An alternative to Split's Measurement Engine: Statsig

Feature flag platforms promise controlled releases and safer deployments. But when teams need to measure the actual impact of their changes, most platforms fall short. Split built their platform around feature management, later adding basic experimentation capabilities.

Statsig took the opposite approach - building measurement into the core from day one. This fundamental difference shapes everything from pricing models to technical architecture, and it's why companies like OpenAI and Figma choose Statsig when they need serious experimentation infrastructure.

Company backgrounds and platform overview

Statsig emerged in 2020 when ex-Facebook VP Vijaye Raji assembled a small team to recreate Facebook's experimentation infrastructure. The goal was simple: make enterprise-grade testing tools accessible to every company. After eight months without customers, they gained traction through former colleagues who understood the platform's power.

Split.io started with feature management at its core. Their platform helps teams deploy code safely through feature flags and phased rollouts. The experimentation features came later - an add-on rather than a foundation. This architectural choice impacts everything from pricing to performance.

The platforms serve fundamentally different needs. Split excels at controlled feature releases and deployment risk management. But when SoundCloud needed comprehensive measurement capabilities, they chose Statsig: "We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration," said Don Browning, SVP at SoundCloud.

Statsig's infrastructure reflects its measurement-first approach. The platform processes over 1 trillion events daily while maintaining sub-millisecond evaluation latency. Split's management console centers on feature flag workflows, with measurement capabilities bolted on afterward.

Feature and capability deep dive

Experimentation capabilities

The statistical foundation separates basic A/B testing from enterprise experimentation. Here's what actually matters:

• CUPED variance reduction: Cut experiment runtime by 50% while maintaining statistical rigor

• Sequential testing: Peek at results safely without inflating false positive rates

• Bayesian and Frequentist methods: Choose the right approach for your use case

Split provides standard A/B testing with basic statistical significance calculations. You get p-values and confidence intervals - the bare minimum for making decisions. But when you're testing changes that affect millions of users and thousands of dollars in revenue, basic isn't enough.

Paul Ellwood from OpenAI explains why advanced capabilities matter: "Statsig's experimentation capabilities stand apart from other platforms we've evaluated. Statsig's infrastructure and experimentation workflows have been crucial in helping us scale to hundreds of experiments across hundreds of millions of users."

The integration story reveals another key difference. Split requires separate analytics tools to understand experiment results deeply. You'll export data, wrestle with CSV files, and analyze results in external tools. Statsig bundles product analytics, session replay, and experimentation in one platform. No data silos. No integration headaches.

Developer experience and infrastructure

Both platforms offer 30+ SDKs across major languages and frameworks. But SDK count tells only part of the story.

Statsig uniquely provides warehouse-native deployment alongside traditional cloud hosting. Your data stays in Snowflake, BigQuery, or Databricks while you run experiments. This matters for two reasons: compliance teams sleep better at night, and you maintain complete control over your data infrastructure.

Infrastructure scale reveals platform maturity:

• Statsig: Processes over 1 trillion events daily with 99.99% uptime

• Split: Infrastructure specifications remain undisclosed in their product overview

This scale difference isn't academic. During Black Friday traffic spikes or viral product launches, infrastructure limitations become painfully obvious. Sub-millisecond evaluation latency at scale requires serious engineering investment.

Pricing models and cost analysis

The pricing structures reveal fundamental philosophy differences. Split follows traditional SaaS pricing: per-seat charges plus fees for feature flag evaluations. Every developer costs money. Every flag check costs money.

Statsig flips this model. You pay only for analytics events while feature flags remain completely free. This approach encourages experimentation - teams don't worry about running up the bill by testing more ideas.

Real-world cost scenarios

Let's get specific about costs. For a company with 100,000 monthly active users:

• Split: Often exceeds $50,000 annually with basic experimentation features

• Statsig: Typically costs 50-70% less than Split for equivalent functionality

The free tier comparison highlights this difference starkly. Statsig includes 50,000 session replays, unlimited feature flags, and full experimentation capabilities at no cost. Split's free tier restricts you to developer-only features with severe limitations on core functionality.

Enterprise contracts reveal even larger gaps. Split's enterprise pricing often exceeds $100,000 annually, with complex negotiations around seats and usage tiers. Statsig offers transparent volume-based pricing - discounts start at 200,000 MAU with no hidden fees.

But direct costs tell only half the story. Brex achieved a 50% reduction in time spent by data scientists after switching platforms. When your data team spends less time wrestling with tools, they deliver more insights that drive business value.

Decision factors and implementation considerations

Implementation complexity and time-to-value

Getting experiments running quickly matters more than feature checklists. Notion scaled from single-digit to 300+ experiments within months of implementing Statsig. The platform's design enables this velocity: SDKs install in hours, not weeks.

Wendy Jiao, Software Engineer at Notion, puts it simply: "Statsig enabled us to ship at an impressive pace with confidence."

Split's documentation focuses heavily on feature flag configuration. Setting up comprehensive experimentation requires additional work: configuring analytics integrations, building custom dashboards, and training teams on multiple tools.

Support and enterprise readiness

Support quality directly impacts success. G2 reviews consistently highlight Statsig's responsive Slack support. Sometimes the CEO answers questions directly - that level of engagement accelerates problem resolution.

Enterprise requirements go beyond basic support:

• Data governance: Warehouse-native deployment keeps sensitive data under your control

• Compliance: SOC 2 Type II, GDPR, and HIPAA compliance out of the box

• Scale: Handle traffic spikes without performance degradation

Split lacks warehouse-native deployment capabilities. For enterprises with strict data residency requirements, this limitation becomes a dealbreaker. You're forced to choose between experimentation capabilities and compliance requirements.

The implementation experience differs dramatically between platforms. Brex reduced their data science workload by 50% after switching to Statsig. Sumeet Marwaha from Brex notes: "Our engineers are significantly happier using Statsig. They no longer deal with uncertainty and debugging frustrations."

Bottom line: why is Statsig a viable alternative to Split?

Statsig combines experimentation, feature flags, analytics, and session replay in one integrated platform. This isn't just convenient - it fundamentally changes how teams work. No more data export headaches. No more reconciling metrics across tools. Everything works together seamlessly.

The technical capabilities match enterprise needs. Processing trillions of events daily for companies like OpenAI and Figma proves the platform can handle serious scale. You get Facebook-grade infrastructure without enterprise pricing barriers.

Cost efficiency extends beyond sticker price. While Statsig typically costs 50-70% less than Split's complex pricing tiers, the real savings come from operational efficiency. Teams ship faster, make better decisions, and spend less time managing tools.

Real business impact validates the platform choice. SoundCloud reached profitability for the first time in 16 years using Statsig's experimentation capabilities. This isn't just about feature flags - it's about understanding what actually moves business metrics.

The warehouse-native deployment option provides a unique advantage for enterprise teams. Run experiments directly in Snowflake, BigQuery, or Databricks while maintaining complete data control. Split simply doesn't offer this capability, forcing compromises between functionality and compliance.

Closing thoughts

Choosing between Split and Statsig comes down to your measurement needs. If you primarily need feature flags with basic A/B testing, Split works fine. But when measurement drives your product decisions - when you need variance reduction, sequential testing, and integrated analytics - Statsig provides the depth that Split's measurement engine lacks.

The companies succeeding with experimentation at scale have made their choice clear. From OpenAI's hundreds of concurrent experiments to SoundCloud's path to profitability, the results speak for themselves.

For teams ready to level up their experimentation practice, check out Statsig's migration guides and their experimentation best practices. The platform offers a generous free tier that lets you test the full capabilities before committing.

Hope you find this useful!