# Top 7 alternatives to Split (Harness FME)

Last updated: 2026-05-04
Canonical source: https://confidence.spotify.com/comparisons/alternatives-to-split
Owner: Spotify AB

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Top 7 Split (Harness FME) alternatives for 2026: why teams leave after the Harness acquisition, plus methodology depth and pricing across the options.

Three reasons drive the search for a Split alternative.

The first is the Harness acquisition. Split was acquired by Harness
in May 2024 (deal closed June 11, 2024) and rebranded as Harness
Feature Management & Experimentation (FME). The product is now
sold inside the broader Harness platform alongside Continuous
Delivery, Continuous Integration, Cloud Cost Management, and
AI-powered code agents. Buyers who chose Split for its
experimentation-first focus, or who do not want their experimentation
roadmap set inside a CI/CD platform, are shopping alternatives.

The second is methodology depth. Harness FME ships frequentist
hypothesis testing, mSPRT sequential testing, sample ratio
mismatch detection (chi-squared p<0.001 threshold), guardrail
metrics, and Multiple Comparison Correction. CUPED variance
reduction (which uses pre-experiment data to tighten confidence
intervals) is not in the public stats documentation as of 2026.
Teams that need CUPED specifically look elsewhere.

The third is platform fit. Harness FME is the natural fit for
teams already on the Harness CI/CD platform. Teams not on
Harness who do not want experimentation embedded inside a software-
delivery suite they do not otherwise use look at experimentation-
first alternatives.

The seven alternatives below are the ones we see most often in
evaluations against Split (Harness FME), starting with our own
platform, Confidence by Spotify.

***

## 1. Confidence by Spotify

### Overview

Confidence is an experimentation platform with integrated feature
flags and analysis, built at Spotify over 15 years and now
available to teams outside Spotify. It runs analysis inside your
data 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
tuned for high-recall regression detection.

Confidence is opinionated. The product team has said no to
Bayesian inference, multi-armed bandits, and switchback experiments
on the grounds that, in 15 years of running experiments at scale,
those features increased complexity without improving the quality
of decisions teams made.

### Key features

* Warehouse-native by default. Analysis runs inside BigQuery,
  Snowflake, Redshift, or Databricks; raw user data never leaves
  the warehouse.
* CUPED variance reduction using the Negi–Wooldridge full
  regression estimator.
* Group Sequential Tests with always-valid inference for safe
  peeking.
* Sample ratio mismatch checks, guardrail metrics, and trigger
  analysis as defaults.
* Feature flags with structured configurations (typed schemas).
  In-process evaluation with no network call at evaluation time.
* OpenFeature SDKs across every supported language; iOS and
  Android OpenFeature provider SDKs donated to the CNCF; Spotify
  on the OpenFeature governance committee.
* **Surfaces**, the 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.

### Pros vs Split

* **Experimentation-first vendor.** Confidence's roadmap is set
  by the team that runs Spotify's experimentation platform.
  Harness FME's roadmap is set inside Harness's broader CI/CD
  platform priorities.
* **CUPED variance reduction.** Confidence ships CUPED with the
  Negi–Wooldridge full regression estimator named in
  documentation. Harness FME does not list CUPED in its public
  stats documentation as of 2026.
* **Operating-history evidence.** 10,000+ experiments per year
  sustained at Spotify for over a decade.
* **OpenFeature contribution.** iOS and Android provider SDKs
  donated to the CNCF, with Spotify on the OpenFeature governance
  committee.

### Cons vs Split

* **No bundled CI/CD or DevOps platform.** Harness FME ships
  alongside Continuous Delivery, Continuous Integration, Cloud
  Cost Management, and AI-powered code agents. Teams that want
  experimentation alongside the rest of their software-delivery
  stack under one vendor will prefer Harness's bundled offering.
* **Smaller customer reference base.** Split has named customers
  including Twilio, Salesforce, GoDaddy, Electronic Arts, and
  Rocket Mortgage; Confidence's external reference base is more
  recent.
* **No mSPRT-based sequential testing.** Confidence's sequential
  testing is Group Sequential Tests with always-valid inference;
  buyers who specifically want mSPRT (mixture sequential
  probability ratio test) should use Split.

***

## 2. LaunchDarkly

### Overview

LaunchDarkly is the dominant enterprise feature flag platform.
Founded 2014 in Oakland by Edith Harbaugh and John Kodumal,
privately held with \~\$330M raised. As of early 2026, 5,500+
customers and 45 trillion flag evaluations per day. The platform
covers feature flags, experimentation, AI Configs, Guarded
Releases, and Observability (acquired with Highlight.io in April
2025\). LaunchDarkly Federal carries FedRAMP Moderate authorization
since January 2023.

Co-founder Edith Harbaugh returned as CEO in August 2025.

### Key features

* Industry-defining feature flag governance: approval workflows,
  RBAC, SSO/SCIM, audit trail attributable per change.
* FedRAMP Moderate (LaunchDarkly Federal).
* Experimentation across paid tiers: CUPED, frequentist sequential
  testing, sample ratio mismatch detection, guardrail metrics.
* Bundled observability via Highlight.io: error monitoring,
  session replay, traces.
* AI Configs (GA May 2025) for A/B testing prompts and models.
* Three MCP servers (hosted, local, observability).

### Pros vs Split

* **Mature flag-governance surface.** Approval workflows,
  change-management policies, audit trails. Harness FME's
  governance is functional but less developed.
* **FedRAMP Moderate authorization.** Harness FME does not publish
  a FedRAMP listing.
* **CUPED variance reduction.** LaunchDarkly ships CUPED; Harness
  FME does not list it.
* **Independent vendor.** LaunchDarkly's roadmap is not set inside
  a CI/CD platform.
* **Bundled observability via Highlight.io.**

### Cons vs Split

* **No bundled CI/CD or release coordination.** Harness FME ships
  inside the Harness platform alongside Continuous Delivery and
  Continuous Integration. LaunchDarkly's bundled scope is flags,
  experimentation, AI Configs, and observability.
* **Pricing.** LaunchDarkly Enterprise and Guardian tiers are
  sales-gated, with third-party estimates of $19,500–$200,000+
  ACV. Harness FME's free Developer tier (up to 10 seats) is the
  easier on-ramp for small teams.

***

## 3. Statsig

### Overview

Statsig was acquired by OpenAI in September 2025; Vijaye Raji,
its founder, is now CTO of Applications at OpenAI. The product
itself is a bundle of feature flags, A/B testing, product
analytics, session replay, and funnels, with a Warehouse Native
mode added in recent releases.

Founded in 2021 by Raji and other ex-Facebook engineers, Statsig
attracts product-led startups with the bundled product covering
experiments, flags, analytics, replay, and funnels and the free
tier.

### Key features

* Feature flags, A/B and multivariate testing, product analytics,
  session replay, and funnels in one product.
* Warehouse Native mode plus the original mode.
* CUPED variance reduction and sequential testing.
* Free tier with a monthly event allowance designed for early-stage
  teams.
* SDKs across major server and client languages.

### Pros vs Split

* **Bundled product analytics, session replay, and funnels.**
  Harness FME does not bundle analytics or replay. For teams that
  want one tool covering analytics and experimentation, Statsig
  is the broader bundle.
* **Free tier.** Statsig's free tier covers enough events for many
  early-stage teams.
* **CUPED variance reduction.** Statsig ships CUPED; Harness FME
  does not list it.

### Cons vs Split

* **OpenAI parent.** Statsig's roadmap is now set inside OpenAI;
  Harness FME's is set inside Harness. Buyers weighting vendor
  parent are picking between two ownership shapes.
* **Less methodology-focused customer base.** Split's customer
  base (Twilio, Salesforce, Electronic Arts, Rocket Mortgage) is
  more enterprise-engineering-led; Statsig's is product-led
  startups.

***

## 4. Eppo

### Overview

Eppo's defining choice is metric definitions in YAML, version-
controlled alongside your data infrastructure. Founded in 2020 by
Che Sharma, Eppo is closed-source and managed, with a focus on
warehouse-native experimentation analysis and first-class feature
flagging rather than a bundled CI/CD or DXP platform.

Eppo is the natural fit for data-science-led teams who already
version-control their data infrastructure and want the same
review discipline applied to metric definitions.

### Key features

* Warehouse-native architecture across BigQuery, Snowflake,
  Databricks, and Redshift.
* CUPED variance reduction and sequential testing.
* Metric definitions managed in code or YAML.
* Feature flagging with assignment SDKs.
* Slack-first notification surfaces for experiment lifecycle events.
* Support for combined observational and experimental workflows.

### Pros vs Split

* **CUPED variance reduction.** Eppo ships CUPED; Harness FME
  does not list it in public docs.
* **Independent vendor.** Eppo has not been acquired.
* **Metric-as-code workflow.** YAML version-controlled alongside
  dbt models. Harness FME's metric definitions are typically
  managed in the UI.
* **Experimentation-only company.** Eppo's roadmap and engineering
  investment are concentrated on experimentation.

### Cons vs Split

* **No CI/CD or release coordination.** Harness FME ships inside
  the Harness platform; Eppo is a focused experimentation product.
* **Smaller customer reference base** than Split's pre-acquisition
  customer list.

***

## 5. GrowthBook

### Overview

GrowthBook is the most-adopted open-source experimentation
platform, available under MIT license with a managed cloud option.
Warehouse-native, supports both Bayesian and frequentist analysis,
and appeals to teams that want full control over their
experimentation infrastructure or have compliance constraints
that favor self-hosting.

Open source matters to a specific kind of team: ones with data
residency requirements that make self-hosting easier than
contracting around them, or ones that already self-host the rest
of their stack.

### Key features

* Open source under MIT license; self-hosted on your infrastructure
  or run on GrowthBook Cloud.
* Warehouse-native. Runs on BigQuery, Snowflake, Databricks, and
  Redshift, plus broader engines like Postgres, ClickHouse, MySQL,
  and Athena.
* Both Bayesian and frequentist analysis methods supported.
* Feature flagging with targeting rules and gradual rollouts.
* Configuration-as-code, including metric definitions in YAML.
* Active open-source community contributing engines, integrations,
  and statistical extensions.

### Pros vs Split

* **Open source under MIT license.** Harness FME is closed source
  and embedded in a proprietary platform.
* **Self-hosting option.** GrowthBook can run on your infrastructure;
  Harness FME is managed-only.
* **Both Bayesian and frequentist analysis.**
* **Lower entry-level cost.** Self-hosted GrowthBook is free.

### Cons vs Split

* **Self-hosting overhead.** GrowthBook self-hosted means you
  operate the platform; Harness FME is managed.
* **No bundled CI/CD or release coordination.**
* **Smaller commercial support footprint** than Split's
  pre-acquisition enterprise account organization.

***

## 6. PostHog

### Overview

PostHog grew up as an open-source product analytics platform and
has added experimentation, feature flags, session replay, error
tracking, surveys, and a data warehouse. Founded January 2020 by
James Hawkins and Tim Glaser (YC W20). Series E in October 2025
led by Peak XV, \~\$1.4B valuation. The main repository is MIT-
licensed except for the `ee/` enterprise directory.

PostHog's experimentation methodology ships Bayesian peeking via
posterior win-probabilities, a frequentist t-test option (added
2025\), automatic SRM detection, and guardrail metrics. CUPED is
not shipped, and there is no SPRT or group-sequential frequentist
procedure.

### Key features

* Open source under MIT license (with proprietary `ee/` enterprise
  directory).
* Product analytics, web analytics, session replay, error tracking,
  feature flags, A/B testing, surveys, data warehouse, CDP, Max
  AI assistant.
* Bayesian (default) and frequentist t-test analysis.
* Automatic SRM detection.
* Free tier covering 1M analytics events, 5K recordings, 1M flag
  requests, 250 surveys per month.

### Pros vs Split

* **Bundled product analytics, session replay, surveys, data
  warehouse.** Harness FME does not bundle product analytics.
* **Open source under MIT license.**
* **Free tier covering analytics and experimentation events.**

### Cons vs Split

* **No CUPED variance reduction.** Both Harness FME and PostHog
  lack CUPED in public docs; this is a wash on that single point.
* **No frequentist sequential testing in the SPRT or group-
  sequential sense.** Bayesian peeking only, plus a fixed-horizon
  t-test option. Harness FME ships mSPRT.
* **No CI/CD or release coordination.** Harness FME ships inside
  the Harness platform.

***

## 7. Optimizely

### Overview

Optimizely is a Digital Experience Platform (DXP) with three
product pillars (Experiment, Orchestrate content, Monetize
commerce). Owned by Insight Partners since 2018. Optimizely's
Stats Engine ships CUPED, sequential SRM detection, a Bayesian
engine, and Warehouse-Native Experimentation Analytics as of
2024–2025.

Marketing-led enterprises come to Optimizely for web personalization
and conversion-rate optimization at scale, often within a content
management system (CMS) deployment.

### Key features

* Web Experimentation with WYSIWYG visual editor (2025 overlay
  version with Opal AI variation generation).
* Feature Experimentation (formerly Full Stack) for server-side
  experimentation.
* Bundled CMS (Optimizely Content Cloud), commerce, and
  personalization.
* Stats Engine with sequential testing, FDR control, CUPED,
  sequential SRM detection, Bayesian engine, and Warehouse-Native
  Experimentation Analytics.
* Opal AI agent layer.

### Pros vs Split

* **CUPED variance reduction.** Optimizely ships CUPED; Harness
  FME does not list it.
* **Bundled CMS, commerce, and personalization.** A different
  bundle than Harness FME's CI/CD platform; better fit for
  marketing-led enterprises.
* **Both Bayesian and frequentist engines.**
* **WYSIWYG visual editor for marketing-led web testing.**

### Cons vs Split

* **Pricing.** Optimizely is fully sales-gated; third-party
  estimates put entry-level pricing at $36,000–$60,000 per year.
  Harness FME's free Developer tier is the easier on-ramp.
* **Not built for engineering-led experimentation.** Optimizely
  Web Experimentation is built for marketers; Harness FME serves
  the engineering-led buyer Split historically targeted.

***

## Which alternative fits which buyer

Choose **Confidence** if you want an experimentation-first vendor
with opinionated frequentist defaults built on 15 years of
Spotify-scale operating evidence, CUPED with the Negi–Wooldridge
(2021) estimator, and OpenFeature portability at the SDK layer.

Choose **LaunchDarkly** if your evaluation is about feature flag
governance with experimentation as a complementary capability:
approval workflows, audit trails, FedRAMP Moderate compliance,
bundled observability via Highlight.io.

Choose **Statsig** if you want a bundled product covering
experiments, flags, analytics, replay, and funnels with a
free-tier entry. Statsig has been an OpenAI subsidiary since
September 2025.

**Eppo** is the right choice if metric definitions in code and
Slack-first lifecycle notifications are the workflow you want,
and if you want CUPED on a managed warehouse-native platform.

**GrowthBook** is the open-source option, MIT-licensed and
self-hostable. Pick it when open source or self-hosting is
non-negotiable.

Choose **PostHog** if you want product analytics, session replay,
surveys, and feature flags under one open-source umbrella, and
if Bayesian-default experimentation methodology with no CUPED is
acceptable for your program.

Choose **Optimizely** if you want a content-and-commerce DXP suite
alongside experimentation, particularly for marketing-led web CRO.

Pick on the constraint that actually binds your team, whether
that is vendor parent stability, CUPED methodology, FedRAMP
Moderate compliance, bundled CI/CD or analytics, open source, or
operating-history evidence. Each constraint picks a different
vendor on this list.

***

*See also: [Confidence vs Split head-to-head](/comparisons/confidence-vs-split) · [What is Split?](/comparisons/what-is-split)*