Shared agentsAI Evals
The quality gate for AI agents, built into your Agent Development Lifecycle.
- AI Quality & Reliability
//Runtime Configuration
Release & experiment in productionat the speed of AI.
The runtime management platform. Feature flags, configs, and AI configs, all governed, all targeted, all measured, all delivered at scale.
Unified
Flags, configs and AI configs, together
Manage flags, configs, and AI configs without redeploys, under one governance model.
Governed
Every runtime change governed
RBAC, policies, approvals, and audit trails on every runtime change.
Measured
Every change measured
Experiment across flags, configs, and AI configs with one statistical engine.
Most teams outgrow their feature flag tool before they realize it.
Your flag tool isn't a configuration platform.
//Overview
What is runtime configuration management?
Runtime configuration management controls how software behaves in production without a redeploy, using feature flags, remote configuration and AI configs to release, target, experiment and roll back.
DataObserve Developer manages flags, configs and AI configs on one governed platform. Every runtime change has approvals and an audit trail, and is measured with warehouse-native experimentation and release monitoring.
//Everything in one platform
The only runtime management platform with governance built in
Feature flags
Progressive rollouts, precise targeting, and instant kill switches, without redeploying.
Configs
Runtime configuration, versioned, schema-validated, and governed.
AI configs
Swap prompts, models, and parameters at runtime, validated with AI experiments.
Experimentation
A/B tests with sequential testing and guardrail metrics built in.
Warehouse native
Run experiments in your data warehouse with trusted metrics. Data never leaves.
Release monitoring
Automated impact detection and rollback triggers within minutes of a release.
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//Use cases
Runtime Configuration: use cases
Progressive rollouts
Release features to a small audience first and widen as metrics hold.
Model and prompt swaps
Change AI models and prompts at runtime and validate the effect.
Experimentation
Run A/B tests measured against data in your own warehouse.
Kill switches
Turn off a misbehaving feature instantly, without a deploy.
//FAQs
Runtime Configuration: questions teams ask
What are feature flags?
They separate deploying code from releasing it: features ship hidden, roll out gradually, and turn off instantly.
What are AI configs?
Runtime configuration for AI behavior: swap models and prompts, then validate each change with experiments.
Which languages are supported?
SDKs for JavaScript, Flutter, Node.js, Python, Java, Go, .NET, Ruby, PHP, Elixir and more.
What is the difference between a feature flag and a config?
A feature flag turns behaviour on or off for chosen users. A config holds values, such as limits or model names, that change how a feature behaves.
What is warehouse-native experimentation?
Experiment results are calculated on the data in your own data warehouse, so metrics match the numbers the business already trusts.
