All Blog Posts - Page 2
Release management tools: What they are and how they work
Understanding the control layer between your CI/CD pipeline and your users.
Jesse Sumrak
Feature flags vs. feature branching: Why you need both for faster, safer releases
Learn where each fits into your delivery workflow.
Jesse Sumrak
MLOps lifecycle: Stages, workflow, and best practices
Understand the MLOps lifecycle from data preparation to monitoring.
Scarlett Attensil
AI pipeline: Preventing drift in production systems
Learn why uncontrolled AI pipeline changes can cause failures in prod.
Scarlett Attensil
Adaptive Triggers: AI that corrects itself in production
Adaptive Triggers is now available in closed beta.
Kelvin Yap
The next era of software needs runtime control
Edith Harbaugh
Introducing AgentControl
AgentControl is the operational layer for managing agents in production.
Kelvin Yap
LLM observability: Tutorial and best practices
LLM observability analyzes how models behave across development, testing, and production.
Scarlett Attensil
LLM pricing comparison: Tutorial and best practices
Large language models (LLMs) power a wide range of AI applications today.
Scarlett Attensil
Agent graphs bring control and visibility to multi-agent AI workflows
Agent graphs bring real-time control to multi-agent AI workflows.
Kelvin Yap
How to automate runtime control with kill switches, progressive rollouts, and user targeting
These strategies can help you design for control in production.
Megan Moore
A closer look at the 2026 Forrester Total Economic Impact™ of LaunchDarkly
Forrester finds LaunchDarkly delivers a 379% ROI through faster releases and lower risk.
LaunchDarkly
Orchestrate and safeguard AI agents with AI Configs
LaunchDarkly AI Configs helps you control AI agents at runtime.
LaunchDarkly
Online evals in AI Configs is now GA
Online evals in AI Configs help you define and monitor quality in production.
Kelvin Yap
Protect yourself from vibe coding errors
Is AI-written code 100% trustworthy? Get control over vibe coding errors.
LaunchDarkly
Where homegrown feature flag systems break
Homegrown feature flag systems work at the start, but runtime demands expose hidden risks.
LaunchDarkly
Why metrics for top engineering teams look different
Top engineering teams measure release health by looking at different metrics.
LaunchDarkly
Core capabilities of a feature management platform
Good feature management tools provide much more than flags.
LaunchDarkly
Beyond feature flags: LaunchDarkly vs. other release management tools
Not all release management platforms are built for production-grade control.
LaunchDarkly
Why "free" tools aren't free
DIY tools are great at the start...
LaunchDarkly
A false sense of security: Guardrails don’t prevent incidents
Tools alone aren't enough.
LaunchDarkly
Introducing sequential testing for LaunchDarkly Experimentation
Sequential testing lets you adapt quickly and check results as you go.
Cameron Savage
AI-generated code ships fast, but runtime control hasn’t kept up
AI is speeding up code generation, but control in production is lagging behind.
Metric Data Sources: import multiple tables for warehouse-native experimentation
Bring your own warehouse tables and schemas to power experimentation
Eric Wang
