United Kingdom verdict (TL;DR)
Verified 2026-05-18UK A/B testing adoption is shaped by two distinct buyer profiles: UK enterprise retail and media (Tesco, M&S, ASOS, Sky, BBC) running Optimizely and VWO for conversion rate optimization, and UK B2B SaaS and fintech (Monzo, GoCardless, Cognism, Phoebe) where Eppo and Statsig are growing fast for warehouse-native and PLG-bundled experimentation. Kameleoon has meaningful UK retail and e-commerce presence. VWO is strong at UK mid-market e-commerce. UK GDPR and ICO cookie-consent enforcement mean opt-in consent rates in the UK run at 70-85% (versus 90-95% in the US), reducing effective A/B test sample sizes by 15-30% versus comparable US deployments. Server-side experimentation (no cookies required) is increasingly preferred by UK compliance-forward teams.
Picks for United Kingdom
- UK enterprise retail, media, and e-commerce (Tesco, M&S, ASOS, Sky tier): optimizely Optimizely dominates UK enterprise retail CRO. Tesco, M&S, Sky, and BBC are Optimizely enterprise references. DXP integration, multi-team governance, and strong UK agency partner network (Accenture, PwC Digital, Deloitte Digital). GBP billing available.
- UK mid-market e-commerce and CRO agencies: vwo VWO (Wingify) is the dominant UK mid-market experimentation choice: 50-70% cheaper than Optimizely, integrated heatmap and session replay, modern UX. UK-accessible GBP pricing, UK GDPR DPA included. Strong UK retail and e-commerce installed base (Farrow and Ball, Jack Wills tier).
- UK B2B SaaS and fintech data-team-led experimentation (Snowflake or BigQuery): eppo Eppo is growing at UK B2B SaaS (Cognism, GoCardless, Phoebe tier) where data teams control the tooling decision and Snowflake or BigQuery is the source of truth. Warehouse-native architecture avoids cookie-based tracking entirely, which is a meaningful UK GDPR compliance benefit.
- UK product-led growth teams (flags plus experiments plus analytics bundled): statsig-experiment Statsig winning London-tier PLG companies on bundled experiments plus flags plus analytics. EU data residency. GBP billing via reseller. Server-side assignment avoids UK GDPR cookie-consent exposure.
- UK retail and financial services wanting strong server-side plus personalization: kameleoon Kameleoon has genuine UK retail and financial services presence. Strong server-side SDK. EU data residency. UK GDPR DPA. Competitive with Optimizely at UK mid-enterprise scale.
- UK teams already on Amplitude: amplitude-experiment Amplitude has significant UK installed base (Farfetch, Deliveroo-tier). Amplitude Experiment extends into server-side experiments with behavioral cohort targeting directly from Amplitude analytics. GBP billing. UK GDPR DPA available.
How the a/b testing and experimentation software market looks in United Kingdom
UK A/B testing is a mature market with clear segment leaders. UK enterprise retail and media (Tesco, M&S, ASOS, Sky, BBC, John Lewis, Boots) run Optimizely for CRO programs that are typically 3-5 years old with strong agency (Accenture, PwC Digital) relationships. Switching costs are high and Optimizely defends this installed base through account management and DXP integration depth.
UK B2B SaaS and fintech is the growth segment. London's fintech cluster (Monzo, GoCardless, Wise, Checkout.com, Revolut) and B2B SaaS companies (Cognism, Phoebe, Marshmallow) are buying Eppo and Statsig for warehouse-native experimentation as their data engineering teams mature. This is the same warehouse-native shift visible in the US, arriving in the UK 12-18 months later at comparable scale.
UK GDPR and ICO enforcement create a meaningful structural constraint on UK A/B testing. ICO guidance requires genuine opt-in consent for analytics and testing cookies before they fire. UK consent-opt-in rates run 70-85% across most sectors (versus 90-95% in the US), which is a 15-30% effective sample size reduction for cookie-based A/B testing. This makes server-side experimentation (Eppo, Statsig, Kameleoon server-side, Amplitude Experiment) materially more attractive in the UK than in the US, because server-side assignment using server-side session tokens avoids PECR cookie requirements and delivers cleaner sample sizes.
Kameleoon is the European platform with the most meaningful UK standalone presence: it has UK enterprise references, UK-based sales representation, and strong server-side SDK depth. AB Tasty has some UK presence, primarily in UK subsidiaries of French enterprises.
Convert.com (Amsterdam) has a strong privacy-first UK positioning and appears in UK fintech experimentation alongside Mixpanel or Amplitude for teams that want GDPR-native consent-first design.
UK GDPR + DPA 2018: experiment assignment data (visitor IDs, device identifiers, variant assignments) constitutes personal data; UK GDPR DPAs required from all US-headquartered experimentation vendors. PECR: cookies used for A/B testing require prior informed consent; opt-in rates 70-85% typical in UK, reducing effective sample sizes. Server-side experimentation using first-party session tokens (Eppo, Statsig, Kameleoon server-side, Amplitude Experiment server-side SDK) avoids PECR cookie-consent requirements. UK IDTA (International Data Transfer Agreement): required for UK-to-US data transfers; US experimentation vendors (Optimizely, VWO, Amplitude, Eppo) must hold current IDTA-compliant DPA addenda. ICO enforcement: ICO has actively investigated experimentation and analytics tooling that bypasses consent; cookie consent must genuinely block tracking until opt-in is received. FCA: UK financial services experimenting on regulated products (loan decisions, investment recommendations) must ensure experiment variants do not create unfair discriminatory outcomes under FCA Consumer Duty (2023); experiment governance must document variant safety review.
Quick comparison, ranked for United Kingdom
| Product | Best for | Starts at | 10-emp/mo* | Pricing | G2 | Geo |
|---|---|---|---|---|---|---|
| 1 Optimizely | Enterprise marketing teams | Quote | - | 4.3 | North America +2 | |
| 3 VWO | SMB and mid-market | $199 | $199 | 4.4 | North America +3 | |
| 6 Eppo | Data-team-led PLG companies | Quote | - | 4.7 | North America +1 | |
| 7 Statsig Experiments | PLG teams with unified platform | $0 | $0 | 4.7 | North America +2 | |
| 5 Kameleoon | European mid-market and enterprise | Quote | - | 4.6 | Europe +1 | |
| 2 AB Tasty | European mid-market and upper-mid-market | $1900 | $1900 | 4.5 | Europe +2 | |
| 10 Amplitude Experiment | Amplitude Analytics customers | Quote | - | 4.5 | North America +2 | |
| 9 LaunchDarkly Experimentation | LaunchDarkly customers | Quote | - | 4.5 | North America +2 | |
| 8 GrowthBook Experiments | Engineering-led teams | $0 | $0 | 4.7 | North America +2 | |
| 4 Convert | Privacy-first mid-market | $350 | $350 | 4.7 | Europe +1 |
*10-employee monthly cost = base fee + (per-employee × 10) using the lowest published tier. For opaque-pricing vendors, no value is shown.
What buyers in United Kingdom actually pay
Median annual deal size by employee band, in GBP. Crowdsourced from anonymized buyer disclosures.
| Product | Employee band | Median annual (GBP) | Sample | Notes |
|---|---|---|---|---|
| Optimizely | 500-5,000 employees | £74,000 | 43 | Web or Feature Experimentation; GBP-billed; UK agency deals common |
| VWO | 50-500 employees | £9,600 | 72 | Growth or Pro plan; GBP-billed; UK GDPR DPA included |
| Eppo | 100-1,000 employees | £32,000 | 21 | Platform tier; GBP-equivalent; warehouse-native |
| Statsig Experiments | 50-500 engineers | £18,500 | 38 | Pro tier; GBP-equivalent; EU data residency |
| Kameleoon | 500-5,000 employees | £44,000 | 18 | Business tier; GBP-billed; EU/UK data residency |
| Convert | 100-1,000 employees | £6,600 | 27 | Plus plan; GBP-equivalent; EU data residency |
| Amplitude Experiment | 200-1,000 employees | £27,500 | 31 | Add-on to Amplitude Analytics; GBP annual |
United Kingdom-built or United Kingdom-strong vendors worth knowing
Not yet ranked in our global top 10, but credible options for United Kingdom buyers and worth a shortlist.
Kameleoon (UK presence)
Visit ↗Paris-headquartered but with meaningful UK sales and enterprise presence. Used by UK retail (Currys, Halfords tier) and UK financial services. Strong server-side SDK avoids PECR cookie-consent issues. EU/UK data residency. GBP billing available.
Convert.com (UK privacy-first)
Visit ↗Amsterdam-headquartered, strong UK presence in privacy-conscious fintech and e-commerce. Bootstrapped and founder-led. EU/UK data residency, UK GDPR DPA native. Used alongside Mixpanel or Amplitude at UK fintech for GDPR-consent-first experimentation. GBP pricing available.
All 10, ranked for United Kingdom
Same intelligence as the global ranking, vendor trust, review patterns, verified pricing, compliance, reordered for the United Kingdom market.
Optimizely
Enterprise experimentation leader with deepest CMS-integrated marketing-led testing.
Optimizely launched 2010 (founders Dan Siroker, Pete Koomen ex-Google), went through several restructurings, was acquired by Insight Partners in 2020 ($600M acquisition), and merged with Episerver in 2021 to form the current Optimizely DXP. The platform remains the enterprise experimentation leader with deepest CMS-integration positioning, broad market reach, and Fortune-1000 customer references. Wins on enterprise scalability, multi-team experimentation programs, and DXP-anchored marketing experimentation. Loses on post-Insight-Partners product investment cadence (visibly slower than warehouse-native peers), pricing complexity, and renewal pricing pressure (15-25% common per customer disclosures).
Enterprise marketing teams (2000+ employees) running CMS-integrated experimentation alongside DXP content management.
Product-led growth teams wanting warehouse-native experimentation (Eppo + Statsig fit better); SMB with budget constraints (VWO fit better).
Strengths
- Enterprise experimentation leader with Fortune-1000 customer references
- Deepest CMS-integration via Optimizely DXP (Episerver heritage)
- Multi-team experimentation programs at enterprise scale
- Strong client-side + server-side SDKs across major languages
- Broad market reach with strong agency + implementation-partner ecosystem
- Optimizely Web + Performance + Feature unified platform
Weaknesses
- Post-Insight-Partners product investment cadence visibly slower than warehouse-native peers
- Pricing complexity with multiple add-on charges
- Renewal pricing pressure 15-25% common per customer disclosures
- Warehouse-native architecture not native; traditional vendor-managed analytics
- Implementation complexity at enterprise scale (typically 8-16 weeks)
- Customer-support quality varies post-2021 Episerver merger restructure
Pricing tiers
opaque- Web ExperimentationClient-side experimentation; up to 100K MAUQuote
- Feature ExperimentationServer-side + feature flags; up to 500K MAUQuote
- Enterprise DXPFull DXP + Experimentation + CMS bundleQuote
- · Implementation services $20K-$120K typical
- · Add-on feature charges for personalization, recommendations
- · Renewal pricing pressure 15-25% common
Key features
- +Client-side + server-side SDK across major languages
- +Optimizely DXP CMS integration
- +Audience targeting and segmentation
- +Multivariate testing + A/B testing + split URL testing
- +Personalization and recommendations engine
- +Advanced statistical methods (Bayesian + frequentist)
- +Multi-team experimentation governance
- +Strong reporting and dashboards
VWO
Affordable mid-market experimentation with integrated heatmap and session-replay.
VWO (Visual Website Optimizer) launched 2009 by Wingify (founder Paras Chopra) and serves SMB-to-mid-market customers with the most affordable pricing in category plus integrated heatmap + session-replay + personalization. Wins on price-per-visitor (typically 50-70% cheaper than Optimizely at SMB scale), modern UX, and integrated UX-research tools. Loses on enterprise scalability for Fortune-500, US market presence in enterprise procurement defaults, and warehouse-native architecture.
SMB and mid-market (100-3000 employees) wanting affordable experimentation with integrated UX-research.
Enterprise Fortune-500 (Optimizely fit better); warehouse-native PLG teams (Eppo + Statsig fit better).
Strengths
- Affordable price-per-visitor (50-70% cheaper than Optimizely at SMB scale)
- Integrated heatmap + session-replay + personalization on one platform
- Modern UX with strong SMB and mid-market reputation (4.5+ G2)
- Bootstrapped and founder-led with consistent strategy
- Strong client-side experimentation
- Multi-language platform support
Weaknesses
- Enterprise scalability for Fortune-500 limited
- US enterprise procurement-defaults lower than Optimizely
- Warehouse-native architecture not native; traditional vendor-managed analytics
- Server-side experimentation thinner than Optimizely + Statsig
- Customer-support quality varies (4.4-4.5 G2)
Pricing tiers
public- StarterUp to 10K MAU; basic experimentation$199 /mo
- GrowthUp to 50K MAU; advanced features$499 /mo
- ProUp to 200K MAU; multi-team governance$999 /mo
- EnterpriseUnlimited MAU; custom featuresQuote
- · Add-on charges for heatmap, session-replay, personalization
- · Implementation services $3K-$15K typical
Key features
- +Client-side experimentation
- +Integrated heatmap + session-replay (add-on)
- +Personalization engine
- +Audience targeting and segmentation
- +Multivariate testing + A/B testing + split URL
- +Bayesian + frequentist statistical methods
- +Modern UX with creator-friendly features
- +Mobile-app testing SDK
Eppo
Warehouse-native experimentation platform built for data teams.
Eppo launched 2020 (founder Chetan Sharma ex-Stitch Fix) and closed a $26M Series A May 2022 led by Menlo Ventures. The platform pioneered the warehouse-native experimentation category: experiment results computed directly on customer data warehouse (Snowflake, BigQuery, Redshift, Databricks) rather than on Eppo-managed analytics infrastructure. Wins on warehouse-native architecture, metric flexibility, and data-team trust. Loses on time-to-first-result versus traditional vendors, brand mindshare in marketing-led procurement defaults, and smaller installed base than category leaders.
Data-team-led PLG companies (300-5000 employees) running warehouse-native experimentation on Snowflake/BigQuery/Redshift.
Marketing-led experimentation (Optimizely + VWO + AB Tasty fit better); SMB without data warehouse.
Strengths
- Warehouse-native architecture (Snowflake, BigQuery, Redshift, Databricks)
- Strong metric flexibility (users define metrics in SQL)
- Data-team-friendly UX with deep statistical methods
- Strong Bayesian statistical methods with sequential testing
- Modern UX with growing PLG-team customer base
- Founder-led with consistent strategy through 2026
Weaknesses
- Time-to-first-result heavier than traditional vendors (data-warehouse setup gating step)
- Brand mindshare in marketing-led procurement defaults lower than Optimizely + VWO
- Smaller installed base than category leaders
- Marketing-led experimentation UX less mature than Optimizely + VWO
- Capital base smaller than Statsig
Pricing tiers
partial- StarterUp to 100K MAU; warehouse-native experimentationQuote
- GrowthUp to 1M MAU; advanced statistical methodsQuote
- EnterpriseUnlimited MAU; multi-team governanceQuote
- · Implementation services $10K-$40K typical
- · Add-on charges for advanced statistical methods
Key features
- +Warehouse-native architecture (Snowflake, BigQuery, Redshift, Databricks)
- +Metric flexibility (users define metrics in SQL)
- +Bayesian statistical methods with sequential testing
- +Client-side + server-side SDK
- +Audience targeting and segmentation
- +Multi-team experimentation governance
- +Modern UX with data-team focus
- +API-first architecture
Statsig Experiments
Unified experimentation + feature flags + product analytics on one platform.
Statsig launched 2021 (founder Vijaye Raji ex-Meta) and closed a $43M Series B Sep 2022. The platform positions distinctively in the category: unified experimentation + feature flags + product analytics on one stack with aggressive freemium positioning (10B events free monthly). Wins on unified platform value, freemium scale, and modern UX. Loses on enterprise sales motion still maturing, brand mindshare in marketing-led procurement defaults, and post-2022 capital base questions. Note: Statsig also appears in our Feature Flag Software ranking under the statsig product entry; this entry covers Statsig Experiments specifically.
PLG teams (50-3000 employees) wanting unified experimentation + feature flags + product analytics on one stack.
Marketing-led enterprise experimentation (Optimizely fit better); standalone experimentation buyer (Eppo fit better).
Strengths
- Unified experimentation + feature flags + product analytics on one platform
- Aggressive freemium positioning (10B events free monthly)
- Modern UX with rapid time-to-value
- Strong client-side + server-side + edge SDKs
- Warehouse-native experiment results option
- Founder-led (ex-Meta) with consistent strategy
Weaknesses
- Enterprise sales motion still maturing
- Brand mindshare in marketing-led procurement defaults lower
- Post-2022-Series-B capital base smaller than Optimizely
- Marketing-led experimentation UX less mature than Optimizely + VWO
- Pricing transparency partial at higher tiers
Pricing tiers
partial- FreeUp to 10B events monthly; unified platform$0 /mo
- ProAdvanced features + premium supportQuote
- EnterpriseMulti-team governance + custom SLAQuote
- · Overage charges at higher event volumes
- · Add-on charges for advanced features at Pro tier
Key features
- +Unified experimentation + feature flags + product analytics
- +Aggressive freemium positioning
- +Modern UX with rapid time-to-value
- +Client-side + server-side + edge SDKs
- +Warehouse-native experiment results option
- +Bayesian + frequentist statistical methods
- +Audience targeting and segmentation
- +Multi-team experimentation governance
Kameleoon
French experimentation platform with strong server-side and feature-flag integration.
Kameleoon launched 2012 in Paris and serves European and US mid-market and enterprise customers with strong server-side experimentation, integrated feature-flag platform, and AI-driven personalization. Wins on feature breadth versus AB Tasty, server-side SDK quality, and EU-data-residency. Loses on US market presence, brand mindshare in US procurement defaults, and capital base smaller than US peers.
European mid-market and enterprise wanting strong server-side + feature-flag-integrated experimentation.
US enterprise (Optimizely fit better); warehouse-native PLG teams (Eppo + Statsig).
Strengths
- Strong server-side SDK quality across major languages
- Integrated feature-flag platform on same architecture
- AI-driven personalization engine
- EU-data-residency native
- Modern UX with strong European-customer reputation
- Competitive mid-market and enterprise pricing
Weaknesses
- US market presence limited; primarily European focus
- Brand mindshare in US procurement defaults lower than Optimizely + AB Tasty
- Capital base smaller than US peers
- Warehouse-native architecture not native
- Smaller installed base than Optimizely + VWO
Pricing tiers
opaque- EssentialClient-side experimentationQuote
- BusinessServer-side + feature flagsQuote
- EnterpriseMulti-team governance + AI personalizationQuote
- · Implementation services $10K-$50K typical
- · Add-on charges for AI personalization
Key features
- +Client-side + server-side SDK across major languages
- +Integrated feature-flag platform
- +AI-driven personalization engine
- +EU-data-residency native
- +Audience targeting and segmentation
- +Multivariate testing + A/B + split URL
- +Bayesian + frequentist statistical methods
- +Multi-team experimentation governance
AB Tasty
French-headquartered experimentation leader with EU-compliance native positioning.
AB Tasty launched 2009 in Paris and serves European mid-market and upper-mid-market with strong EU-data-residency, GDPR-native positioning, and integrated feature-flag + personalization. The platform competes head-to-head with VWO in mid-market and with Optimizely in European enterprise. Wins on EU compliance posture, integrated feature-flag + personalization, and modern UX. Loses on US market presence, brand mindshare in US procurement defaults, and capital base smaller than US peers.
European mid-market and upper-mid-market (300-5000 employees) wanting EU-compliance-native experimentation.
US-headquartered enterprises (Optimizely + Eppo fit better); warehouse-native experimentation requirements (Eppo + GrowthBook).
Strengths
- EU-data-residency native; strong GDPR + Schrems II compliance
- Integrated feature-flag + personalization + experimentation on one platform
- Modern UX with strong European-customer reputation
- Multi-language support (French, German, Spanish, Italian)
- Strong mid-market and upper-mid-market market share in Europe
- Affordable pricing posture versus Optimizely
Weaknesses
- US market presence limited; primarily European focus
- Brand mindshare in US procurement defaults lower than Optimizely + VWO
- Capital base smaller than US peers
- Warehouse-native architecture not native; traditional vendor-managed analytics
- Sales motion lighter than US peers
Pricing tiers
partial- EssentialsUp to 100K MAU; client-side experimentation$1900 /mo
- GrowthUp to 500K MAU; client + server-side + feature flags$4500 /mo
- EnterpriseUnlimited MAU; multi-team governanceQuote
- · Implementation services $5K-$30K typical
- · Add-on charges for personalization + recommendations
Key features
- +Client-side + server-side SDK
- +Feature-flag platform integrated
- +Personalization and recommendations engine
- +Audience targeting and segmentation
- +EU-data-residency native
- +Multi-language platform (French, German, Spanish)
- +Bayesian + frequentist statistical methods
- +Strong reporting and dashboards
Amplitude Experiment
Amplitude product-analytics-anchored experimentation for PLG teams.
Amplitude (NASDAQ:AMPL) IPOd 2021 and serves PLG teams with product analytics extended into experimentation. Amplitude Experiment is the experimentation module that leverages Amplitude product-analytics signal directly. Wins on product-analytics integration depth, PLG-team customer base, and platform consistency. Loses on standalone-experimentation positioning, post-IPO stock decline (~80% from peak), and marketing-led experimentation features versus Optimizely. Note: Amplitude also appears in our Product Analytics ranking under the amplitude product entry; this entry covers Amplitude Experiment specifically.
Amplitude Analytics customers wanting experiments on top of existing product analytics.
Standalone experimentation buyers (Eppo + Statsig + Optimizely fit better); marketing-led teams.
Strengths
- Tight integration with Amplitude product analytics
- Strong PLG-team customer base
- Platform consistency with Amplitude Analytics
- Bayesian + frequentist statistical methods
- Modern UX consistent with broader Amplitude platform
- Multi-team experimentation governance
Weaknesses
- Standalone-experimentation positioning weak versus Eppo + Statsig + Optimizely
- Post-IPO stock decline ~80% from peak; turnaround in progress
- Marketing-led experimentation features thinner
- Pricing layered on top of Amplitude Analytics subscription
- Standalone-experimentation buyers will prefer Eppo + Statsig
Pricing tiers
opaque- Growth Add-onExperimentation add-on to Amplitude GrowthQuote
- Enterprise Add-onExperimentation add-on to Amplitude EnterpriseQuote
- · Pricing layered on top of Amplitude Analytics subscription
- · Implementation services included with Amplitude customer-success
Key features
- +Tight integration with Amplitude product analytics
- +Client-side + server-side SDK
- +Bayesian + frequentist statistical methods
- +Audience targeting and segmentation leveraging Amplitude cohorts
- +Modern UX consistent with Amplitude
- +Multi-team experimentation governance
- +Strong reporting and dashboards
- +API-first architecture
LaunchDarkly Experimentation
LaunchDarkly feature-flag-anchored experimentation for engineering-led teams.
LaunchDarkly launched 2014 (founders John Kodumal, Edith Harbaugh) and closed a $200M Series D Aug 2021 at $3B valuation. The experimentation module extends the dominant LaunchDarkly feature-flag platform into experiment-result-computation, primarily targeting LaunchDarkly customers wanting experiments on top of feature flags. Wins on feature-flag integration, engineering-led customer base, and platform consistency. Loses on standalone-experimentation positioning versus Eppo + Statsig + Optimizely, marketing-led features, and warehouse-native architecture. Note: LaunchDarkly also appears in our Feature Flag Software ranking under the launchdarkly product entry; this entry covers LaunchDarkly Experimentation specifically.
LaunchDarkly customers wanting experiments on top of existing feature-flag platform.
Standalone experimentation buyers (Eppo + Statsig + Optimizely fit better); marketing-led teams.
Strengths
- Tight integration with LaunchDarkly feature-flag platform
- Strong engineering-led customer base
- Platform consistency with LaunchDarkly Feature Flags
- Strong client-side + server-side SDK
- Modern UX consistent with broader LaunchDarkly platform
- Bayesian + frequentist statistical methods
Weaknesses
- Standalone-experimentation positioning weak versus Eppo + Statsig + Optimizely
- Marketing-led features thinner
- Warehouse-native architecture not native
- Implementation cost stack with feature-flag subscription
- Standalone-experimentation buyers will prefer Eppo + Statsig
Pricing tiers
opaque- Pro Add-onExperimentation add-on to LaunchDarkly ProQuote
- Enterprise Add-onExperimentation add-on to LaunchDarkly EnterpriseQuote
- · Pricing layered on top of LaunchDarkly Feature Flags subscription
- · Implementation services included with LaunchDarkly customer-success
Key features
- +Tight integration with LaunchDarkly feature-flag platform
- +Client-side + server-side SDK
- +Bayesian + frequentist statistical methods
- +Audience targeting and segmentation
- +Modern UX consistent with LaunchDarkly
- +Multi-team experimentation governance
- +Strong reporting and dashboards
- +API-first architecture
GrowthBook Experiments
Open-source warehouse-native experimentation with self-hosted option.
GrowthBook launched 2020 (founder Jeremy Dorn) and is the open-source warehouse-native experimentation platform. The MIT-licensed core is self-hostable for free; GrowthBook Cloud offers managed hosting with paid tiers. Wins on open-source MIT license (self-hosting option), warehouse-native architecture, and engineer-friendly positioning. Loses on UX polish versus Eppo + Statsig, marketing-led experimentation features, and brand mindshare in enterprise procurement defaults. Note: GrowthBook also appears in our Feature Flag Software ranking under the growthbook product entry; this entry covers GrowthBook Experiments specifically.
Engineering-led teams wanting open-source warehouse-native experimentation with self-hosting option.
Marketing-led enterprise experimentation (Optimizely fit better); no-engineering-capacity teams.
Strengths
- Open-source MIT-licensed core (self-hostable for free)
- Warehouse-native architecture (Snowflake, BigQuery, Redshift, Postgres)
- Engineer-friendly positioning
- Strong Bayesian + frequentist statistical methods
- Self-hosted deployment option for data-residency requirements
- Affordable Cloud tier pricing
Weaknesses
- UX polish versus Eppo + Statsig less mature
- Marketing-led experimentation features thinner
- Brand mindshare in enterprise procurement defaults lower
- Self-hosted deployment requires engineering capacity
- Capital base smaller than Eppo + Statsig
Pricing tiers
public- Self-HostedMIT-licensed open-source; self-hostable$0 /mo
- Cloud ProCloud-hosted; up to 100K MAU$250 /mo
- Cloud EnterpriseUnlimited MAU; SSO + premium supportQuote
- · Self-hosted requires engineering capacity for deployment + maintenance
- · Cloud Enterprise tier custom pricing
Key features
- +Open-source MIT-licensed core (self-hostable)
- +Warehouse-native architecture
- +Self-hosted deployment option for data-residency
- +Bayesian + frequentist statistical methods
- +Client-side + server-side SDK across major languages
- +Audience targeting and segmentation
- +Feature flag platform integrated
- +Visual editor for marketing-led experiments
Convert
Privacy-first experimentation with strong GDPR + CCPA compliance positioning.
Convert.com launched 2009 in Amsterdam and serves SMB-to-upper-mid-market customers with privacy-first experimentation, strong GDPR + CCPA compliance, and bootstrapped-founder-led trajectory. Wins on privacy positioning, EU-data-residency, and competitive mid-market pricing. Loses on US market presence, brand mindshare in US procurement defaults, and warehouse-native architecture.
Mid-market wanting privacy-first experimentation with strong EU compliance.
US enterprise (Optimizely fit better); warehouse-native PLG teams (Eppo fit better).
Strengths
- Privacy-first experimentation with strong GDPR + CCPA compliance
- EU-data-residency native
- Bootstrapped and founder-led with consistent strategy
- Competitive mid-market pricing
- Strong client-side + server-side SDKs
- Modern UX with strong customer reputation
Weaknesses
- US market presence limited
- Brand mindshare in US procurement defaults lower than peers
- Warehouse-native architecture not native
- Enterprise scalability for Fortune-500 limited
- Smaller installed base than peers
Pricing tiers
public- KickstartUp to 30K MAU; basic experimentation$350 /mo
- PlusUp to 250K MAU; advanced features$850 /mo
- EnterpriseUnlimited MAU; custom featuresQuote
- · Add-on charges for personalization
- · Implementation services priced separately
Key features
- +Client-side + server-side SDK
- +Privacy-first analytics architecture
- +EU-data-residency native
- +Personalization engine
- +Audience targeting and segmentation
- +Bayesian + frequentist statistical methods
- +Strong reporting and dashboards
- +GDPR + CCPA compliance native
Frequently asked questions
The questions buyers actually ask before they sign.
How much does UK GDPR cookie-consent really reduce A/B test sample sizes?
Is Eppo ready for UK enterprise or is it still US-focused?
Optimizely vs Kameleoon for UK retail enterprise in 2026?
Traditional vs warehouse-native experimentation, which one wins?
Why is Optimizely still ranked #1 over Eppo and Statsig?
When does VWO stop being enough?
How much should I budget for experimentation software?
What is the integrated feature-flag + experimentation question?
How is AI changing experimentation?
What is the warehouse-native architecture trade-off?
Do I need a dedicated experimentation platform if I have a feature-flag platform?
Bayesian vs frequentist statistics, which method matters?
What about Mutiny and Dynamic Yield for personalization-led experimentation?
Final word
Looking at a different market? See the global A/B Testing and Experimentation Software ranking, or pick another country at the top of this page.
Last updated 2026-05-18. Local pricing reverified quarterly. Found something inaccurate? Tell us.