Statsig
The Takeaway
Statsig grows by making experimentation frictionless for engineers — free tier + integrated platform locks in early-stage teams before they can fragment into point solutions.
Company Research
Statsig is a modern product development platform that helps software engineering and product teams test, analyze, and roll out new features through integrated experimentation, feature management, and product analytics [13]
Founded: 2021 [2]
Founders: Vijaye Raji (CEO, formerly of Facebook) [2]
Employees: Approximately 50-100 employees as of 2024 [4]
Headquarters: Seattle, Washington, USA [5]
Funding/Valuation: Series C — raised $100 million at a $1.1 billion valuation (May 2025); previously raised $43 million Series B led by Sequoia Capital at $420 million valuation [2][4]
Mission: Statsig's mission is to help product and engineering teams ship better software faster by turning every release into a data-driven experiment [13]
The company's strengths rely on the combination of an all-in-one integrated platform (experimentation + feature flags + analytics in a single product), a transparent usage-based pricing model with a generous free tier, and deep roots in enterprise-grade experimentation methodology built by ex-Facebook engineers. [2][13][6]
• All-in-one integrated platform: Statsig bundles feature flags, A/B testing, product analytics, and session replays into a single platform, eliminating the need for multiple point solutions and providing end-to-end visibility from release to result [13]
• Usage-based, transparent pricing with a free tier: Statsig offers free, unlimited feature flags and a generous free tier, making it accessible to startups and small teams while scaling to enterprise needs — a stark contrast to competitors like LaunchDarkly that charge by monthly active users [6][20]
• Enterprise-grade stats engine from ex-Meta engineers: Founded by a former Facebook engineering leader, Statsig's experimentation methodology and stats engine are built on battle-tested practices used at hyperscale consumer tech companies, giving it credibility with sophisticated engineering teams [2][7]
• Usage-based, transparent pricing with a free tier: Statsig offers free, unlimited feature flags and a generous free tier, making it accessible to startups and small teams while scaling to enterprise needs — a stark contrast to competitors like LaunchDarkly that charge by monthly active users [6][20]
• Enterprise-grade stats engine from ex-Meta engineers: Founded by a former Facebook engineering leader, Statsig's experimentation methodology and stats engine are built on battle-tested practices used at hyperscale consumer tech companies, giving it credibility with sophisticated engineering teams [2][7]
Business Model Analysis
🚨Problem
Software teams struggle to safely ship new features and measure their real impact without fragmented, expensive tooling that slows down product iteration [13]
• Product and engineering teams lack a unified way to control feature rollouts, run statistically valid experiments, and analyze results — forcing them to stitch together multiple separate tools [13]
• Traditional experimentation platforms are prohibitively expensive at scale; for example, LaunchDarkly charges per monthly active user, which becomes cost-prohibitive once teams hit millions of sessions [20]
• Without proper experimentation infrastructure, teams ship features blindly, unable to measure whether changes actually improve key product metrics [7]
• Many companies rely on homegrown internal tooling for A/B testing that is costly to build and maintain, leaving smaller teams without access to rigorous experimentation [8]
• Disconnected analytics and feature management tools create data silos, slowing down the feedback loop between engineering releases and product outcomes [13]
• Traditional experimentation platforms are prohibitively expensive at scale; for example, LaunchDarkly charges per monthly active user, which becomes cost-prohibitive once teams hit millions of sessions [20]
• Without proper experimentation infrastructure, teams ship features blindly, unable to measure whether changes actually improve key product metrics [7]
• Many companies rely on homegrown internal tooling for A/B testing that is costly to build and maintain, leaving smaller teams without access to rigorous experimentation [8]
• Disconnected analytics and feature management tools create data silos, slowing down the feedback loop between engineering releases and product outcomes [13]
💡Solution
Statsig provides an integrated product development platform combining feature flags, A/B experimentation, product analytics, and session replays in a single toolset [13]
• Feature flags allow engineering teams to control which users see new features, enabling safe, gradual rollouts and instant kill-switch capability without a new deployment [13]
• A/B testing and multivariate experimentation with a world-class stats engine help teams run statistically valid experiments to measure the impact of every product change [7]
• Built-in product analytics allow teams to define and track a complete set of product metrics without needing a separate analytics tool, closing the loop between experiment and outcome [13]
• Session replays (50,000 free monthly) provide qualitative context to quantitative experiment results, helping teams understand the 'why' behind metric movements [8]
• Custom exclusion criteria and mutually exclusive experiment groups support sophisticated experiment designs needed by advanced growth and product teams [6]
• A/B testing and multivariate experimentation with a world-class stats engine help teams run statistically valid experiments to measure the impact of every product change [7]
• Built-in product analytics allow teams to define and track a complete set of product metrics without needing a separate analytics tool, closing the loop between experiment and outcome [13]
• Session replays (50,000 free monthly) provide qualitative context to quantitative experiment results, helping teams understand the 'why' behind metric movements [8]
• Custom exclusion criteria and mutually exclusive experiment groups support sophisticated experiment designs needed by advanced growth and product teams [6]
⭐Unique Value Proposition
Statsig delivers 5+ product development tools in a single integrated platform with transparent, usage-based pricing — giving teams the experimentation sophistication of a hyperscale tech company without the cost or complexity of assembling multiple point solutions [13][6]
• End-to-end integration means feature flags, experiments, analytics, and replays all share the same data model — something competitors like LaunchDarkly and Optimizely do not offer in a single platform [11]
• Free, unlimited feature flags and a generous free tier lower the barrier to entry dramatically compared to incumbents, allowing teams of any size to adopt enterprise-grade experimentation [6][8]
• The platform's stats engine and methodology are derived from practices used at Facebook/Meta at hyperscale, providing scientific rigor that self-built or simpler tools cannot match [2][7]
• Real-time debugging panels and integrated results views accelerate the experiment analysis workflow, reducing time from ship to decision [6]
• Free, unlimited feature flags and a generous free tier lower the barrier to entry dramatically compared to incumbents, allowing teams of any size to adopt enterprise-grade experimentation [6][8]
• The platform's stats engine and methodology are derived from practices used at Facebook/Meta at hyperscale, providing scientific rigor that self-built or simpler tools cannot match [2][7]
• Real-time debugging panels and integrated results views accelerate the experiment analysis workflow, reducing time from ship to decision [6]
👥Customer Segments
Statsig targets software product and engineering teams at technology companies ranging from Series A startups to large enterprises, spanning SaaS, e-commerce, mobile apps, and AI-native products [14][15]
• High-growth technology startups (Series A and above) looking to establish a rigorous experimentation culture early without building internal tooling [4][15]
• Mid-market and enterprise SaaS companies that need scalable feature management and experimentation infrastructure to support large engineering organizations [14][17]
• E-commerce and consumer internet companies where rapid A/B testing of UI, pricing, and recommendation systems directly drives revenue [14]
• Mobile app developers who need feature flags for gradual rollouts and crash protection across diverse device and OS environments [14]
• AI-native companies (including OpenAI) that need to test and optimize AI-powered product experiences and model outputs at scale [15][5]
• Mid-market and enterprise SaaS companies that need scalable feature management and experimentation infrastructure to support large engineering organizations [14][17]
• E-commerce and consumer internet companies where rapid A/B testing of UI, pricing, and recommendation systems directly drives revenue [14]
• Mobile app developers who need feature flags for gradual rollouts and crash protection across diverse device and OS environments [14]
• AI-native companies (including OpenAI) that need to test and optimize AI-powered product experiences and model outputs at scale [15][5]
🏢Existing Alternatives
Statsig competes in a fragmented market where feature management and experimentation tools have historically been separate products dominated by LaunchDarkly, Optimizely, and Split [10][11]
• LaunchDarkly: The incumbent feature management platform with the most mature governance workflows and enterprise-grade reliability, but uses a per-MAU pricing model that users find expensive at scale [10][20]
• Optimizely: A leading experimentation and A/B testing platform, evaluated head-to-head against Statsig by enterprise teams, but lacking the integrated feature flag + analytics bundle [11]
• Split.io (now Harness): A feature flag and experimentation platform, considered a direct alternative by teams evaluating modern experimentation tools [12]
• Eppo: An emerging experimentation-focused competitor mentioned alongside Statsig in the context of Datadog's failed acquisition attempt, targeting data-forward product teams [2]
• Flagsmith and other open-source feature flag tools: Serve cost-sensitive teams but lack the advanced stats engine and analytics depth that Statsig provides [10]
• Optimizely: A leading experimentation and A/B testing platform, evaluated head-to-head against Statsig by enterprise teams, but lacking the integrated feature flag + analytics bundle [11]
• Split.io (now Harness): A feature flag and experimentation platform, considered a direct alternative by teams evaluating modern experimentation tools [12]
• Eppo: An emerging experimentation-focused competitor mentioned alongside Statsig in the context of Datadog's failed acquisition attempt, targeting data-forward product teams [2]
• Flagsmith and other open-source feature flag tools: Serve cost-sensitive teams but lack the advanced stats engine and analytics depth that Statsig provides [10]
📊Key Metrics
Statsig reached a $1.1 billion valuation in May 2025 following its $100 million Series C raise, with thousands of companies using the platform ranging from OpenAI to Series A startups [2][15]
• Valuation: $1.1 billion as of May 2025 Series C funding round [2]
• Total funding raised: Over $143 million across Seed, Series A, Series B, and Series C rounds [2][4]
• Series B valuation: $420 million, representing a 10.5x increase from Series A in approximately one year [4]
• Customer base: Thousands of companies globally, including OpenAI, trusting the platform for production feature management and experimentation [15]
• Acquisition: OpenAI announced agreement to acquire Statsig for $1.1 billion in an all-stock deal as of September 2, 2025, pending regulatory review [5]
• Total funding raised: Over $143 million across Seed, Series A, Series B, and Series C rounds [2][4]
• Series B valuation: $420 million, representing a 10.5x increase from Series A in approximately one year [4]
• Customer base: Thousands of companies globally, including OpenAI, trusting the platform for production feature management and experimentation [15]
• Acquisition: OpenAI announced agreement to acquire Statsig for $1.1 billion in an all-stock deal as of September 2, 2025, pending regulatory review [5]
🎯High-Level Product Concepts
Statsig's platform bundles five core product development tools — feature flags, experimentation, product analytics, session replays, and a stats engine — into a single integrated system [13]
• Feature Flags: Enable engineering teams to progressively roll out features to targeted user segments, run canary releases, and instantly roll back without redeployment [13]
• Experimentation (A/B & Multivariate Testing): A world-class stats engine supports mutually exclusive experiment groups, custom exclusion criteria, and real-time results views to help teams make confident ship/kill decisions [6][7]
• Product Analytics: Built-in event tracking, funnel analysis, and metric definition tools so teams can measure product performance without a separate analytics platform [13]
• Session Replays: Up to 50,000 free monthly session replays provide qualitative context to quantitative experiment results [8]
• Integrated Results & Debugging: A real-time debugging panel and unified results dashboard connect feature release data to business outcomes in a single workflow [6]
• Experimentation (A/B & Multivariate Testing): A world-class stats engine supports mutually exclusive experiment groups, custom exclusion criteria, and real-time results views to help teams make confident ship/kill decisions [6][7]
• Product Analytics: Built-in event tracking, funnel analysis, and metric definition tools so teams can measure product performance without a separate analytics platform [13]
• Session Replays: Up to 50,000 free monthly session replays provide qualitative context to quantitative experiment results [8]
• Integrated Results & Debugging: A real-time debugging panel and unified results dashboard connect feature release data to business outcomes in a single workflow [6]
📢Channels
Statsig acquires customers primarily through product-led growth, developer community engagement, and direct sales to engineering and product leaders at technology companies [13][15]
• Product-led growth via a generous free tier: Free unlimited feature flags and free analytics lower the barrier to adoption, allowing individual developers and teams to self-serve and expand organically [6][8]
• Developer-focused content marketing: Detailed technical blog posts (e.g., cost comparisons of experimentation platforms, how-to guides) targeting engineers and product managers searching for tooling solutions [8]
• Customer success and case studies: A public customer showcase featuring well-known brands (e.g., OpenAI) builds social proof and drives inbound interest from similar companies [15]
• Direct enterprise sales: Outbound and inbound enterprise sales motion targeting larger engineering organizations that need custom contracts, SSO, and advanced governance [6]
• Competitive comparison landing pages: Dedicated pages comparing Statsig to LaunchDarkly, Optimizely, and Split capture high-intent search traffic from teams actively evaluating alternatives [11][20]
• Developer-focused content marketing: Detailed technical blog posts (e.g., cost comparisons of experimentation platforms, how-to guides) targeting engineers and product managers searching for tooling solutions [8]
• Customer success and case studies: A public customer showcase featuring well-known brands (e.g., OpenAI) builds social proof and drives inbound interest from similar companies [15]
• Direct enterprise sales: Outbound and inbound enterprise sales motion targeting larger engineering organizations that need custom contracts, SSO, and advanced governance [6]
• Competitive comparison landing pages: Dedicated pages comparing Statsig to LaunchDarkly, Optimizely, and Split capture high-intent search traffic from teams actively evaluating alternatives [11][20]
🚀Early Adopters
Statsig's earliest adopters were growth-minded software engineers and product managers at fast-moving technology startups who wanted Facebook/Google-caliber experimentation without building it themselves [2][4]
• Startup engineering teams (Series A to Series B stage) that had outgrown manual release processes and needed a scalable feature flagging and experimentation system but lacked the resources to build one internally [4]
• Product-led growth companies in SaaS and consumer tech where rapid iteration and data-driven decision-making are core to the business model, and where the cost of a bad release is high [7][17]
• Ex-big-tech engineers at startups who had used internal experimentation tools at companies like Facebook, Google, or Amazon and wanted equivalent capabilities at their new companies [2]
• AI-native product teams experimenting with large language model outputs and AI-powered UX features, who needed a platform capable of testing non-deterministic AI experiences [13][15]
• Product-led growth companies in SaaS and consumer tech where rapid iteration and data-driven decision-making are core to the business model, and where the cost of a bad release is high [7][17]
• Ex-big-tech engineers at startups who had used internal experimentation tools at companies like Facebook, Google, or Amazon and wanted equivalent capabilities at their new companies [2]
• AI-native product teams experimenting with large language model outputs and AI-powered UX features, who needed a platform capable of testing non-deterministic AI experiences [13][15]
💰Fees
Statsig uses a usage-based pricing model with a free tier and paid plans scaling by event volume and feature usage, offering significantly more value per dollar than per-MAU competitors [6][8]
• Free tier: Includes product analytics, feature flags (unlimited), A/B experimentation, and 50,000 free monthly session replays — no credit card required [8]
• Pro/Team tier: Paid plans scale based on event volume and usage, with pricing publicly available; specific per-unit pricing depends on data volume and product modules activated [6]
• Enterprise tier: Custom pricing for large organizations requiring SSO, advanced governance, dedicated support, and custom data retention [6]
• No per-seat or per-MAU charges for feature flags: Unlike LaunchDarkly, Statsig does not charge per monthly active user for feature flag evaluations, making it dramatically cheaper at scale for high-traffic applications [20]
• G2 lists Statsig's pricing editions starting from $0, with paid tiers available; exact enterprise pricing requires contacting sales [9]
• Pro/Team tier: Paid plans scale based on event volume and usage, with pricing publicly available; specific per-unit pricing depends on data volume and product modules activated [6]
• Enterprise tier: Custom pricing for large organizations requiring SSO, advanced governance, dedicated support, and custom data retention [6]
• No per-seat or per-MAU charges for feature flags: Unlike LaunchDarkly, Statsig does not charge per monthly active user for feature flag evaluations, making it dramatically cheaper at scale for high-traffic applications [20]
• G2 lists Statsig's pricing editions starting from $0, with paid tiers available; exact enterprise pricing requires contacting sales [9]
💵Revenue
Statsig generates revenue primarily through usage-based SaaS subscriptions, with expansion revenue driven by data volume growth as customer products scale [6][17]
• Usage-based subscription revenue: Customers pay based on the volume of events, feature flag evaluations, and experiment exposures processed through the platform, creating a natural land-and-expand dynamic [6][8]
• Enterprise contract revenue: Larger organizations sign annual enterprise contracts for advanced features including SSO, priority support, custom data retention, and dedicated infrastructure [6]
• Expansion revenue: As customers' products grow and generate more events and users, their Statsig usage and spend naturally expands without requiring additional seats to be purchased [17]
• No specific revenue figures have been publicly disclosed; the company raised $100 million at a $1.1 billion valuation in May 2025 and was subsequently agreed to be acquired by OpenAI for $1.1 billion [2][5]
• Enterprise contract revenue: Larger organizations sign annual enterprise contracts for advanced features including SSO, priority support, custom data retention, and dedicated infrastructure [6]
• Expansion revenue: As customers' products grow and generate more events and users, their Statsig usage and spend naturally expands without requiring additional seats to be purchased [17]
• No specific revenue figures have been publicly disclosed; the company raised $100 million at a $1.1 billion valuation in May 2025 and was subsequently agreed to be acquired by OpenAI for $1.1 billion [2][5]
📅History
Statsig was founded in 2021 by Vijaye Raji, a former Facebook engineering executive, and grew from a seed-stage startup to a $1.1 billion unicorn acquired by OpenAI in under five years [2][5]
• 2021: Vijaye Raji founded Statsig in Seattle after leaving Facebook, where he had led engineering teams building internal experimentation and product infrastructure [2]
• 2022: Statsig raised its Series A funding round, establishing early product-market fit with technology startups seeking Facebook-caliber experimentation tools [4]
• 2023: Statsig raised a $43 million Series B led by Sequoia Capital with participation from Madrona Venture Group, reaching a $420 million valuation — a 10.5x increase from Series A [4]
• 2024: Datadog reportedly attempted to acquire Statsig but the deal was abandoned, signaling the platform's strategic value to larger infrastructure companies [2]
• May 2025: Statsig raised $100 million in a Series C round at a $1.1 billion valuation, achieving unicorn status [2]
• September 2, 2025: OpenAI announced its agreement to acquire Statsig for $1.1 billion in an all-stock deal, one of the largest acquisitions in OpenAI's history; Statsig to continue operating in Seattle pending regulatory review [5]
• 2022: Statsig raised its Series A funding round, establishing early product-market fit with technology startups seeking Facebook-caliber experimentation tools [4]
• 2023: Statsig raised a $43 million Series B led by Sequoia Capital with participation from Madrona Venture Group, reaching a $420 million valuation — a 10.5x increase from Series A [4]
• 2024: Datadog reportedly attempted to acquire Statsig but the deal was abandoned, signaling the platform's strategic value to larger infrastructure companies [2]
• May 2025: Statsig raised $100 million in a Series C round at a $1.1 billion valuation, achieving unicorn status [2]
• September 2, 2025: OpenAI announced its agreement to acquire Statsig for $1.1 billion in an all-stock deal, one of the largest acquisitions in OpenAI's history; Statsig to continue operating in Seattle pending regulatory review [5]
🤝Recent Big Deals
Statsig's most significant recent development is its announced $1.1 billion acquisition by OpenAI, following a failed acquisition attempt by Datadog and a successful $100 million Series C [2][5]
• September 2025 — OpenAI acquisition agreement: OpenAI announced an agreement to acquire Statsig for $1.1 billion in an all-stock deal, marking one of the largest acquisitions in OpenAI's history; the company will continue to operate from Seattle pending regulatory approval [5]
• May 2025 — $100 million Series C: Statsig closed a $100 million Series C funding round at a $1.1 billion valuation, just months before the OpenAI acquisition announcement [2]
• 2024 — Abandoned Datadog acquisition: Datadog pursued but ultimately abandoned an acquisition of Statsig, highlighting the platform's strategic attractiveness to major infrastructure software companies [2]
• Ongoing — OpenAI as flagship customer: OpenAI's use of Statsig as a production experimentation and feature management platform preceded the acquisition, validating the product's capability for AI-native use cases at scale [15]
• May 2025 — $100 million Series C: Statsig closed a $100 million Series C funding round at a $1.1 billion valuation, just months before the OpenAI acquisition announcement [2]
• 2024 — Abandoned Datadog acquisition: Datadog pursued but ultimately abandoned an acquisition of Statsig, highlighting the platform's strategic attractiveness to major infrastructure software companies [2]
• Ongoing — OpenAI as flagship customer: OpenAI's use of Statsig as a production experimentation and feature management platform preceded the acquisition, validating the product's capability for AI-native use cases at scale [15]
ℹ️Other Important Factors
The pending OpenAI acquisition introduces significant strategic uncertainty for existing Statsig customers and the broader competitive landscape in the experimentation and feature management market [5]
• OpenAI acquisition implications: The all-stock acquisition at $1.1 billion pending regulatory review may affect Statsig's independent product roadmap, pricing, and availability to non-OpenAI customers — a key concern for enterprise clients who chose Statsig as a neutral vendor [5]
• Competitive positioning against LaunchDarkly: Statsig has aggressively positioned itself as an all-in-one, more affordable alternative to LaunchDarkly, including dedicated competitive comparison pages; however, LaunchDarkly's users note Statsig lacks some production control features that enterprise governance teams require [18][20]
• Market trend — AI-native experimentation: The growing demand for testing AI-powered product experiences (e.g., LLM outputs, recommendation systems) represents a major emerging use case that differentiates Statsig from traditional A/B testing incumbents focused on UI changes [13][17]
• Trunk-Based Development compatibility: Statsig's feature flag architecture is particularly well-suited for teams using trunk-based development workflows, a modern engineering practice that requires robust feature flag infrastructure to manage work-in-progress code safely in production [20]
• Competitive positioning against LaunchDarkly: Statsig has aggressively positioned itself as an all-in-one, more affordable alternative to LaunchDarkly, including dedicated competitive comparison pages; however, LaunchDarkly's users note Statsig lacks some production control features that enterprise governance teams require [18][20]
• Market trend — AI-native experimentation: The growing demand for testing AI-powered product experiences (e.g., LLM outputs, recommendation systems) represents a major emerging use case that differentiates Statsig from traditional A/B testing incumbents focused on UI changes [13][17]
• Trunk-Based Development compatibility: Statsig's feature flag architecture is particularly well-suited for teams using trunk-based development workflows, a modern engineering practice that requires robust feature flag infrastructure to manage work-in-progress code safely in production [20]
References
- [1] Statsig - 2026 Company Profile, Team, Funding & Competitors - Tracxn — https://tracxn.com/d/companies/statsig/__5z5k9oxSV8bOviIww7mXeykFxBZR4VS_eLqMdsaNTF0
- [2] Exclusive: Statsig raises $100 million at $1.1 billion valuation after abandoned Datadog acquisition attempt | Fortune — https://fortune.com/2025/05/06/statsig-series-c-100-million-1-1-billion-eppo-datadog/
- [3] Statsig - Crunchbase Company Profile & Funding — https://www.crunchbase.com/organization/statsig
- [4] Early startup journey: My first year at Statsig — https://www.statsig.com/blog/early-startup-journey-my-first-year-at-statsig
- [5] Statsig revenue, funding & news | Sacra — https://sacra.com/c/statsig/
- [6] Statsig | The modern product development platform — https://www.statsig.com/pricing
- [7] Statsig | The world's leading experimentation platform — https://statsig.com/experimentation
- [8] How much does an experimentation platform cost? — https://www.statsig.com/blog/how-much-does-an-experimentation-platform-cost
- [9] Statsig Pricing 2026 — https://www.g2.com/products/statsig/pricing
- [10] Statsig Alternatives: 8 Best Feature Flag Platforms Compared - Flagsmith — https://www.flagsmith.com/blog/statsig-alternatives
- [11] Statsig vs. LaunchDarkly — https://www.statsig.com/vs/launchdarkly
- [12] Split Alternatives for Feature Flag Management and Experimentation | LaunchDarkly — https://launchdarkly.com/blog/compare-split-alternatives/
- [13] Statsig | The modern product development platform — https://www.statsig.com/
- [14] Customer Demographics and Target Market of Statsig – CanvasBusinessModel.com — https://canvasbusinessmodel.com/blogs/target-market/statsig-target-market
- [15] Statsig is the best, say our customers — https://www.statsig.com/customers
- [16] Behavioral Segmentation in B2B SaaS: Methods and Use Cases — https://www.statsig.com/perspectives/behavioral-segmentation-b2b-saas
- [17] Statsig: The Ultimate 2025 Guide to Experimentation, Feature Flags, and Product Growth - Saral Venture Builders — https://builders.saralgroups.com/news/statsig-the-ultimate-2025-guide-to-experimentation-feature-flags-and-product-growth/
- [18] LaunchDarkly vs. Statsig | LaunchDarkly — https://launchdarkly.com/compare/launchdarkly-vs-statsig/
- [19] Compare LaunchDarkly vs. Statsig | G2 — https://www.g2.com/compare/launchdarkly-vs-statsig
- [20] An all-in-one alternative to LaunchDarkly: Statsig — https://www.statsig.com/comparison/allinone-alternative-statsig
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