Best A/B Testing Tools for Mobile Apps

Picking the wrong A/B testing tool for your mobile app doesn't just slow down your experimentation program — it can quietly drain your budget through MAU-based pricing, lock your data inside a vendor's infrastructure, or leave your team with statistical methods too shallow to trust at scale.

The tools in this space look similar on the surface, but they make very different tradeoffs around data ownership, pricing structure, SDK performance, and statistical rigor.

This guide is for engineers, product managers, and data teams who are evaluating A/B testing tools for iOS, Android, React Native, or Flutter apps — whether you're setting up your first experiment or replacing a tool that's no longer working for your team. Here's what each tool breakdown covers:

• What the tool is actually built for (and who it's not a great fit for)
• Key features that matter specifically for mobile — SDK evaluation speed, statistical engines, feature flag integration
• Pricing model and starter tier, so you can assess cost before talking to sales
• Honest tradeoffs compared to other tools in the category

Each tool is covered in its own section with the same structure, so you can read straight through or jump directly to the platforms you're already considering. The eight tools covered are GrowthBook, Firebase A/B Testing, Statsig, Optimizely, LaunchDarkly, Amplitude Experiment, Apptimize, and AB Tasty — ranging from free open-source options to enterprise SaaS platforms, and from mobile-only specialists to full-stack experimentation suites.

We also note where tools offer self-hosting options throughout, as this is a meaningful constraint for teams with data residency requirements or compliance mandates.

GrowthBook

Primarily geared towards: Engineering and product teams who want open-source, warehouse-native A/B testing with rigorous statistical controls and no per-event pricing penalties.

GrowthBook is an open-source feature flagging and experimentation platform built around a warehouse-native architecture — meaning experiment data stays in your own Snowflake, BigQuery, Redshift, or Databricks instance rather than being copied into a proprietary vendor system.

GrowthBook is built specifically for technical teams who already have event tracking in place and want to run experiments against that data without duplicating pipelines or surrendering data ownership.

Mobile teams in particular benefit from lightweight SDKs for iOS (Swift), Android (Kotlin), React Native, and Flutter, which evaluate feature flags locally with no blocking network calls. The JS SDK is 9kb — less than half the size of many competitors — which keeps mobile app performance unaffected by the experimentation layer.

Notable features:

• Warehouse-native architecture: GrowthBook connects directly to your existing data warehouse rather than ingesting data into a proprietary store. For mobile teams, this means experiment events captured by your current tracking setup are analyzed in place — no PII leaves your servers, and there's no secondary data pipeline to maintain.
• Multiple statistical engines: Bayesian, Frequentist, and Sequential testing are all supported within a single unified platform. Teams can choose the methodology that fits their workflow rather than being locked into one approach, and Sequential testing allows valid early stopping decisions without inflating false positive rates.
• Local SDK evaluation: Mobile SDKs serve feature flags from a cached JSON payload evaluated on-device. There are no blocking API calls at flag evaluation time, which keeps mobile app performance unaffected by the experimentation layer.
• CUPED variance reduction: GrowthBook supports CUPED (Controlled-experiment Using Pre-Experiment Data), which accounts for pre-experiment user behavior to reduce variance. This can help experiments reach statistical significance faster — a meaningful advantage for mobile apps where traffic accumulates more slowly than on web.
• Unified feature flags and experiments: Feature flags and A/B testing are both native capabilities of GrowthBook's unified platform — there is no separate tool for each use case. Mobile teams can run gradual rollouts, instant kill switches, and experiment assignments all in one place.
• Full SQL transparency: All SQL used to generate experiment results is exposed and exportable. Teams can audit the analysis directly or pull results into a Jupyter notebook — something most commercial experimentation platforms don't offer.

Pricing model: GrowthBook uses per-seat pricing (Pro tier at $40/user/month), not per-event or per-MAU pricing, so scaling experiment volume or mobile traffic doesn't increase your bill. An open-source self-hosted option is also available at no cost.

Starter tier: The free Cloud Starter plan supports up to 3 users and 1M events per month with no credit card required, including feature flags and A/B testing out of the box.

Key points:

• Open-source codebase means no vendor lock-in — your experiment configurations, metric definitions, and data remain fully under your control, and you can self-host if needed.
• Per-seat pricing makes GrowthBook cost-effective for teams running high experiment volumes across large mobile user bases, where MAU- or event-based pricing from other tools can become expensive quickly.
• GrowthBook's built-in statistical engine — covering Bayesian, Frequentist, Sequential testing, CUPED, and Benjamini-Hochberg multiple testing corrections — is more comprehensive than what most tools in this category include natively.
• Warehouse-native architecture eliminates the need to build or maintain a secondary data pipeline solely for experimentation, which reduces engineering overhead for mobile teams.
• GrowthBook is trusted by teams at Khan Academy, Upstart, and Breeze Airways, among 3,000+ companies worldwide.

Firebase A/B Testing

Primarily geared towards: Mobile developers and small product teams already embedded in the Google/Firebase ecosystem.

Firebase A/B Testing is Google's built-in experimentation layer for the Firebase platform, available to iOS and Android developers at no additional cost. It's designed to let teams run product and marketing experiments — from UI changes to push notification copy — without standing up a separate testing infrastructure. If you're already using Firebase Remote Config, Google Analytics, or Firebase Cloud Messaging, the setup friction is genuinely low.

Notable features:

• Remote Config integration: Experiments are built directly on top of Firebase Remote Config, meaning any variable-driven change in your app — feature flags, UI behavior, content — can be tested without additional instrumentation beyond what you've likely already set up.
• Push notification testing: Teams can A/B test FCM push notification copy and messaging settings natively, which is a meaningful differentiator for mobile teams running lifecycle or re-engagement campaigns without a separate tool.
• Crashlytics-aware targeting: Experiments can be scoped to users who have experienced crashes, as flagged by Crashlytics. This is an unusual and practically useful capability for mobile teams who want to test fixes or fallback experiences against affected user segments.
• Flexible audience segmentation: Variants can be targeted by app version, platform, language, Google Analytics audience membership, or custom user properties — all combinable with AND logic for precise control.
• Statistical significance reporting: Firebase performs backend analysis to determine which variant performed best and whether results meet a significance threshold before surfacing a rollout recommendation. The specific statistical method is not publicly documented in detail.
• Broad platform support: Confirmed support for iOS, Android, C++, and Unity. React Native and Flutter support should be verified directly in the Firebase documentation before assuming compatibility.

Pricing model: Firebase A/B Testing is free as part of the Firebase platform, which offers both a free Spark plan and a pay-as-you-go Blaze plan. The A/B Testing feature itself does not appear to be gated behind a paid tier, though experiment volume limits or MAU thresholds should be confirmed on the current Firebase pricing page before committing.

Starter tier: Free, with no separately documented caps on A/B Testing usage — verify current limits directly with Firebase documentation.

Key points:

• Firebase A/B Testing is a strong fit if your team is already on the Firebase/Google Analytics stack — the integration story is tight and the marginal cost is zero. Teams on Mixpanel, Amplitude, or Segment will find the ecosystem dependency a real constraint.
• The statistical reporting is functional but limited. There's no documented support for CUPED variance reduction, sequential testing, Bayesian vs. Frequentist engine selection, or multiple comparison corrections — capabilities that matter as your experimentation program matures.
• All experiment data lives in Google's infrastructure. Teams with data residency requirements or a preference for warehouse-native experimentation — keeping results in Snowflake, BigQuery, or Redshift under their own control — will hit a ceiling here.
• Firebase A/B Testing is well-suited for early-stage teams and indie developers who need to ship experiments quickly without a dedicated data or experimentation team. It's less suited for organizations that need statistical depth, data portability, or cross-platform experimentation beyond the Google ecosystem.

Statsig

Primarily geared towards: Engineering-led product teams at fast-moving SaaS, consumer app, and AI companies who want experimentation, feature flags, analytics, and session replay in a single platform.

Statsig was founded in 2020 by former Facebook engineers and has built a reputation for clean SDK design and serious statistical infrastructure — the platform processes over 1 trillion events daily at 99.99% uptime, which signals it can handle high-volume mobile apps without flinching. Customers like OpenAI, Notion, and Brex reflect a strong fit for developer-led organizations that move quickly and expect their tooling to keep up.

The core pitch is consolidation: instead of stitching together a feature flag tool, an experimentation platform, and a separate analytics layer, Statsig bundles all of it natively.

Notable features:

• Sub-millisecond SDK evaluation: Statsig's SDK evaluates flags and experiments locally on-device, which keeps latency negligible and avoids the battery drain that can come from network-dependent flag resolution — a meaningful consideration for mobile apps where performance directly affects retention.
• CUPED and sequential testing: Statsig's stats engine includes CUPED for variance reduction and sequential testing for continuous monitoring. Both help mobile teams reach statistical significance faster when working with smaller user bases.
• Warehouse-native deployment option: Teams can run Statsig in a warehouse-native configuration, keeping experiment data inside their own data warehouse rather than Statsig's cloud. This is a practical option for teams with data governance requirements.
• Native iOS and Android SDKs: Both platforms are explicitly supported. React Native support is also available; Flutter SDK support should be verified on Statsig's documentation before assuming it fits your stack.
• Unified platform surface: Experimentation, feature flags, product analytics, and session replay are all available within a single Statsig product, reducing the overhead of managing multiple vendor contracts and reconciling data across systems.

Pricing model: Statsig offers a free tier alongside paid plans, but specific tier names, event caps, and dollar amounts are not published transparently in most third-party sources — check statsig.com/pricing directly for current details before making a vendor decision.

Starter tier: A free tier exists, though the specific limits on events, seats, and feature access are not confirmed in available research and should be verified on Statsig's pricing page.

Key points:

• Statsig is a proprietary SaaS product with no open-source option, which means teams that require full code transparency, self-hosting for compliance, or the ability to audit the platform directly will need to look elsewhere — an open-source, self-hostable alternative exists for that use case.
• Both Statsig and GrowthBook support warehouse-native deployments, but GrowthBook's warehouse-native approach is its core architecture rather than an optional add-on, which matters for teams where data ownership is a primary constraint rather than a secondary preference.
• Bundled session replay and analytics give Statsig a broader native product surface than most experimentation-focused tools, which is an advantage for teams that want fewer vendor relationships — though it also means you're more deeply tied to a single proprietary platform.
• For mobile teams with strict data sovereignty requirements or a preference for open infrastructure, Statsig's cloud-first model introduces vendor dependency that a self-hosted deployment avoids entirely.

Optimizely

Primarily geared towards: Enterprise marketing, CRO, and product teams running high-volume experimentation programs across web and mobile.

Optimizely Feature Experimentation is one of the most established platforms in the experimentation space, offering A/B testing, multivariate testing, feature flags, and gradual rollouts across mobile (iOS, Android, React Native, Flutter) and web. It's a mature, full-featured SaaS platform with a long track record serving enterprise organizations.

The platform is particularly strong for teams where marketing and CRO stakeholders are driving the experimentation agenda alongside product and engineering.

Notable features:

• Mobile SDK coverage: Supports iOS, Android, React Native, and Flutter, allowing teams to run experiments across native and cross-platform mobile stacks without needing separate tooling for each environment.
• Feature flags with controlled rollouts: Enables gradual rollouts, kill switches, and server-side flag control — critical for mobile apps where app store approval cycles make it impractical to ship changes through code releases alone.
• Sequential testing and SRM checks: Optimizely's Stats Engine provides sequential testing capabilities alongside sample ratio mismatch (SRM) checks, offering some protection against peeking and data quality issues.
• Advanced user targeting: Supports targeting by user attributes such as device type, OS version, geography, or custom user cohorts, giving mobile teams fine-grained control over who sees each experiment variant.
• A/B and multivariate tests: Supports both A/B tests and multivariate tests, letting teams evaluate multiple in-app experience variants simultaneously rather than running sequential single-variable tests.
• Enterprise compliance controls: Cloud-hosted with SOC 2 and GDPR compliance, relevant for enterprise mobile teams operating under strict security and data governance requirements.

Pricing model: Optimizely uses traffic-based (MAU-based) pricing with modular add-ons, which means costs scale with experiment traffic volume. Specific pricing is not publicly listed and is typically quote-based for enterprise customers.

Starter tier: Optimizely does not offer a free tier; access requires a paid contract.

Key points:

• Optimizely is designed primarily for marketing and CRO teams, which shapes its feature set and interface — product and engineering teams running feature experimentation may find a developer-first approach a better fit for their workflows.
• Traffic-based pricing can limit experimentation velocity at scale: as traffic costs increase, teams may run fewer experiments to manage spend, which works against building a high-cadence experimentation culture.
• Optimizely is cloud-only with no self-hosting option, which may be a constraint for teams with data residency requirements or those who want full control over their infrastructure.
• Setup is characterized as a weeks-to-months process requiring a dedicated team, compared to platforms designed for faster onboarding — worth factoring in if you're trying to get a mobile experimentation program off the ground quickly.
• The platform does not expose the SQL or statistical formulas used to calculate experiment results. If your data team wants to verify the numbers independently — by re-running the analysis in Python or pulling raw data into a notebook — that's not straightforward to do here.

LaunchDarkly

Primarily geared towards: Enterprise engineering and DevOps teams focused on safe feature releases and progressive rollouts.

LaunchDarkly is a feature flag and release management platform built around operational control — giving teams the ability to roll out features gradually, trigger kill switches instantly, and manage flag lifecycles across large engineering organizations. While it does offer A/B experimentation capabilities, that's not where the platform leads.

LaunchDarkly is purpose-built for release safety first, with experimentation as a secondary layer bolted on top.

Notable features:

• Progressive rollouts: Incrementally expose a feature to a growing percentage of mobile users — reducing the risk of a bad release hitting your entire user base before you can react.
• Instant rollback without redeployment: LaunchDarkly supports automated rollback via flag toggles, which is especially valuable for mobile apps where pushing a hotfix through app store review can take days. The platform claims sub-200ms flag updates at scale.
• Experimentation add-on: LaunchDarkly does support A/B testing with Bayesian, frequentist, and sequential statistical methods, including CUPED. However, this is sold as a paid add-on rather than included in base plans.
• Targeting and segmentation: Rules-based targeting lets teams gate features by device type, OS version, user cohort, or other attributes — useful for staged mobile rollouts across a fragmented device landscape.
• Enterprise governance tooling: Audit logs, flag lifecycle management, and role-based controls make LaunchDarkly a strong fit for regulated industries or large organizations with strict compliance requirements.
• Broad SDK support: LaunchDarkly offers SDKs across a wide range of platforms, including mobile. LaunchDarkly's own site lists 35+ native SDKs, though counts may vary depending on how server-side, client-side, and mobile SDKs are categorized — verify current mobile platform coverage in their documentation.

Pricing model: LaunchDarkly's pricing is based on Monthly Active Users (MAUs), seats, and service connections, with experimentation available as a paid add-on on top of base plan costs. Exact tier pricing is not published transparently — check LaunchDarkly's pricing page directly for current figures.

Starter tier: LaunchDarkly offers a free trial to get started, but specific limits on seats, MAUs, or trial duration are not publicly confirmed — verify on their pricing page before committing.

Key points:

• LaunchDarkly's experimentation engine is a paid add-on, not included by default — teams that want both feature flags and rigorous A/B testing will pay for both separately, whereas platforms like GrowthBook include experimentation natively across all plans.
• The platform is cloud-only with no self-hosting option, which limits flexibility for teams with data residency requirements or those who want to keep experiment data within their own infrastructure.
• Warehouse-native experimentation support should be verified directly with LaunchDarkly for current data warehouse compatibility — publicly available documentation does not confirm support beyond Snowflake as of this writing, compared to GrowthBook's support for all major data warehouses including BigQuery, Redshift, and Postgres.
• MAU-based pricing can become unpredictable at scale — cost grows directly with your user base, which creates budget uncertainty for high-growth mobile apps.
• LaunchDarkly's statistical engine has been described as a black box — the platform shows you a result, but you can't easily pull the underlying data and re-run the analysis yourself to check whether the numbers hold up. For data teams that want to verify results in SQL or Python before acting on them, this is a real limitation.

Amplitude Experiment

Primarily geared towards: Product and growth teams already using Amplitude Analytics who want to add experimentation without adopting a separate tool.

Amplitude Experiment is an A/B testing and feature experimentation platform built natively into the Amplitude product analytics suite. The core value proposition is straightforward: because experiment results are measured using the same behavioral events and metrics already defined in Amplitude Analytics, teams avoid the data reconciliation headaches that come from stitching together separate testing and analytics tools.

For mobile teams, this is meaningful — conflicting numbers between an experiment platform and an analytics platform are a common source of wasted time and misread results.

Notable features:

• Unified analytics and experimentation: Experiment results pull directly from Amplitude Analytics, so the metrics you're testing against are the same ones your team already tracks — no separate event mapping or data pipeline required.
• Behavioral segmentation: Teams can target experiments at users based on what they've actually done in the app, not just device type or demographics. This reduces noise and increases the relevance of experiment results for mobile use cases.
• Feature flags and controlled rollouts: Gradual rollouts via feature flags help manage the risk inherent in mobile releases, where app store review cycles mean you can't push a quick fix if something goes wrong.
• Multiple experiment types: The platform supports A/B/n tests, multivariate experiments, and holdout groups. Holdout groups are particularly useful for mobile teams measuring long-term retention effects from feature rollouts.
• Data warehouse connectivity: Amplitude Experiment can connect to external data warehouses, allowing teams to align experiment outcomes with business metrics that live outside the Amplitude platform.
• Cross-platform metric consistency: Experiment metrics stay consistent across iOS, Android, and web surfaces, which matters for teams running experiments simultaneously across multiple platforms.

Pricing model: Amplitude uses a consumption-based pricing model tied to event volume, but specific pricing tiers for Amplitude Experiment are not publicly detailed — you'll need to contact Amplitude directly or check amplitude.com/pricing for current figures.

Starter tier: Amplitude offers a free Starter plan for its analytics product, but whether Amplitude Experiment features are meaningfully included in that tier is unconfirmed — verify directly with Amplitude before assuming free access to experimentation features.

Key points:

• Amplitude Experiment's value is tightly coupled to Amplitude Analytics — teams not already on the analytics platform would need to pay for both layers, which can make the combined cost significantly higher than open-source or free ecosystem-bundled alternatives.
• The platform is proprietary and closed-source, meaning your experiment data and metric definitions live inside Amplitude's ecosystem. Teams that want full data ownership or the ability to query results from their own warehouse without vendor dependency will find this limiting.
• Statistical methodology details — such as whether the platform supports Bayesian (probability-based), Frequentist (p-value-based), or Sequential testing (valid early stopping) — are not clearly documented in public-facing materials. This matters because the method determines how confident you can be in a result and whether you can stop an experiment early without inflating your error rate. Teams with specific statistical requirements should verify this directly before committing.
• For teams already embedded in the Amplitude ecosystem, the unified approach genuinely reduces tooling complexity. For teams that aren't, the switching cost and platform dependency are real considerations worth weighing against the integration benefits.

Apptimize

Primarily geared towards: Mobile-first product and engineering teams running experiments exclusively within iOS, Android, and React Native apps.

Apptimize is a proprietary A/B testing and feature management platform built specifically for mobile apps — not a general-purpose tool that added mobile support as an afterthought. Its entire feature set is oriented around in-app experimentation on native and cross-platform mobile surfaces.

Teams that have chosen Apptimize typically want a dedicated mobile vendor with deep SDK expertise rather than a broader platform that spans web, server-side, and mobile under one roof.

Notable features:

• Mobile-native SDK support: Provides native SDKs for iOS, Android, and React Native, designed specifically for mobile app environments rather than adapted from a web-first architecture.
• In-app UX experimentation: Focused on testing UI elements, user flows, and in-app experiences directly within the mobile app — the core use case the platform was built around.
• Mobile-only specialization: Unlike multi-platform tools, Apptimize's support, documentation, and product roadmap are entirely focused on mobile — which can be an advantage for teams whose product lives entirely within an app.
• A/B and multivariate testing: Supports standard A/B tests and multivariate experiments across mobile app surfaces, allowing teams to compare multiple variants simultaneously.
• Feature flags for mobile: Positioned as a feature management platform in addition to an experimentation tool, enabling controlled rollouts and flag-based releases within mobile apps.

Pricing model: Apptimize is a commercial, proprietary platform. Specific pricing tiers and price points are not publicly listed and require direct contact with their sales team.

Starter tier: No confirmed free tier — teams should contact Apptimize directly to understand entry-level options.

Key points:

• Apptimize's mobile-only focus is its clearest differentiator, but it also means teams running experiments across web and mobile will need a separate solution for non-mobile surfaces — a unified platform covering web, mobile, server-side, and edge avoids that fragmentation.
• Pricing is not publicly disclosed, which makes it harder to evaluate fit without a sales conversation; open-source alternatives offer a free tier with transparent per-seat pricing on paid plans.
• Apptimize is a closed, proprietary SaaS product with no self-hosting option confirmed; open-source, self-hostable options give teams complete control over where their data lives and how the platform is deployed.
• Teams with strict data ownership or warehouse-native requirements should note that Apptimize's data handling and analytics integration depth are not publicly documented — warehouse-native experimentation keeps all experiment data in your existing data warehouse, with no PII leaving your infrastructure.

AB Tasty

Primarily geared towards: Marketing and CRO teams running client-side experimentation without heavy engineering involvement.

AB Tasty is a cloud-based experimentation and personalization platform built primarily for marketing and growth teams who need to run A/B tests and UX experiments without relying on engineering resources for every change. Its visual editor and point-and-click workflow make it accessible to non-technical users focused on conversion rate optimization.

The platform supports both web and mobile contexts, though its depth on the mobile side — particularly for server-side or full-stack experimentation — is more limited than developer-first tools.

Notable features:

• Visual editor: Allows marketers to create and launch A/B tests by making front-end changes directly in a visual interface, reducing dependency on engineering for client-side experiments.
• Bayesian statistical engine: Uses a Bayesian approach for experiment analysis. This is the only supported statistical framework — Sequential testing and Frequentist methods are not available, which may be a constraint for teams with specific statistical requirements.
• Server-side experimentation: Some server-side capability exists, but it is limited compared to developer-first platforms and may require add-on or flagship products to access fully.
• A/B and multivariate testing: Supports standard A/B tests and multivariate tests across web and mobile experiences, covering the core use cases for CRO-focused teams.
• Personalization capabilities: Includes audience segmentation and targeting tools that let teams deliver tailored experiences to specific user segments, useful for mobile engagement and retention efforts.

Pricing model: AB Tasty uses a custom pricing model with no publicly listed tiers or prices. Costs are negotiated directly and can increase meaningfully as usage scales, with some key capabilities requiring additional paid products.

Starter tier: There is no free tier or trial plan — AB Tasty requires a paid contract to get started.

Key points:

• AB Tasty is designed for marketing teams, not engineering or product teams. Teams that need server-side mobile experimentation, full-stack coverage, or deep statistical controls will find the platform's capabilities limited relative to developer-first alternatives.
• The Bayesian-only statistical engine means teams cannot switch to Frequentist or Sequential testing if their methodology requirements change — a real constraint for organizations that want flexibility as their experimentation program matures.
• There is no self-hosting option and no warehouse-native architecture — experiment data is stored in AB Tasty's vendor-managed infrastructure, which limits transparency and makes independent result auditing difficult.
• Custom pricing with no public tiers makes it hard to evaluate cost without a sales conversation, and teams have reported that costs increase meaningfully as usage grows, with key capabilities gated behind additional paid products.

Pricing structure and data ownership matter more than feature matrices

When evaluating the best A/B testing tools for mobile apps, most comparison guides lead with feature checklists. The more useful lens is the two constraints that actually determine whether a tool works for your team long-term: how it prices, and who owns the data.

The real constraint is usually pricing model or data ownership, not features

Every tool in this guide supports A/B testing on iOS and Android. Most support React Native. The feature overlap is real. What diverges sharply is the cost structure as you scale and what happens to your experiment data.

Pricing model tradeoffs by team type:

• MAU-based pricing (LaunchDarkly, Optimizely): Cost scales directly with your user base. For high-growth mobile apps, this creates budget unpredictability. Teams often respond by running fewer experiments to control spend — which defeats the purpose of building an experimentation culture.
• Event-based pricing (Amplitude): Similar dynamic. As experiment volume increases, so does your bill. Teams on event-based plans frequently self-limit the number of metrics they track per experiment to avoid cost overruns.
• Per-seat pricing (GrowthBook): Cost is tied to the number of people using the platform, not to traffic or event volume. Teams can run unlimited experiments across their full user base without pricing pressure.
• Free ecosystem-bundled tools (Firebase A/B Testing): Zero marginal cost if you're already on the stack. The real cost is capability ceiling — you'll eventually outgrow the statistical depth and data portability.

Data ownership tradeoffs:

• Vendor-managed data (most SaaS tools): Your experiment results live in the vendor's infrastructure. You can export reports, but you can't query the raw data directly, audit the SQL, or combine experiment outcomes with other business metrics in your own warehouse without building a separate pipeline.
• Warehouse-native (GrowthBook, Statsig warehouse-native mode): Experiment data stays in your Snowflake, BigQuery, Redshift, or Databricks instance. You query it directly, combine it with other product data, and verify every result independently. No secondary pipeline required.
• Self-hostable open source (GrowthBook): Full control over where the platform runs, not just where the data lives. Relevant for teams with strict compliance requirements, air-gapped environments, or a preference for not depending on any vendor's uptime.

Our final recommendation: when to choose GrowthBook for mobile experimentation

GrowthBook is the strongest fit for mobile teams that meet any of the following conditions:

• You already have event tracking in place (Segment, Rudderstack, GA, or a custom setup) and want to run experiments against that data without building a new pipeline
• You're running or planning to run experiments at scale, where MAU- or event-based pricing from other tools would become a meaningful budget constraint
• Your team has data residency requirements, compliance mandates, or a preference for keeping experiment data inside your own infrastructure
• You want statistical depth — Bayesian, Frequentist, Sequential testing, CUPED, and multiple comparison corrections — without paying for an enterprise tier to unlock it
• You want a single platform for feature flags and A/B testing rather than two separate tools with separate contracts and separate data models

GrowthBook is a less obvious fit if your team is entirely embedded in the Firebase/Google ecosystem and your experimentation needs are simple, or if your primary use case is marketing-led client-side CRO rather than product and feature experimentation.

Where to start depending on where your experimentation program actually is

If you're running your first mobile experiment and have no existing tooling: Start with GrowthBook's free Cloud Starter plan (up to 3 users, 1M events/month, no credit card required) or Firebase A/B Testing if you're already on Firebase. Both get you to a first experiment without a procurement process.

If you're replacing a tool that's become too expensive: Calculate your current per-experiment cost and compare it against GrowthBook's per-seat model. Teams running more than a handful of experiments per month on MAU- or event-based pricing typically find the switch pays for itself quickly.

If you're evaluating tools for an enterprise program with compliance requirements: GrowthBook's self-hosted deployment option and warehouse-native architecture are worth evaluating seriously. The open-source codebase means you can audit the statistical engine directly — something no proprietary platform offers.

If you're a data team trying to trust experiment results: The single most important question to ask any vendor is: "Can I see the SQL used to calculate this result?" GrowthBook exposes it. Most tools don't. That transparency matters when you're making product decisions based on experiment outcomes.

To get started with GrowthBook's free plan, visit growthbook.io/get-started. Documentation for mobile SDK integration — including iOS (Swift), Android (Kotlin), React Native, and Flutter — is available at docs.growthbook.io.