Feature flags decouple deployment from release. They let you ship code to production and control who sees what, when. This guide covers feature management strategies, implementation patterns, and building confidence in your releases.
What Are Feature Flags?
Feature flags are boolean controls that toggle features on/off without deploying new code:
# Simple feature flag
if feature_flags.is_enabled("new_checkout"):
return render_new_checkout()
else:
return render_old_checkout()
Architecture Overview
A feature flag system consists of several layers that work together:
┌─────────────────────────────────────────────────────────────┐
│ Feature Flag System │
├─────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ SDKs │ │ Dashboard │ │ Analytics │ │
│ │ (Web, │◄──▶│ (Manage, │◄──▶│ (Track, │ │
│ │ Mobile) │ │ Monitor) │ │ Analyze) │ │
│ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ │
│ │ │ │ │
│ └──────────────────┼───────────────────┘ │
│ ▼ │
│ ┌─────────────────────┐ │
│ │ Flag Service │ │
│ │ (Evaluate, Rules) │ │
│ └──────────┬──────────┘ │
│ │ │
│ ┌───────────────────┼───────────────────┐ │
│ ▼ ▼ ▼ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ Redis │ │ Database │ │ Config │ │
│ │ Cache │ │ (Rules) │ │ (Static) │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
│ │
└─────────────────────────────────────────────────────────────┘
Core Concepts and Terminology
Feature Flag: A switch that controls whether a feature is enabled or disabled. A boolean value (true/false) that determines if a code path should execute.
Variant: In A/B testing, variants are different versions of a feature. Instead of just true/false, variants allow multiple options. For example, a button color test might have variants: control (blue), variant_a (green), variant_b (red).
Context: Information about the current user or request that determines which variant they should see. This includes user ID, tenant, plan tier, location, device, and other relevant attributes.
{
"user_id": "user_123",
"tenant_id": "tenant_456",
"plan": "enterprise",
"country": "US",
"device": "mobile"
}
Rollout Percentage: The percentage of users who should see a feature. Enables gradual rollouts starting at 1% and increasing over time.
Targeting Rules: Specific conditions that determine who sees a feature, going beyond simple percentages to include user attributes. For example: “Enable for enterprise users in the US with more than 100 employees.”
Evaluation: The process of determining which variant a user should see based on their context and the flag’s rules. Consistent hashing ensures the same user always gets the same variant.
Backing Out: The ability to quickly disable a feature without redeploying code. This is the emergency off-ramp that makes feature flags valuable.
Types of Feature Flags
| Type | Purpose | Lifetime |
|---|---|---|
| Release Flags | Hide unfinished features | Short-term |
| Experiment Flags | A/B testing | Medium-term |
| Operational Flags | Kill switches, config | Long-term |
| Permission Flags | User-specific features | Long-term |
Implementing Feature Flags
Simple In-Code Implementation
class FeatureFlags:
def __init__(self):
self._flags = {
"new_checkout": False,
"dark_mode": True,
"ai_recommendations": False,
"beta_dashboard": ["user-1", "user-2", "user-3"], # Users list
"percentage_rollout": 10, # 10% rollout
}
def is_enabled(self, flag_name: str, user_id: str = None) -> bool:
flag = self._flags.get(flag_name)
if flag is None:
return False
if isinstance(flag, bool):
return flag
if isinstance(flag, list):
return user_id in flag
if isinstance(flag, int):
# Percentage rollout
user_hash = hash(f"{flag_name}:{user_id}") % 100
return user_hash < flag
return False
# Usage
flags = FeatureFlags()
if flags.is_enabled("new_checkout", user_id="user-123"):
return NewCheckoutPage()
else:
return OldCheckoutPage()
Using a Feature Flag Service
import requests
class LaunchDarklyClient:
def __init__(self, api_key, project_key):
self.api_key = api_key
self.project_key = project_key
self.base_url = "https://app.launchdarkly.com"
def is_enabled(self, flag_key: str, user_key: str) -> bool:
response = requests.get(
f"{self.base_url}/flags/{self.project_key}/{flag_key}",
headers={"Authorization": api_key}
)
# Evaluate targeting rules
return evaluate_flag(response.json(), user_key)
# Or use the SDK
import launchdarkly_server_sdk
ld_client = launchdarkly_server_sdk.init("api-key")
user = {"key": "user-123", "email": "[email protected]"}
show_new_feature = ld_client.variation("new-checkout", user, False)
In Configuration
# config/features.yaml
features:
new_checkout:
enabled: false
rollout_percentage: 0
dark_mode:
enabled: true
rollout_percentage: 100
ai_recommendations:
enabled: true
rollout_percentage: 10
target:
- email: "*@company.com"
- plan: "premium"
beta_dashboard:
enabled: true
target_users:
- user-1
- user-2
Class-Based Feature Flag Architecture
For enterprise systems, use a class hierarchy with clear separation of concerns:
class FeatureFlag:
"""Base class for feature flag evaluation."""
def __init__(self, key: str, default: bool = False):
self.key = key
self.default = default
def evaluate(self, context: dict) -> bool:
raise NotImplementedError
class SimpleFeatureFlag(FeatureFlag):
"""Simple on/off flag."""
def __init__(self, key: str, enabled: bool = False):
super().__init__(key)
self.enabled = enabled
def evaluate(self, context: dict) -> bool:
return self.enabled
class PercentageFeatureFlag(FeatureFlag):
"""Percentage-based rollout flag."""
def __init__(self, key: str, percentage: int = 0):
super().__init__(key)
self.percentage = max(0, min(100, percentage))
def evaluate(self, context: dict) -> bool:
user_id = context.get('user_id', 'anonymous')
hash_value = int(hashlib.md5(
f"{self.key}:{user_id}".encode()
).hexdigest(), 16)
bucket = hash_value % 100
return bucket < self.percentage
class TargetingFeatureFlag(FeatureFlag):
"""Flag with targeted user/group rules."""
def __init__(self, key: str, rules: dict):
super().__init__(key)
self.rules = rules
def evaluate(self, context: dict) -> bool:
# Check specific users
if 'users' in self.rules:
if context.get('user_id') in self.rules['users']:
return True
# Check user attributes
for attr, values in self.rules.get('attributes', {}).items():
if context.get(attr) in values:
return True
# Fall back to percentage-based rollout
percentage = self.rules.get('percentage', 0)
hash_value = int(hashlib.md5(
f"{self.key}:{context.get('user_id', 'anonymous')}".encode()
).hexdigest(), 16)
return (hash_value % 100) < percentage
Evaluation Engine with Caching
class FeatureEngine:
"""Feature flag evaluation engine with caching."""
def __init__(self):
self.flags = {}
self.cache = {}
def load_flags(self, config: dict):
for key, config in config.items():
flag_type = config.get('type', 'simple')
if flag_type == 'simple':
self.flags[key] = SimpleFeatureFlag(key, config.get('enabled', False))
elif flag_type == 'percentage':
self.flags[key] = PercentageFeatureFlag(key, config.get('percentage', 0))
elif flag_type == 'targeting':
self.flags[key] = TargetingFeatureFlag(key, config.get('rules', {}))
def is_enabled(self, key: str, context: dict = None) -> bool:
context = context or {}
if key not in self.flags:
return False
return self.flags[key].evaluate(context)
def get_variant(self, key: str, context: dict = None) -> str:
"""Get variant for A/B testing with caching."""
variant_key = f"{key}:variant:{context.get('user_id', 'anonymous')}"
if variant_key in self.cache:
return self.cache[variant_key]
hash_value = int(hashlib.md5(
f"{key}:{context.get('user_id')}".encode()
).hexdigest(), 16)
variants = ['control', 'variant_a']
variant = variants[hash_value % len(variants)]
self.cache[variant_key] = variant
return variant
FastAPI Middleware Integration
from fastapi import Request
class FeatureMiddleware:
"""Inject feature flags into request scope."""
def __init__(self, app, feature_engine):
self.app = app
self.engine = feature_engine
async def __call__(self, scope, receive, send):
if scope['type'] == 'http':
context = {
'user_id': dict(scope.get('headers', {})).get(b'x-user-id', b'anonymous').decode(),
'plan': dict(scope.get('headers', {})).get(b'x-user-plan', b'free').decode()
}
scope['features'] = {
key: self.engine.is_enabled(key, context)
for key in self.engine.flags.keys()
}
await self.app(scope, receive, send)
# Usage
@app.get("/dashboard")
async def dashboard(request: Request):
if request.app.state.features.get('new_dashboard'):
return NewDashboard()
return OldDashboard()
Canary Releases
Gradually roll out changes to a small percentage of users:
import hashlib
class CanaryRelease:
def __init__(self, service_name: str):
self.service_name = service_name
self.rollout_percentage = 10
def should_route_to_canary(self, user_id: str = None) -> bool:
if not user_id:
# Use random for no-user requests
import random
return random.random() * 100 < self.rollout_percentage
# Consistent hashing - same user always gets same result
hash_value = int(hashlib.md5(
f"{self.service_name}:{user_id}".encode()
).hexdigest(), 16)
return (hash_value % 100) < self.rollout_percentage
# Usage with API Gateway
@app.route("/api/<path:endpoint>")
async def proxy(endpoint, request):
user_id = get_user_id(request)
canary = CanaryRelease("order-service")
if canary.should_route_to_canary(user_id):
# Route to canary version
return await proxy_to("order-service-v2", request)
else:
# Route to stable version
return await proxy_to("order-service-v1", request)
Kubernetes Canary
# Canary deployment with Istio
apiVersion: networking.istio.io/v1beta1
kind: VirtualService
metadata:
name: order-service
spec:
hosts:
- order-service
http:
- route:
- destination:
host: order-service-v1
weight: 90
- destination:
host: order-service-v2
weight: 10
Flagger Canary
apiVersion: flagger.app/v1beta1
kind: Canary
metadata:
name: order-service
spec:
targetRef:
apiVersion: apps/v1
kind: Deployment
name: order-service
service:
port: 80
canaryAnalysis:
interval: 1m
threshold: 10
maxWeight: 50
stepWeight: 10
metrics:
- name: request-success-rate
threshold: 99
interval: 1m
- name: error-rate
threshold: 5
interval: 1m
A/B Testing
Test different variations with user segments:
class Experiment:
def __init__(self, name: str, variations: dict):
"""
variations: {
"control": 50, # 50% weight
"variant_a": 25,
"variant_b": 25
}
"""
self.name = name
self.variations = variations
def get_variant(self, user_id: str = None) -> str:
if not user_id:
import random
r = random.random() * 100
else:
# Consistent hashing
hash_value = int(hashlib.md5(
f"{self.name}:{user_id}".encode()
).hexdigest(), 16)
r = (hash_value % 10000) / 100
cumulative = 0
for variant, weight in self.variations.items():
cumulative += weight
if r < cumulative:
return variant
return "control"
# Usage
checkout_experiment = Experiment(
name="new_checkout",
variations={"control": 50, "variant_a": 25, "variant_b": 25}
)
variant = checkout_experiment.get_variant(user_id="user-123")
if variant == "control":
return render_old_checkout()
elif variant == "variant_a":
return render_checkout_with_visa()
else:
return render_checkout_with_paypal()
Tracking Results
import analytics
class ExperimentTracker:
def __init__(self, experiment_name: str):
self.experiment_name = experiment_name
def track_assignment(self, user_id: str, variant: str):
analytics.track(
user_id,
"Experiment Assigned",
{
"experiment_name": self.experiment_name,
"variant": variant
}
)
def track_conversion(self, user_id: str, event_name: str, properties: dict = None):
analytics.track(
user_id,
event_name,
{
"experiment_name": self.experiment_name,
**(properties or {})
}
)
# Usage
tracker = ExperimentTracker("new_checkout")
@app.route("/checkout")
def checkout():
variant = checkout_experiment.get_variant(user_id)
tracker.track_assignment(user_id, variant)
if variant == "control":
return render_checkout_control()
return render_checkout_variant()
@app.route("/purchase")
def purchase():
# Track conversion
tracker.track_conversion(user_id, "purchase_completed", {
"total": 99.99,
"variant": checkout_experiment.get_variant(user_id)
})
Kill Switches
Quickly disable problematic features:
class KillSwitch:
def __init__(self):
self._switches = {}
def enable(self, feature: str):
self._switches[feature] = True
def disable(self, feature: str):
self._switches[feature] = False
def is_active(self, feature: str) -> bool:
return self._switches.get(feature, True)
async def wrap(self, feature: str, func, *args, **kwargs):
if not self.is_active(feature):
return {"error": f"Feature {feature} is disabled"}
try:
return await func(*args, **kwargs)
except Exception as e:
# Auto-disable on error
logger.error(f"Feature {feature} failed, disabling", error=str(e))
self.disable(feature)
raise
# Usage
killswitch = KillSwitch()
@killswitch.wrap("ai_recommendations", ai_service.get_recommendations, user_id)
async def get_recommendations(user_id):
return await ai_service.get_recommendations(user_id)
Gradual Rollout
Increase rollout based on metrics:
class GradualRollout:
def __init__(self, feature_name: str, initial_percentage: int = 1):
self.feature_name = feature_name
self.current_percentage = initial_percentage
self.metrics = {"errors": 0, "success": 0, "p50": [], "p99": []}
def record_success(self, latency_ms: int):
self.metrics["success"] += 1
self.metrics["p50"].append(latency_ms)
self.metrics["p99"].append(latency_ms)
def record_error(self):
self.metrics["errors"] += 1
def should_increase(self) -> bool:
total = self.metrics["success"] + self.metrics["errors"]
if total < 1000:
return False
error_rate = self.metrics["errors"] / total
p99_latency = sorted(self.metrics["p99"])[int(len(self.metrics["p99"]) * 0.99)]
# Increase if error rate < 1% and p99 < 500ms
return error_rate < 0.01 and p99_latency < 500
def promote(self):
if self.should_increase() and self.current_percentage < 100:
self.current_percentage = min(100, self.current_percentage * 2)
logger.info(f"Promoted {self.feature_name} to {self.current_percentage}%")
# Reset metrics for next phase
self.metrics = {"errors": 0, "success": 0, "p50": [], "p99": []}
Sample Size Calculation for Experiments
Statistical significance depends on having enough data. Calculate required sample sizes before running experiments:
import math
from scipy import stats
class SampleSizeCalculator:
"""Calculate required sample size for A/B tests."""
def calculate_sample_size(self,
baseline_rate: float,
min_detectable_effect: float,
alpha: float = 0.05,
power: float = 0.8) -> int:
"""Calculate required sample size per variant."""
z_alpha = stats.norm.ppf(1 - alpha/2)
z_beta = stats.norm.ppf(power)
control_rate = baseline_rate
variant_rate = baseline_rate * (1 + min_detectable_effect)
pooled_rate = (control_rate + variant_rate) / 2
sample_size = (
(z_alpha + z_beta) ** 2 *
(control_rate * (1 - control_rate) + variant_rate * (1 - variant_rate))
) / (variant_rate - control_rate) ** 2
return math.ceil(sample_size)
def calculate_duration(self, sample_size: int, daily_traffic: int) -> int:
"""Calculate test duration in days."""
return math.ceil(sample_size / daily_traffic)
# Usage
calculator = SampleSizeCalculator()
n = calculator.calculate_sample_size(
baseline_rate=0.05, # 5% baseline conversion
min_detectable_effect=0.1, # Want to detect 10% relative change
alpha=0.05, # 95% confidence
power=0.8 # 80% power
)
print(f"Need {n} users per variant")
S-Curve Rollout Strategy
An S-curve rollout starts slowly, accelerates through the middle, and slows at the end — matching how adoption naturally occurs:
from datetime import datetime
import math
class SCurveRollout:
"""S-curve rollout with logistic function."""
def __init__(self, start_percentage: int = 1,
end_percentage: int = 100,
duration_hours: int = 72):
self.start_percentage = start_percentage
self.end_percentage = end_percentage
self.duration_hours = duration_hours
self.start_time = datetime.utcnow()
def get_current_percentage(self) -> int:
elapsed = (datetime.utcnow() - self.start_time).total_seconds() / 3600
if elapsed >= self.duration_hours:
return self.end_percentage
progress = elapsed / self.duration_hours
k = 10 # Steepness factor
# Logistic function for S-curve
percentage = self.start_percentage + (
(self.end_percentage - self.start_percentage) /
(1 + math.exp(-k * (progress - 0.5)))
)
return int(percentage)
Canary Rollout with Health Checks
class CanaryRollout:
"""Canary rollout with automated health verification."""
def __init__(self, initial_percentage: int = 1,
increment: int = 5,
health_interval: int = 300):
self.current_percentage = initial_percentage
self.increment = increment
self.health_interval = health_interval
self.errors = []
def should_enable(self, user_id: str) -> bool:
hash_value = int(hashlib.md5(f"canary:{user_id}".encode()).hexdigest(), 16)
return (hash_value % 100) < self.current_percentage
def record_error(self, error: str):
self.errors.append({
'timestamp': datetime.utcnow(),
'error': error
})
def should_increase(self) -> bool:
if len(self.errors) < 10:
return True
recent = [e for e in self.errors
if (datetime.utcnow() - e['timestamp']).total_seconds() < 3600]
error_rate = len(recent) / len(self.errors)
return error_rate < 0.01 # Less than 1% error rate
Feature Flag Management UI
Build a simple admin interface:
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import Optional, List
app = FastAPI()
class FeatureFlag(BaseModel):
name: str
enabled: bool
rollout_percentage: int = 100
target_users: Optional[List[str]] = None
flags_db = {}
@app.get("/flags")
def list_flags():
return flags_db
@app.post("/flags")
def create_flag(flag: FeatureFlag):
flags_db[flag.name] = flag
return flag
@app.put("/flags/{flag_name}")
def update_flag(flag_name: str, flag: FeatureFlag):
if flag_name not in flags_db:
raise HTTPException(status_code=404, detail="Flag not found")
flags_db[flag_name] = flag
return flag
@app.delete("/flags/{flag_name}")
def delete_flag(flag_name: str):
if flag_name in flags_db:
del flags_db[flag_name]
return {"status": "deleted"}
Tools Comparison
| Tool | Type | Features | Pricing |
|---|---|---|---|
| LaunchDarkly | Managed | Full-featured | $$$ |
| Split.io | Managed | A/B testing | $$$ |
| Unleash | Open-source | Self-hosted option | Free/$ |
| FeatureFlags.io | Open-source | Simple | Free |
| Cloudflare Workers | Built-in | Edge | $ |
| Custom | Build your own | Flexible | Free |
Best Practices
Do
- Keep flags short-lived
- Clean up after rollout
- Monitor flag metrics
- Have rollback plan
- Use meaningful names
Don’t
- Don’t use for every small change
- Don’t nest flags deeply
- Don’t forget to clean up
- Don’t ignore failure modes
Anti-Patterns to Avoid
1. Flag Sprawl: Creating flags for every minor change leads to a tangled mess. Use flags for significant features only and clean up after rollout complete.
2. Flag Debt: Leaving flags in code for years creates dead code paths and confusion. Set expiration dates and remove flags after successful rollout.
3. No Monitoring: Flags that aren’t monitored for performance impact create blind spots. Track error rates, latency, and user feedback for every active flag.
4. Complex Flag Logic: Deeply nested flag conditions make code impossible to reason about. Keep flag logic simple and use targeting rules instead of conditional nesting.
5. Bypassing Flags in Tests: Test both flag states explicitly. Bugs often hide in the disabled code path that nobody tests.
// Test both flag states
it('should show new dashboard when flag enabled', () => {
flags.enable('new-dashboard');
const result = renderDashboard();
expect(result).toContain('New Feature');
});
it('should show old dashboard when flag disabled', () => {
flags.disable('new-dashboard');
const result = renderDashboard();
expect(result).toContain('Legacy Dashboard');
});
Postgres-Based Flags
# Self-hosted feature flags
class PostgresFeatureFlags:
def __init__(self, db_pool):
self.db = db_pool
def get_flags(self, user_id):
"""Get all flags for user."""
query = """
SELECT f.name, f.enabled, f.rollout_percentage
FROM feature_flags f
WHERE f.enabled = true
"""
flags = self.db.execute(query)
return {
f["name"]: self._evaluate_flag(f, user_id)
for f in flags
}
def _evaluate_flag(self, flag, user_id):
if flag["rollout_percentage"] is None:
return True
return hash(f"{flag['name']}:{user_id}") % 100 < flag["rollout_percentage"]
Unleash Feature Flags
# Unleash client
from unleash_client import UnleashClient
client = UnleashClient(
url="https://unleash.example.com/api",
app_name="payment-service",
environment="prod"
)
client.start()
# Check flag
if client.is_enabled("new-payment-flow"):
enable_new_flow()
Experimentation
A/B Testing Setup
# Simple A/B test implementation
class Experiment:
def __init__(self, name, variants):
"""
variants: {"control": 50, "treatment": 50}
"""
self.name = name
self.variants = variants
def assign_variant(self, user_id):
"""Assign user to variant."""
# Consistent assignment
bucket = hash(f"{self.name}:{user_id}") % 100
cumulative = 0
for variant, percentage in self.variants.items():
cumulative += percentage
if bucket < cumulative:
return variant
return "control"
# Usage
checkout_experiment = Experiment(
"new_checkout",
{"control": 50, "treatment": 50}
)
variant = checkout_experiment.assign_variant(current_user.id)
if variant == "treatment":
show_new_checkout()
track_event("checkout_viewed", {"variant": "treatment"})
else:
show_old_checkout()
track_event("checkout_viewed", {"variant": "control"})
Statistical Analysis
# Calculate experiment results
import math
from dataclasses import dataclass
@dataclass
class ExperimentResults:
control_conversions: int
treatment_conversions: int
control_size: int
treatment_size: int
@property
def control_rate(self):
return self.control_conversions / self.control_size
@property
def treatment_rate(self):
return self.treatment_conversions / self.treatment_size
def confidence_level(self):
"""Calculate statistical significance."""
n1, n2 = self.control_size, self.treatment_size
p1, p2 = self.control_rate, self.treatment_rate
# Pooled proportion
p = (self.control_conversions + self.treatment_conversions) / (n1 + n2)
# Standard error
se = math.sqrt(p * (1 - p) * (1/n1 + 1/n2))
# Z-score
z = (p2 - p1) / se
# Convert to confidence
if z > 1.96:
return "95% confident - significant"
elif z > 1.645:
return "90% confident"
else:
return "Not significant"
Kubernetes Canary Deployments
# Kubernetes canary with feature flags
apiVersion: v1
kind: Service
metadata:
name: payment-service
spec:
selector:
app: payment-service
ports:
- port: 80
targetPort: 8080
---
# Stable deployment (90%)
apiVersion: apps/v1
kind: Deployment
metadata:
name: payment-service-stable
spec:
replicas: 9
selector:
matchLabels:
app: payment-service
version: stable
template:
spec:
containers:
- name: payment
image: payment-service:v2.0
env:
- name: FEATURE_NEW_CHECKOUT
value: "false"
---
# Canary deployment (10%)
apiVersion: apps/v1
kind: Deployment
metadata:
name: payment-service-canary
spec:
replicas: 1
selector:
matchLabels:
app: payment-service
version: canary
template:
spec:
containers:
- name: payment
image: payment-service:v2.0
env:
- name: FEATURE_NEW_CHECKOUT
value: "true"
Progressive Rollout
# Automated progressive rollout
async def progressive_rollout(flag_name, target_percentage, step=10):
"""Gradually increase rollout percentage."""
current = 0
while current < target_percentage:
current += step
# Update flag
await update_flag_percentage(flag_name, current)
# Wait and monitor
await wait(1 hour)
# Check metrics
error_rate = await get_error_rate(flag_name)
if error_rate > 0.01: # 1% threshold
print(f"Error rate {error_rate} exceeded threshold, rolling back")
await update_flag_percentage(flag_name, 0)
break
print(f"Rollout: {current}% - error rate: {error_rate}")
Progressive Rollout
# Automated progressive rollout
async def progressive_rollout(flag_name, target_percentage, step=10):
"""Gradually increase rollout percentage."""
current = 0
while current < target_percentage:
current += step
# Update flag
await update_flag_percentage(flag_name, current)
# Wait and monitor
await wait(1 hour)
# Check metrics
error_rate = await get_error_rate(flag_name)
if error_rate > 0.01: # 1% threshold
print(f"Error rate {error_rate} exceeded threshold, rolling back")
await update_flag_percentage(flag_name, 0)
break
print(f"Rollout: {current}% - error rate: {error_rate}")
Conclusion
Feature flags transform how you ship software:
- Separate deployment from release
- Enable canary and gradual rollouts
- Run A/B experiments safely
- Kill switches for quick response
- Build your own or use a service
Start with simple flags, add experimentation, then build full progressive delivery.
External Resources
Related Articles
- CI/CD Pipelines - Deployment automation
- Software Architecture Patterns - Progressive delivery patterns
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