Eppo (Datadog Experiments)
Warehouse-native experimentation platform known for rigorous statistics (CUPED, sequential testing), now operating as Datadog Experiments.
Acquired by Datadog in 2025; the statistical benchmark for warehouse-native testing.
Ownership
- Formerly Eppo by Datadog · renamed Apr 2026 · source · In the Martech Market Report
Under Datadog's ownership, the Eppo product was folded into Datadog Experiments, which Datadog announced as generally available on 2 April 2026, and the Eppo website now says Eppo is Datadog Experiments.
- Acquired by Datadog · closed May 2025 · source · In the Martech Market Report
Datadog bought the experimentation and feature-flagging platform Eppo. Both companies said on 5–6 May 2025 that the deal was already done, and Eppo kept running under the brand 'Eppo by Datadog'.
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Eppo (Datadog Experiments) Pros & Cons
Key strengths and limitations to consider, drafted by an AI model from public sources
Strengths
- Queries customer warehouse data instead of copying raw event data into Eppo
- Can analyze assignments from Eppo flags or external randomization systems
- Supports Snowflake, BigQuery, Databricks, and Redshift
- Provides sequential, fixed-sample, and Bayesian analysis methods
- Datadog integration links flag evaluations to RUM performance data
- SDKs support web, mobile, server-side, and cross-platform applications
Limitations
- Requires usable warehouse event and assignment data before analysis can begin
- Warehouse-native analysis can add query-cost and data-latency dependencies
- Not a visual no-code web testing suite for marketers
- Pricing is not publicly posted
- Product direction is evolving as Eppo capabilities move into Datadog
- Feature flag operations are less established than LaunchDarkly's ecosystem
Ideal For
Who benefits most from Eppo (Datadog Experiments), drafted by an AI model from public sources
Quick Analysis
Eppo by Datadog competes in product experimentation, feature management, and warehouse-native experiment analytics. Its core model separates assignment from measurement: teams can use Eppo/Datadog flags or bring an existing randomization system, while results are computed against governed metrics in Snowflake, BigQuery, Databricks, or Redshift. Since Datadog acquired Eppo in May 2025, the product is increasingly positioned as Datadog Experiments, linking experimentation with Product Analytics, RUM, Session Replay, and observability data.
Its strongest fit is for data-mature product organizations that want rigorous causal measurement on business metrics already modeled in a warehouse, rather than a web-optimization tool with a separate event store. Eppo differentiates from Optimizely, Statsig, LaunchDarkly, and Split by emphasizing warehouse-native analysis, bring-your-own assignment data, advanced statistics such as sequential testing and CUPED-style variance reduction, and experiment diagnostics. Datadog is also a differentiator for engineering-led teams that need to connect release health and user-performance data to experiment outcomes.
Buyers should evaluate Eppo when experiment credibility, metric governance, and analysis flexibility matter more than turnkey visual web testing. Consider Statsig for a more vertically integrated product analytics and experimentation stack, LaunchDarkly for feature-management-led deployments, Optimizely for broad digital experience optimization, and Amplitude Experiment for teams standardized on Amplitude analytics. Validate warehouse query cost and latency, SDK coverage, assignment-event logging, metric-model governance, and the maturity of Datadog product integration before committing.
A SaaS product team testing a new onboarding flow against activation and retention metrics in Snowflake
An engineering team canary-releasing a checkout service change and correlating treatment exposure with RUM performance
A marketplace comparing ranking-model variants against conversion, margin, and repeat-purchase metrics in BigQuery
A mobile team using feature flags to test subscription paywall variants across iOS and Android
An AI product team allocating traffic across LLM prompts or models and measuring quality, cost, and user engagement
A data team analyzing historical experiments randomized through LaunchDarkly, Optimizely, or an internal assignment system
Capabilities
Core Capabilities
Pricing
Model
usage based
Starting Price
$450 per launched experiment per month (billed annually; $575 on-demand)
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