Top 7 alternatives to Split (Harness FME)

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.

Top 7 alternatives to Split (Harness FME)

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