Yes. LaunchDarkly provides CUPED, Bayesian and frequentist analysis, frequentist sequential testing, multiple comparison correction, sample ratio mismatch detection and stratified sampling. LaunchDarkly also provides holdouts, multi-armed bandits, and warehouse-native experimentation.
LaunchDarkly vs. Statsig
Production control and experimentation rigor, on one platform.
Your feature releases, experiments, and AI agents run in production. The platform controlling them should be built like production infrastructure (e.g. streaming delivery in under 200 milliseconds, regression detection roughly every minute, and runtime control over agent behavior). That’s what LaunchDarkly is designed for.
Statsig, now part of Amplitude, is consolidating around an analytics-centered operating model. The question isn’t which platform has more features, it’s what you want at the center of how you ship.
Two numbers that define the gap between analytics and production infrastructure.
<200ms vs. 10 sec
LaunchDarkly streams urgent flag changes in under 200 milliseconds. Statsig server SDKs poll every 10 seconds by default.
~1min vs. 24hrs
Guarded Rollouts re-evaluate regression metrics roughly every minute. Statsig Cloud evaluates Rollout Alerts every 24 hours.
The Amplitude partnership
Why teams are
re‑evaluating now.
In May 2026, Amplitude announced that it would take on Statsig’s brand and customers, maintain and develop the existing Statsig platform across cloud and data warehouse deployments, and build a more integrated roadmap across Amplitude and Statsig.
The acquisition doesn’t change Statsig overnight, but it does answer the question of where the platform is headed: deeper into Amplitude’s analytics roadmap. If your team relies on Statsig for feature management, experimentation, or production rollouts, this is the right moment to confirm long-term platform fit.
Choose LaunchDarkly if you prioritize…LaunchDarkly
Statsig may fit if you prioritize…Statsig
Runtime control for code and agent workflows
AI prompt and model evaluation workflows, where currently available capabilities meet requirementsFeature management and experimentation governed as production infrastructureFeature management and experimentation managed inside a product analytics operating modelStreaming-first delivery of urgent feature, experiment, and rollback changesAnalytics and experimentation consolidation across Statsig and AmplitudeFrequent regression evaluation during Guarded Rollouts, backed by warehouse-native experimentation against source-of-truth metricsFrequent regression evaluation during Guarded Rollouts, backed by warehouse-native experimentation against source-of-truth metrics#1 in Feature Management for 3 years running.
Trusted by product teams. Proven in production.
See why customers love us
What LaunchDarkly supports today.
Flag update delivery in less than 200 milliseconds and streaming connections by default for server-side SDKs.
Flag update delivery in less than 200 milliseconds and streaming connections by default for server-side SDKs.
Compare technical capabilities.
Server-side SDKs maintain streaming connections by default and receive updated flag values when a flag changes. Flag update delivery in less than 200 milliseconds.
Statsig documents that server SDKs poll for updates every 10 seconds by default, configurable. Some SDKs support gRPC streaming through Statsig Forward Proxy.
What buyers should validate:
What propagation time is acceptable for an emergency change?
During Guarded rollouts, LaunchDarkly recalculates metric results roughly every minute using sequential testing and can automatically rollback when a regression is detected.
Statsig documents Rollout Alerts evaluated every 24 hours in Statsig Cloud, or whenever Metric Results load in Warehouse Native. Safeguards can act when configured alerts fire.
What buyers should validate:
What is your acceptable exposure window for a statistically detectable rollout regression?
CUPED, Bayesian analysis, frequentist and sequential testing, multiple comparison correction, SRM detection, stratified sampling, holdouts, multi-armed bandits, and Warehouse Native experimentation.
Statsig documents advanced experimentation capabilities including CUPED, sequential testing, Bayesian analysis, holdouts, and Warehouse Native experimentation
What buyers should validate:
Once required methods are validated in both platforms, which production control foundation should engineering depend on?
Native experimentation integrations for BigQuery, Databricks, Redshift and Snowflake.
Statsig documents Warehouse Native experimentation.
What buyers should validate:
Which warehouse, metric pipeline, latency, compute cost and governance model meets your needs?
AgentControl offers completion mode and agent-based configurations, including prompts, model settings, structured multi-step workflows, tools, judges, variations, targeting, approvals, monitoring, experimentation, and Guarded Rollouts.
Statsig documents Prompts & Graders and AI Evals for prompt and model evaluation and rollout. AI Evals are currently documented as beta and unavailable to new beta customers.
What buyers should validate:
Do you need prompt testing only, or production control for agent workflows, tools, approvals, monitoring, and rollback?
When an incident is already understood, teams need a kill switch to reach applications quickly. LaunchDarkly server-side SDKs maintain streaming connections by default and receive updated flag values when a flag changes. Flag update delivery is achieved in less than 200 milliseconds.
Statsig's server SDKs poll every 10 seconds by default (configurable), with gRPC streaming available on some SDKs via Forward Proxy.
What to validate:
Is default polling fast enough for production critical changes, or would you need to deploy and operate streaming?
A harmful release is not always obvious to an operator. Teams may need the platform to identify degradation based on metrics and automatically limit exposure.
LaunchDarkly Guarded rollouts monitor (recalculating roughly every minute) selected metrics throughout the rollout. If sequential testing identifies a statistically significant negative impact and automatic rollback is enabled, LaunchDarkly rolls the release back.
Statsig documents Rollout Alerts that measure metric delta in the context of an experiment or feature gate. In Statsig Cloud, those Rollout Alerts are evaluated every 24 hours. In Statsig Warehouse Native, they are evaluated whenever Metric Results are loaded. Statsig Safeguards can take automated actions, including rollback, when configured alerts fire.
Statsig also documents Topline Alerts for absolute metric thresholds or changes. These are distinct from rollout attributed treatment and are currently documented as limited beta.
What to validate:
How quickly must a bad rollout be evaluated and rolled back before customer impact becomes unacceptable?
Experimentation
Modern experimentation, built into your release infrastructure.
LaunchDarkly experimentation is built on the same statistical foundations advanced teams expect (CUPED variance reduction, sequential testing, Bayesian and frequentist analysis, SRM detection, and warehouse-native metrics) and it runs on the same platform that governs your production releases. Once both platforms clear your statistical bar, experimentation stops being the deciding factor. Production control becomes the decision.
Experimentation requirement
LaunchDarkly capability
Reduce variance and detect differences more precisely
CUPED covariate adjustmentChoose an appropriate statistical approachBayesian analysis and frequentist analysisMonitor results over time without relying only on fixed-horizon testsFrequentist sequential testingReduce false positives across multiple metrics or treatmentsMultiple comparison correctionDetect potentially invalid traffic allocationSample ratio mismatch detectionReduce known covariate imbalanceStratified samplingDynamically direct traffic toward better performing variationsAnalyze experiments using warehouse-backed metric dataWarehouse-native integrations for BigQuery, Databricks, Redshift and SnowflakeThe real decision
If Statsig and LaunchDarkly both satisfy your required experimentation methods, experimentation should not be evaluated in isolation. The better question to then ask is, which platform do you want engineering to rely on for production releases, experiments, and AI behavior control?
AgentControl
Control AI behavior with the same runtime discipline as software releases.
Prompt evaluation helps teams understand how an agent performs before release. Production control governs how prompts, models, and agent workflows behave once they are serving customers.
Because these components can change independently of application code, teams need the ability to target changes, monitor live performance, and act quickly when quality, latency, cost, safety, or customer experience begins to degrade.
LaunchDarkly
Statsig
Prompt and model configuration outside application code
Structured, multi-step agent workflows.
Tools and skills attached to agent variations.
Targeting, approvals, performance monitoring, and Guarded rollouts for agent-based configurations.
Judge-based evaluation workflows where supported, including programmatic evaluation of agent variations.
Statsig documents Prompts & Graders and AI Evals for prompt and model evaluation and rollout. Statsig currently documents AI Evals as beta and unavailable to new beta customers.
What to validate:
Can the platform govern live agent behavior and automatically limit exposure when production performance degrades?
Migrate from Statsig to LaunchDarkly with our new free, automated migration tool.
Seamless migration
Our automated flag import tool makes switching faster and easier than ever.
Dedicated support
A LaunchDarkly specialist will partner with you through your migration and rollout.
No additional cost
Eligible customers get access to LaunchDarkly Experimentation for free until your Statsig renewal date, for up to 1 year. Offer expires on January 31, 2027, subject to availability.
