Feature Flags & Progressive Releases

Launch features safely.
Control releases in real time.

Manage feature rollouts with deterministic percentage bucketing, targeting rules, and master kill-switches—powered by local in-memory evaluation.

Launch Developer Console

Interactive Feature Switchboard

Target Flag: ai-smart-search

Test User Identity:

Rollout Percentage:

0% (Internal) 50% (Half) 100% (All Users)

Computed Evaluation Latency: < 0.08ms

Deterministic Percentage Rollouts • Attribute Targeting Rules • Master Kill-Switch Protection • Multi-Environment Support • Immutable Audit Logging •

02 // Evaluation Architecture

In-Memory Evaluation vs. Remote Network Calls

How in-process evaluation avoids adding network latency to critical application paths.

Remote HTTP Evaluation

Network Dependent

1. Application sends HTTP request to external service Network hop

2. Remote server executes rule matching logic Server compute

3. Response payload returned over the internet Payload return

Trade-off: External network latency is added to every evaluated request.

Local In-Memory Evaluation

In-Process

1. Flag configurations cached locally in application memory

Local RAM

2. Deterministic FNV-1a hash and rule evaluation in-process

Sub-millisecond

3. Immediate boolean/variant decision returned

Zero I/O

Advantage: No network dependency on the request critical path.

03 // Audience Consistency

Deterministic Sticky Bucketing for Every User.

Users never jump between variants. A user assigned to bucket #42% remains in bucket #42% across all visits, sessions, and platforms.

FNV-1a 64-Bit Hash Simulation

Evaluating character sequence deterministic distribution

100% Deterministic

Identity:

Resolved Cohort:

04 // Canary Releases

Control Blast Radius with Gradual Rollouts.

Safely advance from internal staff verification to canary cohorts, 50/50 A/B testing, and full global release.

STAGE 1: TEAM 0% Public

Staff Whitelist

Internal developers and QA verify live in production.

STAGE 2: CANARY 10% Traffic

Early Beta Slice

Monitor telemetry, error rates, and server memory.

STAGE 3: 50/50 50% Split

A/B Experiment

Compare conversion metrics and engagement delta.

STAGE 4: GLOBAL 100% General

Worldwide Release

Active across all edge nodes with zero downtime.

05 // Instant Rollback

Emergency Kill Switch Circuit Breaker.

Discovered a breaking defect? Trigger the master kill-switch to immediately fall back to the safe control state across all edge nodes in milliseconds.

Kill-Switch Circuit Breaker Simulation

Test instant edge emergency disengagement

Circuit State:

06 // Developer Integration

Single-Line Integration in Any Stack.

Drop native in-memory evaluation clients into Go, React, Node.js, Python, or edge worker environments.

quickstart-example

07 // Architecture & Reliability

Engineered for Massive Scale and Reliability.

Compare architectural capabilities between Flagura and traditional feature management stacks.

Evaluation Method	Resolution Path	Network I/O	Offline Resilient
Local In-Memory (Flagura)	In-Process Memory	None	Yes
Remote API Evaluation	HTTP Roundtrip	1 Request per Eval	No
Direct Database Queries	Database Connection	1 Query per Eval	No

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Launch features safely. Control releases in real time.