While Split focuses on feature management and experimentation, PostHog provides a more comprehensive suite of tools including product analytics, session recording, and feature flags.
Split is a feature management and experimentation platform that enables development teams to release features faster and more safely. The platform combines feature flags with testing and observability, allowing teams to ship updates more frequently while instantly detecting the impact of every feature they release.
Split's core offerings include:
Feature flags: Deploy code when you want and release when you're ready
A/B testing and experimentation: Validate changes and make data-driven decisions
Observability tools: Monitor feature impact and performance in real-time
Split's platform is geared toward product development teams in complex or regulated industries seeking governance, flexibility, and automation for their releases. It aims to help these organizations prioritize speed and safety in their software delivery process.
PostHog is an open-source product analytics platform that helps companies build better products by understanding user behavior. Founded in 2020, PostHog's mission is to enable product teams to make data-driven decisions and improve their offerings through a suite of tools for analyzing user interactions and experiences.
PostHog's core offerings include:
Product analytics: Track events and visualize user journeys through funnels and paths to gain insights into retention and stickiness
Session recording: Observe how users interact with your product to identify areas of friction and optimize the user experience
Feature flags and A/B testing: Safely test new features and experiment with different variations to drive growth and engagement
PostHog's platform is geared toward product teams looking to understand their users and make informed decisions based on data. The company's open-source model and generous free tier make it an attractive choice for startups and businesses seeking affordable, flexible analytics solutions. Additionally, PostHog's extensive documentation and API appeal to developers who value control over their analytics stack and the ability to integrate with existing tools and workflows.
Split provides a flexible, usage-based pricing model with a free tier for basic feature flag capabilities, integrations, and up to 50,000 monthly tracked keys. Paid plans start at $35 per seat per month for the Startup plan, which includes access to all features and functionalities.
PostHog offers a generous free tier with usage-based pricing beyond the limits, charging based on monthly usage for each product like analytics and session recordings. Enterprise plans with advanced features and dedicated support start at $2,000 per month, while PostHog's open-source model makes it an attractive choice for startups and businesses looking for an affordable solution.
Split is well-suited for complex, highly regulated industries with strict governance requirements. The platform's advanced feature management, observability, and experimentation capabilities make it an ideal choice for teams prioritizing speed and safety in software delivery. Split's Feature Data Platform also provides automated rollout monitoring and instant impact detection, making it a good fit for organizations seeking comprehensive feature observability.
However, Split may have higher costs for smaller teams or startups compared to other solutions. The platform's extensive feature set and advanced capabilities could also present a steeper learning curve for non-technical users or teams new to feature management and experimentation practices. Additionally, Split has limited open-source or community-driven development compared to some alternatives, which may be a consideration for organizations that value those aspects.
TL;DR: Split is better suited for complex, highly regulated industries and teams prioritizing speed and safety, but may have higher costs and a steeper learning curve for smaller teams or non-technical users.
PostHog is well-suited for startups and small teams with limited budgets seeking an open-source, self-hosted analytics solution. The platform's generous free tier and usage-based pricing make it accessible to organizations of all sizes. PostHog's flexibility and control over the analytics stack also make it an attractive choice for teams that value data privacy and customization.
However, PostHog may have limited scalability or enterprise-grade features compared to proprietary solutions. As an open-source platform, PostHog relies on community contributions for ongoing development and support, which could lead to potential challenges with data privacy or compliance for certain industries. Additionally, the self-hosted nature of PostHog may require more technical expertise to set up and maintain compared to fully managed solutions.
TL;DR: PostHog is better suited for startups and teams seeking an open-source, self-hosted analytics solution, but may not be the best fit for organizations requiring enterprise-grade features or fully managed support.
Statsig is an all-in-one platform that offers feature flags, product analytics, and experimentation in a single tool. We scale with you, whether you're a startup or an enterprise - like OpenAI, Notion, and Atlassian.
Our pricing is transparent and usage-based, making us a great option for companies of all sizes. Sign up for free to get started, or request a demo to learn more about our enterprise offerings.
Standard deviation and variance are essential for understanding data spread, evaluating probabilities, and making informed decisions. Read More ⇾
We’ve expanded our SRM debugging capabilities to allow customers to define custom user dimensions for analysis. Read More ⇾
Detect interaction effects between concurrent A/B tests with Statsig's new feature to ensure accurate experiment results and avoid misleading metric shifts. Read More ⇾
Statsig's biggest year yet: groundbreaking launches, global events, record scaling, and exciting plans for 2025. Explore our 2024 milestones and what’s next! Read More ⇾
A guide to reporting A/B test results: What are common mistakes and how can you make sure to get it right? Read More ⇾
This guide explains why the allocation point may differ from the exposure point, how it happens, and what you to do about it. Read More ⇾