": "dev"}
 
def verify(x_api_key: str | None = Header(default=None), settings=Depends(get_settings)):
    if x_api_key != settings["api_key"]:
        raise HTTPException(401)
 
@app.get("/secure", dependencies=[Depends(verify)])
def secure() -> dict[str, str]:
    return {"ok": "true"}

What this demonstrates:

Sub-dependencies
Route-level dependencies
Header-based auth gate

Deep Dive

How It Works

Dependencies resolve once per request and cache by callable identity.
yield dependencies run teardown after the response.
Use app.dependency_overrides in tests.

Gotchas

Boundary validation skipped - Invalid data reaches persistence layers.. Fix: Validate with Pydantic or framework forms at the edge..
Leaking stack traces - Clients see internal errors.. Fix: Map exceptions to stable HTTP responses..
Blocking async event loops - Workers stall under concurrent load.. Fix: Use async drivers or threadpool wrappers..
Secrets in source control - Credentials leak via git history.. Fix: Load secrets from env or a vault at runtime..
Missing observability - Incidents are hard to debug.. Fix: Add structured logs, metrics, and request IDs..

Alternatives

Alternative	Use When	Don't Use When
Alternate framework in this cookbook	Team standard or existing monolith	Greenfield API with different constraints
Managed BaaS	CRUD-only MVP	Custom auth, workflows, or compliance needs
gRPC	Internal high-performance RPC	Public HTTP clients and browser access

FAQs

When should I adopt FastAPI dependency injection?

Use it when the patterns and trade-offs on this page match your API or data boundary.

What is the top production mistake with FastAPI dependency injection?

Skipping validation, timeouts, or explicit error contracts at the HTTP edge.

How do I test FastAPI dependency injection?

Use the framework test client, override dependencies, and assert status plus JSON shape.

Does FastAPI dependency injection work with Python 3.14?

Yes - examples target Python 3.14 with pinned framework versions from the stack footer.

How does FastAPI dependency injection relate to Pydantic 2?

Validate and serialize at boundaries; keep services working with typed domain objects.

Sync or async?

Prefer async routes when I/O dominates; keep CPU work small or offload to workers.

Where should business logic live?

Thin handlers; services own rules; repositories own queries.

How do I document APIs?

Publish OpenAPI or schema docs that match response models in code.

How do I handle versioning?

Explicit URL or header versioning with deprecation windows - avoid silent breaks.

What should I read next?

Follow the Related links for the next layer of depth in this section.

How do I stay secure?

Authenticate callers, authorize per resource, rate-limit, and never log secrets.

Performance first step?

Measure DB and upstream latency before swapping frameworks.

FastAPI Basics - Core routes and models
Dependency Injection - Per-request providers
Testing FastAPI - Test client patterns
Deploying FastAPI - Production serving

Stack versions: This page was written for Python 3.14.0 (stable 3.14, maintenance 3.13), FastAPI 0.115+, Django 5.2, Flask 3.1, Pydantic 2, PyTorch 2.6+, pandas 2.2+, Polars 1.x, ruff 0.9+, and uv 0.6+.

Recipe

Quick-reference recipe card - copy-paste ready.

from collections.abc import Generator
from fastapi import Depends, FastAPI
 
app = FastAPI()
 
def get_db() -> Generator[dict, None, None]:
    db = {"items": []}
    yield db
 
@app.get("/items")
def list_items(db: dict = Depends(get_db)):
    return db["items"]

When to reach for this:

Share DB sessions
Compose auth dependencies
Override providers in tests

Working Example

from fastapi import Depends, FastAPI, Header, HTTPException
 
app = FastAPI()
 
def get_settings():
    return {"api_key": "dev"}
 
def verify(x_api_key: str | None = Header(default=None), settings=Depends(get_settings)):
    if x_api_key != settings["api_key"]:
        raise HTTPException(401)
 
@app.get("/secure", dependencies=[Depends(verify)])
def secure() -> dict[str, str]:
    return {"ok": "true"}

What this demonstrates:

Sub-dependencies
Route-level dependencies
Header-based auth gate

Deep Dive

How It Works

Dependencies resolve once per request and cache by callable identity.
yield dependencies run teardown after the response.
Use app.dependency_overrides in tests.

Gotchas

Boundary validation skipped - Invalid data reaches persistence layers.. Fix: Validate with Pydantic or framework forms at the edge..
Leaking stack traces - Clients see internal errors.. Fix: Map exceptions to stable HTTP responses..
Blocking async event loops - Workers stall under concurrent load.. Fix: Use async drivers or threadpool wrappers..
Secrets in source control - Credentials leak via git history.. Fix: Load secrets from env or a vault at runtime..
Missing observability - Incidents are hard to debug.. Fix: Add structured logs, metrics, and request IDs..

Alternatives

Alternative	Use When	Don't Use When
Alternate framework in this cookbook	Team standard or existing monolith	Greenfield API with different constraints
Managed BaaS	CRUD-only MVP	Custom auth, workflows, or compliance needs
gRPC	Internal high-performance RPC	Public HTTP clients and browser access

FAQs

When should I adopt FastAPI dependency injection?

Use it when the patterns and trade-offs on this page match your API or data boundary.

What is the top production mistake with FastAPI dependency injection?

Skipping validation, timeouts, or explicit error contracts at the HTTP edge.

How do I test FastAPI dependency injection?

Use the framework test client, override dependencies, and assert status plus JSON shape.

Does FastAPI dependency injection work with Python 3.14?

Yes - examples target Python 3.14 with pinned framework versions from the stack footer.

How does FastAPI dependency injection relate to Pydantic 2?

Validate and serialize at boundaries; keep services working with typed domain objects.

Sync or async?

Prefer async routes when I/O dominates; keep CPU work small or offload to workers.

Where should business logic live?

Thin handlers; services own rules; repositories own queries.

How do I document APIs?

Publish OpenAPI or schema docs that match response models in code.

How do I handle versioning?

Explicit URL or header versioning with deprecation windows - avoid silent breaks.

What should I read next?

Follow the Related links for the next layer of depth in this section.

How do I stay secure?

Authenticate callers, authorize per resource, rate-limit, and never log secrets.

Performance first step?

Measure DB and upstream latency before swapping frameworks.

FastAPI Basics - Core routes and models
Dependency Injection - Per-request providers
Testing FastAPI - Test client patterns
Deploying FastAPI - Production serving

Stack versions: This page was written for Python 3.14.0 (stable 3.14, maintenance 3.13), FastAPI 0.115+, Django 5.2, Flask 3.1, Pydantic 2, PyTorch 2.6+, pandas 2.2+, Polars 1.x, ruff 0.9+, and uv 0.6+.

Dependency Injection

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Dependency Injection