Now processing 1B+ events daily · Free Preview available

The modern product
experimentation platform

Sequential testing, CUPED variance reduction, multi-armed bandits, and AI-powered experiment design — all completely free.

Java, Python, React & JS SDKs  ·  SOC 2 compliant  ·  Deploy to your AWS

Free Preview · All features included · Self-hosted on your AWS

1B+
Events daily
<50ms
P99 latency
99.99%
Uptime SLA
20–40%
Sample size reduction via CUPED
5
SDK languages (Python, JS, Java, React, Go)
SOC 2
SOC 2 / ISO 27001 ready

Everything you need to experiment

From feature flags to enterprise-grade statistical methods — all included

Core Platform

A/B Testing

Run multivariate experiments with statistical rigor. Built-in significance testing, confidence intervals, and automated SHIP / KEEP / CONTINUE recommendations.

Feature Flags

Deploy safely with gradual rollouts, staged schedules, and kill switches. Bulk toggle, SSE audit stream, and full change history included.

Real-time Analytics

See results as they happen. Trend charts, cumulative and daily views, sample size meters, and days-to-significance estimates.

Safety Monitoring

Automated guardrails monitor error rates and performance. Auto-rollback on anomaly detection protects your users from bad releases.

Advanced Targeting

20+ targeting operators including semantic versioning, geo-distance, time windows, and JSON path. Rules engine evaluates 125K+ ops/sec.

Developer First

SDKs for Python, JavaScript, Java, and React. RESTful APIs with OpenAPI docs. RBAC with custom roles — ADMIN, DEVELOPER, ANALYST, VIEWER.

Advanced Statistical Methods

Sequential Testing

mSPRT-based early stopping with always-valid confidence intervals. Stop experiments early when you have evidence, without inflating false positive rates. Alpha spending via O'Brien-Fleming and Pocock boundaries.

CUPED Variance Reduction

Reach significance 20–40% faster using pre-experiment covariate adjustment (OLS θ). Winsorization handles outliers. Works for both conversion and numeric metrics.

Multi-Armed Bandit

Maximize conversions during experiments. Choose Thompson Sampling, UCB1, or Epsilon-Greedy. Background scheduler auto-reallocates traffic to winning variants.

Interaction Detection

Detect cross-experiment interference before it corrupts your results. Jaccard overlap analysis, chi-squared interaction tests, novelty effect detection, and SUTVA violation alerts.

AI Experiment Design

Claude API–powered hypothesis generation and design suggestions. MCP Server integrates with Claude Code, Cursor, and other coding assistants so your AI tools can design and interpret experiments.

Warehouse-Native Analytics

Query experiment results directly in Snowflake, BigQuery, or Redshift. SQL-sanitized sync keeps your warehouse as the source of truth.

Traffic Management & Governance

Mutual Exclusion Groups

Prevent experiment contamination using consistent-hashing traffic partitioning. Global holdout groups measure the cumulative impact of your entire experimentation program.

Dimensional Analysis

Break down results by device, country, plan, or any user attribute. Bonferroni-corrected per-segment analysis with heterogeneous treatment effect (HTE) detection.

Audit Logging & Compliance

HMAC-SHA256 signed tamper-proof audit trail with real-time SSE stream. SOC 2 Type II & ISO 27001 compliance reports with CSV/JSON export. Custom RBAC roles and effective permissions resolution.

SDK Ecosystem & Integrations

Java SDK

Native Java integration with Spring Boot auto-configuration starter. OkHttp client, LRU+TTL caching, and MD5-based consistent hash bucketing for stable variant assignment.

React SDK

React hooks and HOC — useFeatureFlag, useExperiment, useMultipleFlags, and withExperimentation HOC. SSR support via ServerClient with no hydration mismatch.

Third-party Integrations

Jira, Salesforce, and GitHub webhooks — sync experiment status, automatically create issues, and push results to your existing toolchain without leaving the platform.

Full Bayesian Statistics

Beta-Binomial posteriors, Monte Carlo PtBB simulations, Bayes Factor (BF10) via Savage-Dickey, credible intervals, and an automatic Bayesian stopping rule — alongside your frequentist analysis.

Split URL Testing

Server-side URL splitting via Lambda@Edge — no client-side JavaScript, works with any frontend framework. Persistent cookie-based assignment ensures a consistent experience across visits.

No-Code Experiment Builder

5-step guided wizard for experiment creation — define hypotheses, set variants, choose metrics, configure targeting, and launch — all without writing a line of code.

Works with your stack

Seamlessly integrate with the tools you already use

AWS
Cloud Infrastructure
Snowflake
Data Warehouse
BigQuery
Data Warehouse
Redshift
Data Warehouse
Segment
CDP Integration
Slack
Notifications
SendGrid
Email Alerts
Claude
AI Design via MCP
Jira
Issue Sync
Salesforce
CRM Integration
GitHub
Webhook & Issues

Simple to integrate

Get started in minutes — SDKs for Python, JavaScript, Java, and React

JavaScriptPythonJavaReact
JavaScript
import { Experimently } from '@experimently/sdk';

const client = new Experimently('YOUR_API_KEY');

// Get variant + track conversion
const variant = await client.getVariant(
  'checkout-redesign', userId
);

client.track('purchase_completed', userId, {
  revenue: 99.99
});

// Feature flag with targeting
const isEnabled = await client.isEnabled(
  'new-checkout', userId,
  { country: 'US', plan: 'enterprise' }
);
Java (Spring Boot)
// application.properties
experimently.api-key=YOUR_API_KEY
experimently.cache.ttl-seconds=60

// Auto-configured via Spring starter
@Autowired
ExperimentlyClient client;

String variant = client.getVariant(
    "checkout-redesign", userId
);

boolean enabled = client.isEnabled(
    "new-checkout", userId,
    Map.of("country", "US", "plan", "enterprise")
);
React
import {
  ExperimentlyProvider,
  useFeatureFlag,
  useVariant,
} from '@experimently/react';

// Wrap your app
<ExperimentlyProvider apiKey="YOUR_API_KEY">
  <App />
</ExperimentlyProvider>

// Use in any component
function Checkout() {
  const variant = useVariant('checkout-redesign');
  const isNewUi = useFeatureFlag('new-checkout');

  return isNewUi ? <NewFlow /> : <OldFlow />;
}
Python
from experimently import ExperimentlyClient

client = ExperimentlyClient(api_key="YOUR_API_KEY")

# Get experiment variant
variant = client.get_variant(
    "checkout-redesign", user_id
)

# Track conversion
client.track("purchase_completed", user_id,
    properties={"revenue": 99.99})

# Feature flag with targeting
enabled = client.is_enabled(
    "new-checkout", user_id,
    context={"country": "US", "plan": "enterprise"}
)

How we compare

Enterprise-grade statistical methods — self-hosted, no price tag

FeatureExperimentlyOptimizelyStatsig*AmplitudeLaunchDarkly
A/B / Multivariate Testing⚠️ Add-on
Feature Flags
Sequential Testing (mSPRT)
CUPED Variance Reduction✅ (binary + numeric)⚠️ Numeric only⚠️ Add-on
Multi-Armed Bandit✅ (3 algorithms)
Full Bayesian (BF10 + Monte Carlo)⚠️ Partial
Split URL Testing (Lambda@Edge)
Interaction Detection⚠️ Partial
Mutual Exclusion Groups⚠️ Partial⚠️ Partial
Global Holdout Group⚠️
Dimensional Analysis✅ + Bonferroni⚠️ Partial⚠️ Partial
No-Code Experiment Wizard⚠️✅ (Web)
AI Experiment Design✅ (Claude + MCP)✅ (Opal AI)
Warehouse Analytics✅ (3 warehouses)✅ (Snowflake)
Jira / Salesforce / GitHub Integration⚠️ Partial⚠️ Partial
Java SDK✅ (Spring Boot starter)⚠️
React SDK (hooks + SSR)⚠️
SOC 2 / ISO 27001 Compliance Export✅ (HMAC-signed)⚠️ Enterprise⚠️ Enterprise
Audit Log + SSE Stream⚠️⚠️
Deploy in Your AWS Account
Pricing (entry)🎉 Free Preview$36K+/yearFree → $150+/moFree → CustomFree → $20K+/year

* Statsig acquired by OpenAI for $1.1B in September 2025. Future roadmap subject to change. ⚠️ = Partial or enterprise-only feature.

Free Preview

Every feature is available — self-host on your own AWS account at no cost.

$0/month

Self-hosted on your AWS account — you own your data

Unlimited A/B experiments
Feature flags with staged rollouts
Sequential testing (mSPRT)
CUPED variance reduction
Multi-armed bandit (3 algorithms)
Full Bayesian (BF10, Monte Carlo)
Split URL testing via Lambda@Edge
Interaction detection
AI experiment design (Claude API)
Warehouse analytics (Snowflake, BigQuery)
Java SDK + Spring Boot starter
React SDK (hooks, HOC, SSR)
Jira, Salesforce & GitHub integrations
SOC 2 / ISO 27001 compliance reports
Full audit log + custom RBAC roles
Slack + email alerting
Get started — deploy to your AWS

Start experimenting today

Sequential testing, CUPED, Bayesian stats, Java/React/JS SDKs, and AI design — all completely free. Deploy to your own AWS.

Free Preview · Self-hosted · All features included