An alternative to Heap for experimentation: Statsig

Product teams choosing between analytics platforms face a fundamental decision: do you want comprehensive behavioral tracking or integrated experimentation capabilities? Heap built its reputation on automatic event capture—every click, scroll, and interaction tracked without code changes. But this approach creates a critical gap.

Statsig takes the opposite approach, bundling experimentation, feature flags, and analytics into one platform. This isn't just feature consolidation; it's a different philosophy about how teams should ship and measure products. The choice between these platforms shapes your entire product development workflow.

Company backgrounds and platform overview

Statsig emerged in 2020 when ex-Facebook engineers decided to build something different. They wanted an experimentation platform without the legacy bloat that plagued existing tools. Their focus: speed, developer experience, and unified data infrastructure.

Heap took a different path, launching in 2013 with automatic event capture at its core. The platform tracks every user interaction without manual instrumentation. This retroactive analysis lets teams ask new questions about historical data—a capability that attracted thousands of companies. But seven years later, the platform still lacks native experimentation features.

The philosophical differences run deep. Statsig bundles experimentation, feature flags, analytics, and session replay into one platform. Feature flags live alongside metrics; session replays connect directly to conversion funnels. You run experiments where you analyze data. Heap positions itself as analytics-first, emphasizing comprehensive data collection over experimentation workflows.

Sumeet Marwaha, Head of Data at Brex, explains the value: "Having experimentation, feature flags, and analytics in one unified platform... removes complexity and accelerates decision-making." This integration isn't just convenient—it fundamentally changes how teams ship features.

Both platforms serve modern product teams but solve different core problems. Statsig optimizes for the build-measure-learn cycle with integrated testing capabilities. Heap optimizes for behavioral understanding without the tools to act on those insights directly.

Feature and capability deep dive

Core experimentation capabilities

Statsig offers advanced A/B testing with sequential testing, CUPED variance reduction, and automated rollback guardrails. The platform includes stratified sampling, switchback testing, and both Bayesian and Frequentist statistical approaches. Teams can run mutually exclusive experiments and configure holdout groups for long-term impact measurement. Every feature flag doubles as an experiment—no extra configuration needed.

Heap lacks native experimentation features entirely. Users on Reddit discuss frustrations with Heap's limited testing capabilities compared to competitors. You'll need third-party integrations for basic A/B testing, creating data silos and workflow friction. Some teams maintain three separate tools: Heap for analytics, LaunchDarkly for flags, and Optimizely for experiments.

Analytics and data architecture

Both platforms provide user journey mapping, funnel analysis, and retention metrics—but implementation differs drastically. Statsig's warehouse-native deployment lets your data live in Snowflake, BigQuery, or Databricks. You maintain complete control while accessing full analytics capabilities. Heap requires data export to your warehouse, adding latency and complexity to analysis workflows.

Heap automatically captures all user interactions through its single-snippet implementation. Sounds great until you see the storage bill. This approach creates massive data volumes that increase costs exponentially. One mid-size SaaS company reported their Heap costs tripled after enabling session replay across their product. Statsig allows selective event tracking with custom metric configuration including:

• Winsorization for outlier handling

• Custom capping thresholds

• Metric-specific filters

• User-level aggregations

The platforms diverge sharply on real-time capabilities. Statsig processes over 1 trillion events daily with sub-millisecond latency for feature evaluations. Heap's architecture wasn't built for this scale—teams implement sampling strategies just to keep the platform responsive.

Pricing models and cost analysis

Transparent versus custom pricing structures

Statsig charges only for analytics events with unlimited feature flags included at no additional cost. Check their pricing page: clear tiers, published rates, no surprises. You can calculate annual costs in a spreadsheet before talking to sales. Heap starts at $3,600 annually with session-based limits that make budget planning nearly impossible.

The opacity frustrates teams. Reddit users express frustration with Heap's pricing model: "You can't calculate costs without multiple sales conversations." Custom quotes for basic features. Hidden fees for additional seats. Overages that appear months later.

Real-world cost scenarios

Let's talk real numbers. For 100K MAU generating standard events:

• Statsig: ~$1,200/month (all features included)

• Heap Growth tier: ~$2,500/month (analytics only)

• Add experimentation tools: +$1,500/month minimum

Statsig's pricing analysis shows consistent savings across all usage levels. The free tier includes 50K session replays monthly versus Heap's limited 10K session allowance. Small teams run full experimentation programs without budget approval.

A company with 1M MAU faces stark choices. Heap's costs balloon to tens of thousands monthly. Add separate experimentation tools and you're looking at six-figure annual commitments. Statsig scales linearly: you know exactly what growth will cost.

Decision factors and implementation considerations

Developer experience and time-to-value

Statsig provides 30+ SDKs with sub-millisecond evaluation latency across every major platform. Integration takes hours: drop in the SDK, wrap features in flags, start collecting data. The platform's edge computing support means zero latency for global deployments. Your Australian users get the same performance as those in San Francisco.

Heap's automatic tracking captures events without code changes initially. But users report that meaningful analysis requires significant technical work. Custom event tracking needs developer involvement. Complex funnels require SQL knowledge. The "no-code" promise breaks down quickly when you need actionable insights.

Support and documentation quality

Response time matters when experiments break. Statsig offers direct Slack access to engineers—sometimes even the CEO jumps in to help. Their AI-powered support bot handles common questions instantly while humans tackle complex issues. G2 reviews consistently mention this responsiveness: "Our CEO just might answer!"

Documentation quality shows in the details:

• Every SDK includes runnable code examples

• SQL queries are published transparently

• Best practices guide based on real customer learnings

• Video tutorials for complex workflows

Heap provides email support on paid plans with standard SLAs. Community forums exist but lack the immediacy of direct engineering access. Advanced use cases often require piecing together information from multiple sources.

Data control and privacy considerations

Warehouse-native deployment sets Statsig apart for privacy-conscious teams. Your data never leaves Snowflake, BigQuery, or Databricks. This architecture satisfies GDPR, HIPAA, and SOC 2 requirements without compromising functionality. Financial services and healthcare companies can actually use the platform.

Heap requires sending data to their servers for processing. They offer security certifications, but some industries simply can't accept third-party data storage. The lack of self-hosted options eliminates Heap from consideration for many regulated businesses. One fintech startup had to migrate off Heap entirely when their compliance team discovered this limitation.

Scalability and performance at volume

Statsig processes over 1 trillion events daily with 99.99% uptime across all services. Infrastructure scales automatically: no performance cliffs, no query timeouts, no sampling required. Notion scaled from single-digit to over 300 experiments per quarter on this infrastructure.

Performance metrics that matter:

• Feature flag evaluations: <1ms globally

• Analytics queries: <3 seconds for most reports

• Experiment results: Real-time updates

• SDK payload size: <20KB gzipped

Heap's session-based pricing model becomes prohibitive at scale. Performance degrades with data volume—complex queries timeout, dashboards load slowly, exports fail. Users implement workarounds: sampling data, archiving old events, limiting tracked interactions. Not exactly the "capture everything" promise they started with.

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

Statsig delivers integrated experimentation capabilities that Heap simply doesn't offer. While Heap focuses solely on analytics, Statsig combines A/B testing, feature flags, and analytics in one platform. You ship features and measure impact without switching tools. This unified approach helped Notion scale their experimentation program 30x in under a year.

The transparent pricing model eliminates budget surprises while providing enterprise-grade scalability. Unlike Heap's opaque custom pricing that frustrates users seeking affordable alternatives, Statsig publishes clear rates. You pay only for analytics events. Feature flags remain free at any scale. No hidden fees, no surprise overages.

Software Engineer Wendy Jiao from Notion puts it simply: "Statsig enabled us to ship at an impressive pace with confidence." Companies like OpenAI and Anthropic chose Statsig for unified analytics and experimentation workflows. They run hundreds of concurrent experiments while maintaining sub-second page loads.

Three key advantages stand out:

1. Warehouse-native deployment for complete data control

2. 50K free session replays monthly (10x competitor offerings)

3. Direct engineering support via Slack for all customers

Enterprise customers report 50% cost savings compared to maintaining separate analytics and experimentation platforms. But the real value isn't just cost—it's velocity. Teams ship faster when experimentation lives alongside analytics.

Closing thoughts

Choosing between Statsig and Heap isn't really about features—it's about philosophy. Do you want to observe user behavior or actively experiment with it? Heap gives you comprehensive tracking but forces you to build experimentation workflows elsewhere. Statsig provides the complete toolkit for teams serious about data-driven development.

The platforms serve different stages of product maturity. Early teams exploring user behavior might start with Heap's automatic tracking. But once you need to run experiments, control rollouts, and measure impact precisely, the lack of native experimentation becomes a bottleneck. That's when teams typically evaluate Statsig.

For teams ready to dive deeper, check out Statsig's migration guides, ROI calculator, or spin up a free account to explore the platform directly. The best way to understand the difference is to run your first experiment.

Hope you find this useful!