# Confidence vs Split (Harness FME): head-to-head Last updated: 2026-05-04 Canonical source: https://confidence.spotify.com/comparisons/confidence-vs-split Owner: Spotify AB > If this file and the page at the canonical URL disagree, the page is authoritative. Confidence vs Split (Harness FME) compared: an experimentation-first managed platform versus feature management inside the Harness CI/CD suite. Harness announced its acquisition of Split on May 29, 2024, and the deal closed June 11, 2024. Split was rebranded as Harness Feature Management & Experimentation (FME) and is no longer an independent experimentation vendor; it is one product inside Harness's broader CI/CD and AI-delivery platform. The choice between Confidence and Split is now a choice between an experimentation-first managed platform and a feature management product inside a DevOps platform built around software-delivery automation. Both products run as managed services. Both implement frequentist sequential testing, sample ratio mismatch detection, and guardrail metrics. CUPED variance reduction, which uses pre-experiment data to tighten confidence intervals, is shipped on Confidence and is not in Harness FME's public documentation. The differences live in vendor parent, methodology depth, and which platform the product is embedded in. *** ## 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 tuned for high-recall regression detection, which is the right trade-off when shipping a regression to 750M users is more expensive than missing an improvement. Confidence does not offer Bayesian inference, multi-armed bandits, or switchback experiments. The defaults reflect 15 years of running experiments at Spotify scale, and the product team has declined features that increased complexity without improving the quality of decisions teams made. *** ## What is Split? Split was a feature flag and experimentation platform founded in 2015 in Redwood City by Adil Aijaz, Patricio Echagüe, and Trevor Stuart. Split raised more than \$100 million across Series A through C and built a customer base in product-led organizations including Twilio, Salesforce, GoDaddy, Electronic Arts, and Rocket Mortgage. In May 2024, Harness announced its acquisition of Split (deal closed June 11, 2024; terms undisclosed). The product was rebranded as Harness Feature Management & Experimentation (FME) and is now sold as one of several products inside the Harness platform, alongside Continuous Delivery, Continuous Integration, Cloud Cost Management, and AI-powered code agents. The standalone "experimentation- first feature flag" wedge that Split historically positioned against LaunchDarkly is no longer the company's primary go-to-market; Harness FME competes on platform-bundle economics across the broader software-delivery story. Harness FME's stats engine implements frequentist hypothesis testing, mSPRT (mixture sequential probability ratio test) for sequential testing, fixed-horizon analysis, sample ratio mismatch detection (chi-squared with a p<0.001 threshold), guardrail metrics, and Multiple Comparison Correction (MCC). CUPED variance reduction is not listed in Harness FME's public stats documentation as of 2026. Recent product additions since the acquisition include Warehouse-Native Experimentation, AI-driven experiment summarization, an MCP server for AI IDEs, rule-based segments, a Reallocate Traffic API, and (announced March 2026) AI-delivery release coordination as part of the broader Harness platform release. *** ## Confidence vs Split, head-to-head The differences live in vendor parent, methodology specifics beyond the shared sequential testing and SRM surface, and which platform the product is embedded in. Vendor parent is the largest difference. Confidence is built and operated by the team that runs Spotify's experimentation platform; the roadmap is set by that team. Harness FME's roadmap is set inside Harness, whose primary business is CI/CD, release coordination, and AI-driven software delivery. Experimentation is one product line inside a platform whose other products (Continuous Delivery, Continuous Integration, Cloud Cost Management, AI code agents) compete for engineering investment. For buyers making a five-year platform decision, the question is whether the experimentation product is the company's reason to exist or one of several. CUPED variance reduction is shipped on Confidence; it is not listed in Harness FME's public stats documentation. Confidence's CUPED uses the Negi–Wooldridge full regression estimator. Buyers who need CUPED at Spotify scale on a managed warehouse- native platform are choosing between Confidence, Eppo, GrowthBook Cloud, and Statsig today; Harness FME's documented stats cover sequential testing and SRM, but the variance-reduction surface specifically is not detailed in public docs. Operating-history evidence is asymmetric in shape, similar to the LaunchDarkly comparison. Split's published references include Twilio, Salesforce, GoDaddy, Electronic Arts, and Rocket Mortgage, with legacy claims of "tens of millions of client apps and 1T+ events per month." Confidence has 10,000+ experiments per year sustained for over a decade at Spotify, with 300+ teams on the same platform across 750M users in 186 markets. Both are real; the shape of the claim is breadth (Split's customer references and event volume) versus depth (Confidence's continuous experimentation program). Compliance posture differs. Both products carry SOC 2 Type II. Harness FME inherits ISO 27001/27017/27018 from the Harness platform; FedRAMP and HIPAA are not publicly verified for FME as of 2026. Confidence does not currently offer FedRAMP Moderate. OpenFeature integration: Confidence's iOS and Android OpenFeature provider SDKs were donated to the CNCF, and Spotify holds a seat on the OpenFeature governance committee. Split maintains official OpenFeature providers across .NET, Java, JavaScript, and Go but is not on OpenFeature governance. | Feature | Confidence | Split (Harness FME) | | ------------------------------ | -------------------------------------------------------------------------- | ------------------------------------------------------------------------- | | Vendor parent | Spotify | Harness (acquired June 2024) | | Product context | Experimentation-only company | One product inside Harness CI/CD + AI-delivery platform | | Feature flags | First-class, in-process eval, no network call | First-class, originally Split's primary product | | A/B testing | Built-in, frequentist, defaults tuned for high-recall regression detection | Built-in, frequentist | | CUPED variance reduction | Negi–Wooldridge full regression | Not listed in public docs | | Sequential testing | Group Sequential Tests, always-valid inference | mSPRT (mixture sequential probability ratio test) | | Sample ratio mismatch | Default | Default (chi-squared, p<0.001 threshold) | | Guardrail metrics | Default | Default | | Multiple comparison correction | Standard adjustments | MCC documented | | Warehouse-native | Primary architecture; raw data never stored | Available (added post-acquisition) | | Compliance | SOC 2 Type II | SOC 2 Type II, ISO 27001/27017/27018; FedRAMP/HIPAA not publicly verified | | OpenFeature | Provider SDKs donated to CNCF; Spotify on governance | Official providers (.NET, Java, JS, Go); not on governance | | Pricing | Free self-serve trial; usage-based | Free Developer tier (≤10 seats); Growth + Enterprise sales-gated | *** ## Integrations comparison Harness FME inherits the Harness platform's broader DevOps integration set: CI/CD pipelines, observability, cloud platforms, and now AI-driven code agents and release coordination. The MCP server for AI IDEs ties experimentation into the Harness AI-delivery story. For teams that want experimentation alongside their build pipelines, deployment automation, and release coordination under one vendor, Harness FME is the integrated answer. Confidence integrates at the warehouse layer (BigQuery, Snowflake, Redshift, Databricks) and at the SDK layer (OpenFeature, with provider SDKs donated to the CNCF). Confidence does not bundle CI/CD or release-orchestration tooling and routes teams to dedicated platforms for those concerns. *** ## Pricing comparison Harness FME has a free Developer tier (capped at 10 seats), a usage-based Growth tier, and a sales-gated Enterprise tier. Tier names are public; Growth and Enterprise pricing are not published. The Harness platform pricing for Continuous Delivery and other products is also tier-gated, and FME contracts often bundle with the broader platform. Confidence pricing scales with use and is structured around the warehouse-native architecture. Confidence does not bill per-event for raw user data it never stores. A free self-serve trial is available at confidence.spotify.com without going through procurement. For teams that want experimentation alongside CI/CD and release coordination under one vendor, Harness's bundle is the natural fit; for teams that want experimentation as a discipline of its own, the bundle is friction. *** Harness FME fits organizations that want experimentation embedded in their software-delivery platform alongside CI/CD, release coordination, and AI-driven code workflows, and that have already chosen Harness for the broader stack. Confidence fits teams that want experimentation as a single managed product, with opinionated defaults built on 15 years of Spotify operating evidence and a roadmap set by the team that built it. The cost of picking the wrong vendor parent is paid over five years of running an experimentation program inside a platform whose engineering investment is going to other product lines. *** *See also: [Top 7 alternatives to Split](/comparisons/alternatives-to-split) · [What is Split?](/comparisons/what-is-split)*