New Portal Experience for Feature Management

nuzhat-minhaz

Microsoft

Nov 18, 2025

Azure App Configuration's Feature manager gets a new look with three scenario-based feature flag creation journey that starts with intent.

Feature flags have become an essential tool for modern software development, enabling teams to deploy code safely, control feature rollouts, and experiment with new functionality without the risk of breaking production environments. As AI becomes increasingly integrated into applications— from LLM-powered features to model version management— the need for safe, controlled deployments has never been more critical. The previous experience forced you to make a decision upfront: should I create a feature flag or a variant feature flag? What's the difference between these options? Which one do I need for my use case? Did I make a wrong choice? Do I need to delete and start over?

Our new portal experience starts with your goal instead of requiring deep knowledge of feature flag architecture. The new experience asks you: What will you be using your feature flag for?

Scenarios That Match How You Think

When you select a scenario, the portal dynamically adjusts to reveal only the relevant configuration tabs and options. Switch shows straightforward toggle controls, Rollout presents percentage and targeting options, and Experiment allows variant allocation and traffic distribution settings. Let's dive deeper into how they fit into your needs.

Switch: "I need an on/off toggle"

Immediate on/off control over features, that is commonly used for:

Emergency kill switches: Instantly disable problematic features or AI models in production
Maintenance mode: Toggle site-wide maintenance without deployments
Feature gating: Block access to beta features for non-beta users
Debug modes: Enable verbose logging or diagnostic features for troubleshooting
Regional compliance: Quickly disable features that conflict with local regulations
Fallback mode: Switch from AI-powered responses to rule-based responses during model downtime

Rollout: "I want to control who gets this and when"

Gradual exposure with smart targeting for controlled feature releases that can be useful for:

Canary deployments: Start with 5% of users, gradually increase to 100%
Geographic rollouts: Launch multimodal AI in one region (e.g US-West), then expand based on language model performance
Subscription tiers: Give premium features to paid users first (e.g provide RAG-enhanced search to enterprise users)
Employee dogfooding: Internal testing before customer release, test new LLM-powered features with employees before customer release
Time-based releases: Automatically activate features during business hours
Load testing: Gradually increase traffic to stress-test new infrastructure

Experiment: "I want to make informed data-driven decisions"

Here's the key: You don't necessarily have to run statistical experiments. Beyond A/B testing, this scenario can be applied if you have multiple variants of something - different algorithms, UI layouts, configurations, or business logic paths - and is typically utilized for:

Statistical Analysis, A/B Testing & Experiments:

Conversion optimization: Test different checkout flows to measure completion rates
UI/UX experiments: Compare button colors, layouts, or copy to optimize user engagement
Model comparison: Compare Claude vs GPT-4 vs Gemini for completion rates and accuracy
Pricing strategy tests: Evaluate different pricing models with statistical significance
Algorithm performance: Compare recommendation engines using click-through rates and revenue metrics

Configuration & Variant Management:

Configuration management: Serve different API timeout values to different regions
Feature variants: Offer basic/premium/enterprise feature sets through one flag
Model routing: Route traffic between fine-tuned model, base model, and RAG-enhanced model based on query complexity
Content personalization: Show different onboarding flows based on user characteristics (e.g show different system instructions based on user expertise level for prompt personalization)
Multi-tenant customization: Serve tenant-specific configurations and behaviors

Telemetry can be enabled in all scenarios if your App Configuration store is connected to an App Insights Workspace. This update doesn't change how your existing flags work, the scenario-based approach simply improves new flag creation process on the Azure App Configuration Portal.

Experience the new approach today:

1. Navigate to your App Configuration resource in the Azure Portal
2. Go to Operations > Feature manager > Create
3. Selecting your scenario: Switch, Rollout, or Experiment and the subsequent setup steps will appear dynamically in tabs
4. Configure with purpose-built options that match your chosen scenario

This scenario-based approach is just the beginning. We're continuing to invest in making feature management more intuitive, starting with insights into better performing variants to AI experimentation.