Ship AI you can trust, in production.
Control, observe, and roll back AI products in real time. No redepoys. No blind spots.
Ship with safeguards, recover without redeploys.
Change prompts, parameters, or models on the fly, without code changes.
Monitor how your AI performs in production.
Track metrics and costs, and roll back instantly if output quality degrades.
Test and learn faster, so you can ship better.
Run safe, production-grade experiments across models or prompts.
Ship with safeguards.
Update prompts or models in real time, with instant rollback and redirects.
Ship with safeguards.
Update prompts or models in real time, with instant rollback and redirects.
Adapt models and prompts in real time.
Switch models or tweak prompts live in production, no code changes required.
Roll back in real time
Leverage a kill switch to disable AI configs if performance degrades.
Redirect to safer or cheaper models
Instantly switch traffic to an alternative model when costs spike or quality drops.
Monitor performance in production.
Track performance, trace workflows, and catch drift across every model, prompt, and agent.
Monitor performance in production.
Track performance, trace workflows, and catch drift across every model, prompt, and agent.
Track metrics in one place
Visualize metrics like token usage, latency, and user satisfaction per-config.
Audit every workflow
Follow completions, retries, and prompt flows across single-model and multi-agent workflows.
Flag unusual patterns with alerts
Catch drift early with alerts on your own quality or cost thresholds and automatically roll back to a previous variation.
Test, learn, improve.
Experiment with prompts and models in production, measure real results, and scale only what works.
Test, learn, improve.
Experiment with prompts and models in production, measure real results, and scale only what works.
Visualize performance across environments and teams
Compare model behavior, performance, and cost to see what’s best before you scale.
Compare prompts and models
Measure prompt and model combinations using LLM-as-judge, human feedback, or both.
Test and compare variations
Run experiments across models, prompts, and agents to find what performs best.
Poka goes “flag-first” to transform its release processes and AI innovation
/ /
If prompts were only on the backend, only the backend people could modify them. But since they're a flag in LaunchDarkly, the product managers, front-end developers, or even the designers might have access to modifying them if they want to test something out.
Learn more about AI Configs
![]()
Blog
Introducing AI Experiments and AI Versioning
Test, optimize, and manage AI Configs to accelerate AI app development
![]()
Blog
DeepSeek vs Qwen: local model showdown in Python, featuring LaunchDarkly AI Configs
Compare locally running models in Python, using LaunchDarkly AI configs.
![]()
Blog
Prompt versioning & management guide for building AI features
Learn how prompt versioning can help you maintain consistency across your applications.
![]()
Blog
AI model deployment: Best practices for production environments
A developer's guide to deploying ML models using LaunchDarkly AI Configs.
![]()
Blog
DeepSeek vs Qwen: local model showdown featuring LaunchDarkly AI Configs
Compare DeepSeek-R1 and Alibaba’s Qwen AI models, using LaunchDarkly AI configs.
Optimize prompts, models and costs in real time.
