An alternative to Split for smaller teams: Statsig

Feature flags and experimentation platforms have become essential for modern product teams, but enterprise pricing often locks out smaller companies. Split built its reputation serving large organizations with mature feature flag needs - teams that can justify spending thousands per month on deployment safety and basic A/B testing.

But what if you're a 50-person startup that needs experimentation infrastructure without enterprise costs? This is where the market has failed smaller teams. They're stuck choosing between expensive platforms designed for Fortune 500 companies or cobbling together multiple tools that create data silos and integration headaches.

Company backgrounds and platform overview

Split emerged as an enterprise feature flag solution. The company built its platform around safe deployment practices - giving teams controlled rollouts and instant rollback capabilities. Most Split customers follow a predictable path: they start with feature management to reduce deployment risk, then bolt on experimentation capabilities months or years later.

Statsig took a radically different approach. Former Facebook VP Vijaye Raji and his team spent eight months building without taking a single customer call. They weren't interested in incremental improvements to existing tools. Instead, they recreated Facebook's internal experimentation infrastructure - the same system that handles trillions of events daily at Meta. The goal was simple: bring hyperscale experimentation capabilities to companies that don't have 10,000 engineers.

This philosophical difference shapes everything. Split's management console and API optimize for feature control and gradual rollouts. You get enterprise-grade deployment safety, but experimentation feels like an afterthought. Statsig flips this model - feature flags and experimentation share the same infrastructure from day one. Companies like OpenAI and Notion chose this integrated approach specifically because they wanted to start experimenting immediately, not six months after implementing feature flags.

The customer profiles tell the story. Split serves enterprises with 100-10,000 employees, particularly in financial services and SaaS sectors. These companies need compliance features, approval workflows, and enterprise support contracts. Statsig attracts a different crowd: fast-moving teams at Figma, Microsoft, and Anthropic who need Facebook-grade infrastructure without Facebook's engineering overhead. They process 200 billion events daily while maintaining sub-millisecond latency - performance that matters when you're running experiments on millions of users.

Feature and capability deep dive

Core experimentation capabilities

Split provides standard A/B testing integrated with their feature flag system. The feature management platform handles basic experiment workflows: you create variants, allocate traffic, and measure results. It works well for straightforward tests, but advanced statistical methods require external tools or custom implementation.

Statsig brings data science firepower that typically requires a dedicated team. The platform includes:

• Sequential testing that lets you peek at results without statistical penalties

• CUPED variance reduction to detect smaller effects with the same sample size

• Automated heterogeneous effect detection that surfaces how experiments impact different user segments

• Warehouse-native deployment options for teams that can't send data to third-party servers

These aren't just buzzwords - they translate to faster experiment cycles and more reliable results. A test that would take four weeks on Split might reach statistical significance in two weeks on Statsig. For smaller teams running fewer experiments, this acceleration matters.

Analytics and developer experience

Split's analytics story gets complicated quickly. Their product overview demonstrates basic metric tracking tied to feature changes. But you'll need Amplitude, Mixpanel, or custom dashboards for deeper insights. This creates the classic problem: your experimentation data lives in one system while user behavior data sits elsewhere.

Statsig bundles product analytics, session replay, and experimentation into one platform. This isn't just convenient - it fundamentally changes how teams work. Engineers can watch actual user sessions from failed experiments. Product managers can dive from experiment results into user journey analysis without switching tools. Data scientists get transparent SQL query visibility with one click, inspecting every calculation.

As Sumeet Marwaha, Head of Data at Brex, explained: "The biggest benefit is having experimentation, feature flags, and analytics in one unified platform. It removes complexity and accelerates decision-making."

Both platforms support 30+ SDKs across major languages. But Statsig adds capabilities that matter for modern architectures: edge computing support for Cloudflare Workers, real-time streaming APIs, and native TypeScript SDKs. These details matter when you're building performance-critical applications.

Pricing models and cost analysis

Transparent vs. complex pricing structures

Split's pricing immediately reveals its enterprise DNA. Teams start at $33 per user per month, with separate charges for monthly tracked users and impressions. Add five engineers, two PMs, and a data analyst? You're already at $264 monthly before touching usage limits.

Statsig chose radical transparency: unlimited free feature flags forever. No seat limits. No monthly active user caps. You only pay for analytics events, and the pricing runs 50% cheaper than competitors. A startup can use the full platform - including advanced statistics - up to 10 million events monthly without paying anything.

Real-world cost scenarios

Let's get specific. A 50-person B2B SaaS startup with 100K monthly active users faces predictable costs:

• Each user generates ~20 sessions monthly

• Standard feature flag checks and basic analytics

• Team includes 10 engineers and 3 PMs using the platform

With Split, you're looking at $400+ monthly just for seats. Add usage fees and you'll quickly hit four figures. The same company stays completely free on Statsig's tier, with room to triple their user base before paying anything.

Enterprise migrations tell an even starker story. Don Browning, SVP of Data & Platform Engineering at SoundCloud, shared their evaluation process: "We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration." Cost wasn't the only factor, but switching from per-seat pricing to usage-based billing meant SoundCloud could give access to their entire engineering team without budget meetings.

The math gets worse for Split as you scale. Adding team members directly increases costs, regardless of actual platform usage. Statsig's event-based model means a dormant engineer account costs nothing - you only pay for experiments actually running.

Decision factors and implementation considerations

Time-to-value and onboarding complexity

Getting to your first experiment reveals fundamental platform differences. Split requires separate setup for feature flags and experimentation modules. Teams typically spend weeks configuring:

1. Feature flag infrastructure and approval workflows

2. Experiment allocation and targeting rules

3. Analytics integrations and metric definitions

4. Team permissions and governance policies

Statsig collapses this timeline. Since experiments and flags share infrastructure, any feature flag becomes an A/B test with one toggle. Teams launch their first experiment within days. The generous free tier - unlimited flags plus 5 million events - means you can validate platform fit before involving procurement.

Support quality and scalability

Both platforms offer enterprise support with dedicated customer success teams and SLAs. But Statsig adds something unusual: direct founder access via Slack. One G2 reviewer noted with surprise: "Our CEO just might answer!" This isn't sustainable forever, but it signals a different relationship with customers.

Technical scalability tells the real story. Statsig processes over 1 trillion daily events with 99.99% uptime - infrastructure originally built for Facebook scale. Split matches this reliability but at significantly higher cost. The pricing comparison shows Statsig running 50-80% cheaper for equivalent volume.

Infrastructure architecture impacts your data strategy too. Statsig offers warehouse-native deployment that keeps sensitive data in your control. Split lacks this option - all data flows through their servers. For healthcare startups or financial services companies, this architectural decision often eliminates Split from consideration entirely.

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

Statsig solves a specific problem: smaller teams need enterprise-grade experimentation without enterprise pricing. The platform delivers the same capabilities that power experiments at OpenAI, Figma, and Notion - but with pricing that works for 50-person startups.

The unlimited free feature flags change the conversation entirely. While Split charges from day one, Statsig lets teams build their entire flag infrastructure at no cost. You only pay when you need advanced analytics - and even then, costs run 50% below traditional platforms.

Integration matters as much as price. Sumeet Marwaha from Brex captured why teams consolidate on Statsig: "The biggest benefit is having experimentation, feature flags, and analytics in one unified platform. It removes complexity and accelerates decision-making." Instead of stitching together Split, Amplitude, and FullStory, you get unified insights from one system.

The infrastructure handles hyperscale when you need it. Teams running hundreds of monthly experiments across billions of users rely on the same platform available to early-stage startups. Sub-millisecond latency and warehouse-native deployment options mean you won't outgrow the platform as you scale.

Closing thoughts

Choosing between Split and Statsig often comes down to company stage and philosophy. Split serves enterprises well - teams that need approval workflows, compliance features, and traditional vendor relationships. But smaller teams increasingly question why experimentation infrastructure should cost thousands monthly when Statsig delivers more capabilities for less.

The market is validating this approach. Companies from 10-person startups to Microsoft subsidiaries run production workloads on Statsig. They're proving that world-class experimentation doesn't require enterprise budgets.

Want to dig deeper? Check out Statsig's transparent feature comparison or their detailed migration guides for moving from other platforms. Hope you find this useful!