Both are powerful, both have rave reviews, and many of the features overlap. Besides that cute hedgehog, what’s the difference?

PostHog and Statsig both offer suites of tools that help builders be more data-driven in how they develop products, ranging from product analytics and session replay to feature flagging and experimentation.

While the platforms have many similarities, they also have stark fundamental differences, causing them to appeal to different audiences.

To help users looking to understand these differences (and the similarities) between the two, we’ve outlined the key takeaways below:

How does Statsig differ from PostHog?

Built to scale with you

Getting up and running on a platform quickly is incredibly valuable. Once you’re up and running and your product is growing, the last thing you want to do is have to re-platform and swap out the tooling stack your team has come to know and love.

This is why we’ve built Statsig to scale.

Over the last few years, we’ve upscaled our own tech stack to support massive event volumes so that our customers never need to worry about the performance of our product at scale.

Today, we process over 250 billion events a day with 99.99% uptime, snappy analytics query speed, and <1ms evaluation latency for flags and experiment configs. PostHog publicly reports that they've tracked ~100B events all-time. We track more than twice as many events each day.

As a result, Statsig powers companies ranging from startups just beginning to roll out their first beta through to the likes of OpenAI, Flipkart, and Atlassian.

Appropriately, our pricing is built to scale too. We help you get up and running with the most generous free tier in the industry (2M free events, 10k free session replays/ month), and the more you grow, the cheaper your event volume unit rate is.

So while you may not need scale today, planning ahead and choosing the solution that will grow with you as your product grows will be invaluable. Future you will thank you. 😎

Statsig versus Posthog, head to head

Wondering how Statsig stacks up against Posthog in a feature comparison? We've got the data.

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Product optimization as a first-class product

As you build, you’ll find yourself wondering how your users are engaging with your product, analyzing data, replaying user sessions, and scouring for trends that might be arising.

However, once you’ve identified a pain point or opportunity for product improvement, odds are you’ll want to act on that and launch a change to your product, measure the impact of that change, and start the whole process again.

We call this the Build → Measure → Learn loop, and while analysis tooling is critical for the “learn” component, building/ launching product changes and measuring the impact of these changes is an equally critical part of the product development cycle.

Statsig originally started with feature flags & experimentation—i.e., tools that power the Build → Measure part of the cycle—and subsequently moved into analysis tooling like Product Analytics and Session Replay.

As a result, we've prioritized tooling to ship products as much as tooling to analyze user behavior throughout our entire experience. We offer a world-class feature flagging and experimentation solution at a fraction of the cost of competitors.

Product optimization, whether within your core product, on your website, or for your acquisition funnel, is a critical function for companies of any size—especially startups that are going to market with something new and for whom iteration clock speed is a critical advantage.

Statsig makes this a competitive strength for any company building and launching products.

“Unscalable” customer obsession

Ever had an issue or a feature request for a B2B SaaS product?

If you weren’t a large-spending Enterprise account with a dedicated sales rep, you probably filed a ticket through some customer support portal, it went into a black hole, and you never heard about it again.

At Statsig, we view support as an entirely different ballgame. We don’t have a regular customer support team. We have a Customer Engineering team. Our engineers, product managers, data scientists, and designers are our customer support team. Everyone at the company participates in Slack support, hops on calls with customers, and interacts with our users in some capacity daily.

This means we’re not just more reachable when it comes to solving your problems, it also means you as a customer are a co-designer of the product. Your pain points, questions, and feature requests directly inform our roadmap, whether it’s on a weekly/ monthly/ quarterly basis.

We don’t just believe that the distance between our customers and our product development team should be 0; we live this mentality. This is something that our customers—from the Free tier to our largest Enterprise accounts—cite as a huge differentiator for them to use and grow with Statsig.

Related reading: Why people love Statsig’s customer support.

Better for Teams

One of our core tenets is that we believe building products is a team sport. This is why we don’t charge based on seats: We think everyone in the company should be in the Statsig Console, looking at dashboards, engaging with experiment readouts, and opting themselves in and out of new features to ensure they’re experiencing beta versions of the product.

We’ve incorporated collaborative features throughout our console from day one.

A few examples:

1. Slack integrations & “Follow” capabilities: Set up both our team-level and individual Slack notifications to ensure you’re receiving the right granularity of updates for configs you care about. Plus, we make it easy to “Follow” any config in the Console so that you can keep tabs on rollouts, experiments, and dashboards you care about most.

2. Discussions: We offer inline commenting and discussions to ensure you maintain the context of a config inline alongside the configuration itself.

3. Reviews: Configure review requirements for rollouts to add an extra layer of safety before pushing final changes to Production. Reviews also enable you to add a note, which gets preserved in the history of the config for easy reference in the future.

4. Experiment reports: Memorialize discussion alongside experiment results inline within Statsig’s Experiment Summary tab. These summaries will live in the tool for future reference and can also be easily exported as PDFs.

Finally, we make it safe and transparent for everyone on the team to make changes within Statsig. We offer comprehensive audit logs for easy debugging if something goes wrong or the settings on a config change in a way you may not have expected.

Your warehouse or ours

A major Statsig differentiator is our first-class support for two different deployment models: Cloud and Warehouse Native. Already started building your tech stack on Snowflake or GCP? No problem. Statsig Warehouse Native offers all the power of Statsig, sitting within your Warehouse on top of your canonical metrics catalog.

As startups begin to invest in a data warehouse solution earlier and earlier in their lifecycle, we’re meeting teams where they’re at, offering a full-stack product launch and analysis solution with no need to egress events or metrics to Statsig.

What’s not different between Statsig and PostHog?

Both are built for engineers

Both Statsig and PostHog are companies built by builders, for builders. Even our CEO is pushing PRs at all hours of the night (his commit history suggests he gets his second wind around 1AM). Our engineers use our products to build and launch Statsig and regularly suggest features to improve our core experience. We feel our customers’ pain because we are our customers.

Both ship ridiculously fast

In terms of similarities, both Statsig and PostHog ship very fast, and are customer-centric companies to their core. Both companies also care deeply about enabling product builders to leverage data to build better products.

Both are all-in-one platforms

Both Statsig and PostHog are all-in-one platforms. Statsig's products span and integrate all the products that product builders need to ideate, improve, test, release, and measure the impact of new features.

Our product analytics, experimentation, feature flags, session replay, web analytics, auto-capture, data connection, dynamic config, and warehouse-native products are purpose-built to work together.

The open-source question

An important detail to keep in mind is that PostHog professes a commitment to open source, which is appealing to DIYers and self-starters, but may not live up to expectations.

For one, many PostHog features—specifically ones like permissions, long-term data retention, and scalability—are not open source at all. Posthog has been up-front that their self-hosted deployment option is not fully supported, and they warn that it only scales to around 100k events per month.

It’s important for users beginning their product optimization and analytics journeys to be aware of this—and all the other details about open source as a whole. While it sounds like a quick way to get started, if you end up needing to re-platform once you hit any meaningful scale, you’re just kicking the can down the road.

And while you’re doing your research, check out our generous free tier: it’s no cost to get up and running, and you get 1 million events (plus 10k session replays) a month for free.

And if you’re a startup, check out Statsig’s startup program. Offering up to $50k in value for a year alongside premium support, Be Significant is a great way for startups to hit the ground running with building products in a more data-driven way.

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