Both Confidence and Eppo run analysis in your warehouse. Both ship CUPED, sequential testing, sample ratio mismatch checks, and guardrail metrics as defaults. The choice between them comes down to two things a feature checklist cannot show: how much operating history sits behind the defaults, and whether your team wants metric definitions in YAML or in a UI.
Confidence is the experimentation platform Spotify has run for 15 years and still depends on. Eppo is a commercial warehouse-native experimentation platform founded in 2020, built around metric definitions managed in code. The differences live in operating- history scale evidence, methodology specifics, product scope, and metric workflow.
What is Confidence?
Confidence is an experimentation platform with integrated feature flags and analysis, built at Spotify over 15 years and now available externally. It runs analysis inside your warehouse (BigQuery, Snowflake, Redshift, or Databricks) and never stores your raw user-level data. Today, 300+ Spotify teams use Confidence to run 10,000+ experiments per year across 750 million users in 186 markets. 42% of those experiments are rolled back after guardrail metrics flag a regression. The platform is built to surface regressions before they ship to 750M users, where shipping a regression is more expensive than missing an improvement.
The product is opinionated. Confidence does not offer Bayesian inference, multi-armed bandits, or switchback experiments. We say no to features that, in 15 years of running experiments at scale, increased complexity without improving the quality of decisions teams made. Simplicity at scale is the design position.
What is Eppo?
Eppo is a warehouse-native experimentation platform founded in 2020 by Che Sharma. It pioneered the warehouse-native commercial architecture and is built around metric definitions managed in code, typically in YAML files version-controlled alongside the rest of your data infrastructure. Its product scope is focused: experiment analysis with a feature-flagging layer, rather than a bundled analytics suite.
Eppo's typical customer is a data-science-led organization that already has a data warehouse, dedicated metric definitions, and a culture of treating experimentation as a discipline. Eppo and Confidence overlap heavily on buyer profile and methodology posture. Eppo has a published methodology bench and a customer base that pushes the platform on rigor.
Confidence vs Eppo, head to head
Both products are warehouse-native. Both implement CUPED variance reduction (CUPED uses pre-experiment data to tighten the confidence interval around an experiment's effect) and sequential testing (peeking-safe statistical methods that let you stop experiments early without inflating false-positive rates). Both ship sample ratio mismatch checks, guardrail metrics, and trigger analysis (analyzing only the users actually exposed to the variant rather than all assigned users) as defaults. The differences are narrow but consequential.
The operating-history claim is asymmetric. Confidence is the platform Spotify has run for 15 years. 10,000+ experiments per year, 300+ teams, 750M users in 186 markets, every year for over a decade. Eppo has five years of commercial operation with a strong customer base in data-science-led organizations including DraftKings.
Confidence's CUPED uses the Negi–Wooldridge 2021 full regression estimator, named in our documentation, which produces tighter confidence intervals than the original CUPED formulation. Eppo's public documentation describes CUPED support; practitioners evaluating on this dimension should ask Eppo directly which estimator their implementation uses.
Eppo wins on metric workflow. Eppo's metric-as-code pattern in YAML, version-controlled alongside dbt models, is more mature than Confidence's equivalent. Pull requests for metric changes; review history; metric definitions reviewed in the same code review as your data infrastructure. If your team version-controls everything else, this is a real workflow difference.
Confidence wins on multi-team coordination. Surfaces are a multi-team coordination primitive that prevents teams from stepping on each other's experiments at scale, with shared required metrics enforced across a product area. Eppo's coordination model relies on workspace-level conventions and Slack notifications rather than a dedicated multi-team primitive. For organizations where Slack is already the coordination surface, that is a fair trade.
Product scope is similar. Both focus on experimentation analysis and feature flags rather than bundled product analytics or session replay. Confidence uses OpenFeature for SDK integration, with iOS and Android OpenFeature provider SDKs donated to the CNCF (the Cloud Native Computing Foundation). Your flag-evaluation code is not Confidence-specific; if you change platforms, you do not rewrite the integration. Eppo's SDKs are Eppo-specific.
| Feature | Confidence | Eppo |
|---|---|---|
| A/B testing | Built-in, Spotify-grade defaults | Built-in, methodology-forward |
| Feature flags | First-class, in-process eval after config refresh | Assignment SDKs |
| Warehouse-native | Primary architecture; raw data never stored | Primary architecture; raw data never stored |
| CUPED variance reduction | Negi–Wooldridge 2021 full regression | Supported (estimator not published) |
| Sequential testing | Group Sequential Tests, always-valid inference | Supported |
| Sample ratio mismatch / guardrails | Default | Default |
| Metric-as-code | Supported | YAML-in-git; mature workflow |
| Multi-team coordination | Surfaces with shared required metrics enforced | Workspace conventions plus Slack notifications |
| Open SDK standard | OpenFeature, donated to CNCF | Eppo SDKs |
| Operating history | Spotify, 15 years; 10,000+ experiments/yr | Founded 2020, commercial since launch |
| Scale evidence | 750M users at Spotify, 42% rollback at guardrails | Data-science-led customer references |
Integrations comparison
Confidence integrates deeply with the warehouse layer (BigQuery, Snowflake, Redshift, Databricks) and uses OpenFeature for SDK integration. The OpenFeature donation to the CNCF means your flag-evaluation code is portable across any OpenFeature provider; if you ever change platforms, you do not rewrite your codebase.
Eppo integrates with the same major warehouses and provides assignment SDKs across major server and client languages. Eppo's Slack-first notification surfaces for experiment lifecycle events are a real ergonomic strength for teams already coordinating in Slack.
Pricing comparison
Both products price as serious experimentation tools rather than free-tier-led products. Eppo's pricing is gated; Confidence's pricing is published on request. Both target organizations that have decided experimentation matters enough to invest in. A trial of Confidence is available at confidence.spotify.com without going through procurement.
What separates Confidence from Eppo
Five differences shape the choice between the two products.
The first is operating history. Confidence has 15 years of continuous use at Spotify, with 10,000+ experiments per year sustained for over a decade. Eppo has five years of commercial operation and a strong methodology bench. Both are real; the scale of operating evidence is asymmetric.
The second is statistical method coverage. Confidence's CUPED uses Negi–Wooldridge 2021 full regression. Both products ship sample ratio mismatch checks and guardrail metrics as defaults. Confidence is opinionated against Bayesian inference and multi-armed bandits; Eppo offers a broader range of methodological options.
The third is metric workflow. Eppo's metric-as-code pattern is more mature than Confidence's equivalent. If metric definitions reviewed in code rather than a UI is a non-negotiable workflow requirement, Eppo is the stronger fit.
The fourth is the SDK posture. Confidence's flag-evaluation code is portable across any OpenFeature provider. Eppo's SDKs are Eppo-specific.
The fifth is multi-team coordination. Confidence's Surfaces enforce shared required metrics across a product area. Eppo's coordination surface is lighter (Slack notifications and workspace conventions rather than a dedicated primitive).
Both products are warehouse-native, methodology-forward, and aimed at teams that treat experimentation as a discipline. Operating history and SDK portability favor Confidence; code-defined metric workflows and methodological optionality favor Eppo. Pick the wrong axis and you spend the next several years running an experimentation program that never quite fits how your team works.
See also: Top 7 alternatives to Eppo · What is Eppo?