Eppo — Warehouse-native experimentation and feature flagging for every team. Best for Data teams that want a single source of truth for experiment analysis with governed metrics, Engineers building feature flags, safe rollouts, and kill switches at scale, Marketers running no-code experiments on websites, email, and SMS against revenue metrics. Contact Sales pricing.
Data teams that want a single source of truth for experiment analysis with governed metricsEngineers building feature flags, safe rollouts, and kill switches at scaleMarketers running no-code experiments on websites, email, and SMS against revenue metricsProduct Managers who need self-service slice-dice analysis without data team bottlenecksOrganizations building AI products that need model evaluation and contextual bandits
Not ideal for
Teams without a cloud data warehouse (Snowflake, BigQuery, etc.) – Eppo is warehouse-nativeSimple, plug-and-play A/B testing without statistical rigor – Eppo assumes data sophisticationSmall teams wanting a low-cost free tier – pricing is enterprise-orientedStatic content experiments with no need for metric governance or version controlTeams exclusively using non-warehouse tools like Optimizely and unwilling to change workflow
Eppo is a top-tier choice for data-driven teams needing rigorous, warehouse-native experimentation. Its acquisition by Datadog adds monitoring muscle, but may raise migration concerns.
Eppo stands out for its warehouse-native architecture and statistical rigor, which avoids data silos and ensures metric consistency. It's ideal for companies already invested in a data warehouse (e.g., Snowflake, BigQuery) and wanting self-service analytics for PMs and marketers. The recent acquisition by Datadog could mean tighter integration with observability tools, but users should watch for licensing or roadmap shifts. Compared to LaunchDarkly, Eppo offers deeper experimentation analytics and CUPED++, while feature flags are simpler but fast. It is not for teams needing simple, no-code A/B tests without a warehouse setup, as the platform assumes some data infrastructure.
Skip Eppo if Skip Eppo if you don't have a data warehouse or want fully transparent public pricing before a sales call.
Latest from Eppo
Updated 3 days ago
Across the latest 2 updates: 1 community discussion and 1 news mention.
How likely is Eppo to still be operational in 12 months? Based on 6 signals including funding, development activity, and platform risk.
funding runway
80
website health
90
github activity
50
category mortality
70
wrapper dependency
100
hyperscaler overlap
80
About Eppo
Eppo is a next-generation experimentation and feature flagging platform, now part of Datadog. It helps data teams, engineers, marketers, and product managers automate trustworthy A/B tests and safe rollouts directly on their data warehouse. With a zero-copy, warehouse-native architecture, Eppo ensures complete metric governance, cost-efficient pipelines, and no data egressing. Key features include the most advanced statistical engine with CUPED++ variance reduction, contextual bandits for AI personalization, and automated rollouts with feature flags that power billions of daily assignments. Unlike black-box tools, Eppo provides full transparency and rigorous statistical methods (sequential, fixed sample, Bayesian), enabling org-wide experimentation culture.
Researching Eppo? Skip the guesswork.
Tell us what you want to build — we'll match the AI tools that fit your goal, budget & existing stack.
Key Features
Automate experiment analysis with trusted statistical methods
Centralize and govern metric definitions and experiment protocols
Zero-copy, warehouse-native architecture for data trust
Fastest feature flags powering billions of daily assignments
Contextual Bandits for real-time AI personalization
CUPED++ variance reduction to shorten experiment runtime
No-code website and email campaign experiments
Geolift tests for advertising incrementality measurement
Experiment Forecasts to plan roadmaps and measure impact
AI Model Evaluation with business metrics
Sequential, fixed-sample, and Bayesian analysis frameworks
Real-world workflow fit
Concrete scenarios for the personas Eppo actually fits — and what changes day-one when you adopt it.
Data scientist
You want to run an A/B test on a new recommendation algorithm. You connect Eppo to your Snowflake warehouse, define the metric (revenue per user) in the semantic layer, and launch the experiment.
Outcome: CUPED++ reduces variance by 30%, and automated analysis flags a significant positive lift, all without moving data out of your warehouse.
Engineer
You're deploying a new feature and want a safe rollout. You create a feature flag in Eppo, set a gradual rollout to 10% of users, and monitor with automated kill switch conditions.
Outcome: If errors spike, Eppo automatically rolls back; otherwise, you ramp to 100% in hours.
Marketer
You want to test an email campaign. Using Eppo's no-code editor, you set up an A/B test for subject lines, tied to revenue metrics from your warehouse.
Outcome: You see which subject line drives higher conversion within a week, without engineering support.
Use Cases
Run A/B tests on revenue and retention metrics without copying data out of your warehouse
Deploy feature flags with automated rollouts and kill switches to reduce deployment risk
Pricing is not publicly listed and requires contacting sales, which can be a barrier for smaller teams. The platform relies on your existing data warehouse—if your warehouse is slow or expensive, experimental workloads may hurt performance or budget. Some advanced features like Geolift and Contextual Bandits may require additional setup or expertise. As a newly acquired Datadog product, future integration and pricing changes are possible.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Annual total
—
Contact sales for a quote
Effective monthly
—
—
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Eppo tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Contact for pricing
Custom
Ideal for
Mid-market to enterprise teams with a data warehouse that want custom onboarding and support.
What this tier adds
Starting tier with custom pricing; includes full platform access and dedicated support.
Hidden costs & gotchas
What the public pricing page doesn't put in bold. Captured from pricing-page footnotes, contract terms, and recurring complaints.
•Contact sales for pricing; no free tier or self-serve plans available.
•Your own warehouse compute costs may increase with experiment workloads.
Where the pricing makes sense
The company stage and team size where Eppo's pricing actually pencils out — and where peers do it cheaper.
Eppo is enterprise-focused with custom pricing after a sales demo. It fits companies already invested in Snowflake or BigQuery. For smaller teams, it’s likely more expensive than Optimizely or LaunchDarkly, which offer transparent self-serve tiers.
Setup time & first value
How long it actually takes to get something useful out of Eppo — broken out by persona, not the marketing-page minute.
For data scientists: connecting your warehouse and defining metrics takes a few hours. Engineers: integrating the SDK for feature flags can be done in a day. Marketers: no-code web experimentation can be set up in under an hour.
Switching to or from Eppo
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Migrating in
→From Optimizely: Export experiment configurations and historical data; then redeploy flags and experiments in Eppo.
→From LaunchDarkly: Use Eppo's migration guide to switch feature flags, with support from their team.
Migrating out
↗To Optimizely: Export experiment data from Eppo's warehouse-native tables; manually recreate configurations.
Recent material changes
Pricing, brand, ownership, or deprecation changes worth knowing before you commit. Most-recent first.
•2026-05-26: Eppo acquired by Datadog; now part of Datadog Experiments.
