GrowthBook vs PostHog: Which Open-Source Tool to Choose

GrowthBook and PostHog get compared constantly because they share a label, open source, and almost nothing else about their design. GrowthBook was built as a feature flagging and experimentation platform from its first commit. PostHog was built as an open-source product analytics suite and grew until experimentation became one module inside a much wider product. Every other difference in this comparison, from the statistics engine to how heavy the self-hosted stack is, follows from that origin. This guide compares them criterion by criterion, adds the traffic arithmetic that neither vendor page will run for you, and ends with a decision framework by team profile. For the category-level view, see our neutral comparison of CRO and A/B testing tools and the open-source A/B testing tools guide.

Disclosure first: Donnu is an A/B testing tool and competes with both on part of this scope. This piece is written to be useful to a reader who ends up choosing either one.

GrowthBook vs PostHog: quick overview

	GrowthBook	PostHog
Origin	Feature flagging and experimentation from the first commit	Open-source product analytics that expanded into experimentation
Primary audience	Developer-first product and engineering teams	Teams already centralizing product analytics in one tool
Architecture	Server-side through SDKs, optional client-side visual editor	Server-side and client-side over the same event pipeline
Default statistics	Bayesian, with frequentist and sequential testing available	Bayesian, with a frequentist alternative documented
Variance reduction	CUPED, documented as available for both engines	CUPED, documented at the metric level
Where experiment data comes from	Your own SQL warehouse, or SDK-reported events	The same event pipeline that powers funnels and trends
Self-hosted	Single Docker image plus MongoDB, docker-compose or Helm	Heavier data stack, officially unsupported and free-tier only
Managed cloud	Yes, free to get started	Yes, free tier per product
Beyond experimentation	Feature flags, little else by design	Funnels, session replay, surveys, flags, data warehouse

Documentation details in that table were checked on 18 August 2026. The rest of this guide unpacks each row.

Where each one came from, and why it still matters

GrowthBook started with a deliberately narrow proposal: feature flagging and experimentation, without trying to become a general analytics tool. The product assumes from the outset that you already have, or will build, your own event collection or warehouse, and that its job is to deliver variant assignment and the statistics engine on top of that, not to replace the analytics you already run.

PostHog started as an open-source product analytics tool, aimed at giving product teams a self-hostable alternative to the hosted analytics platforms. Experimentation arrived later, built on the same event infrastructure that already powered funnels, retention and trends. That order of arrival matters: in PostHog, an A/B test is, underneath, a question asked of the same data pipeline that answers every other report in the product.

The statistics engines, documented rather than assumed

Both tools default to Bayesian analysis, which surprises people who expect the developer-first tool to be the frequentist one. The differences are in what sits around that default. Details below were read in each vendor’s official documentation on 18 August 2026.

GrowthBook states plainly that it defaults to Bayesian statistics “because they provide a more intuitive framework for decision making for most customers”, and lets you switch the engine at the organization or project level. Its frequentist engine runs two-sample t-tests and adds sequential testing, documented specifically to mitigate concerns with peeking when experiments are stopped early. CUPED variance reduction is documented as available for both engines.

PostHog documents a Bayesian analysis engine as its default, describing it as a statistically rigorous yet user-friendly approach, and reports outcomes as probability statements, for example a 96 percent chance that a variant increases the conversion rate, rather than as p-values. Frequentist analysis is documented alongside it, and CUPED variance reduction can be enabled when configuring a metric, described as reducing noise by accounting for pre-exposure behavior.

The honest summary: on statistical school, these two are closer than the internet suggests. Both default Bayesian, both offer frequentist, both document variance reduction. The distinguishing item is GrowthBook’s sequential testing option, which matters a great deal if your team will look at results before the planned end, and the deeper difference is not statistical at all, it is architectural, which is the next section.

Whichever engine you pick, no method rescues a test with an insufficient sample. Our statistical significance guide covers the mechanism end to end, and the peeking problem explains what sequential testing is protecting you from.

Where the number comes from: warehouse-native versus event-pipeline-native

This is the difference that actually changes your work.

In GrowthBook, the SDK fetches the flag or variant assignment and, when needed, reports a minimal exposure event. The metric calculation can then happen entirely over the SQL warehouse you already maintain, against definitions your data team already owns. Nothing has to be re-instrumented, and the numbers in the experiment report reconcile with the numbers in the rest of the business by construction, because they come from the same tables.

In PostHog, the experiment SDK is the same SDK that tracks every other product event. For a team that has already instrumented analytics there, turning on an experiment is nearly free in integration terms, because the behavioral data is already flowing and any existing metric can become an experiment metric without new work.

Self-hosting: same promise, very different weight

Both offer an open-source core you can host yourself and a managed cloud. The operational weight is where they separate, and the gap is large.

GrowthBook documents a single Docker image containing the NextJS frontend, the ExpressJS API and the Python stats engine, plus a MongoDB instance to store login credentials, cached experiment results and metadata. It publishes a docker-compose setup for quick deployment and an official Helm chart for Kubernetes.

PostHog self-hosting is a full event-processing data stack, and PostHog’s own documentation is unusually direct about it: the self-hosted deployment is distributed openly but is officially unsupported, gives access only to free-tier features, with everything on paid plans remaining cloud-exclusive, and the company explicitly recommends the cloud for most users, reserving self-hosting for teams with genuine privacy requirements or serious infrastructure capability.

	GrowthBook self-hosted	PostHog self-hosted
Core components	Single Docker image plus MongoDB	Full event-processing stack
Documented quick start	docker-compose, plus official Helm chart	Available, with cloud recommended for production
Vendor’s own stance	A supported deployment path	Officially unsupported, free-tier features only
Realistic ongoing cost	Version upgrades and database care	Cluster operation, storage growth, upgrade risk

Neither is install-and-forget. If your reason for self-hosting is to avoid a subscription, price the engineering hours honestly first: for a small team, they usually exceed the subscription, and the heavier stack makes that gap wider.

The arithmetic neither vendor page runs for you

Tool choice consumes weeks of evaluation, while the thing that actually decides whether your experiments produce answers is traffic. Take a self-serve signup flow with an activation rate of 22 percent and about 9,000 eligible users per week, tested server-side through either platform:

Current conversion rate%Minimum detectable effectrelative (%)absolute (pp)Confidence909599%Power8090%Visitors per week (total)Testtwo-sidedone-sided

-Visitors per variation

-Total (2 variations)

-Estimated duration

Two-proportion normal approximation, 2 variations (50/50). Tweak the inputs and watch it update live.

Effect you want to detect	Users per variation	Total for two arms	Days at 9,000 per week
+10 percent relative (22.0 to 24.2 percent)	5,761	11,522	9
+5 percent relative (22.0 to 23.1 percent)	22,657	45,314	36

At 95 percent confidence and 80 percent power, two-sided. The first row is a comfortable sprint-length experiment. The second is more than a month for an effect only half as large, which is the quadratic relationship in action and the reason your traffic, not your tool, sets how subtle an improvement you can chase. Neither GrowthBook nor PostHog changes those numbers; the same arithmetic applies to any platform, including ours.

Reading the result

Say the 10 percent design runs to 6,000 users per variation and finishes with 1,320 activations on control (22.00 percent) against 1,452 on the variation (24.20 percent).

Control (A)

VisitorsConversions

Variation (B)

VisitorsConversions

Confidence90%95%99%

Control (A) · Rate-

Variation (B) · Rate-

Relative lift-

p-value-

95% CI of the difference-

Two-sided two-proportion z-test. "Not significant" almost always means not enough sample, not that the versions are equal.

Through this blog’s engine: z = 2.86, p-value = 0.0042, a 95 percent confidence interval on the difference of +0.69 to +3.71 percentage points, and a relative improvement of +10.0 percent. A clean win, and worth noticing what the interval says: the honest range of the true effect spans from a modest gain to a large one, which is what a well-sized test buys you and what a lone “96 percent probability to win” badge, in either tool’s default reporting style, tends to obscure.

Decision framework by team profile

Team profile	The choice that usually fits	Why
Small startup, no dedicated engineering time for the tool	Managed cloud, either vendor	Self-hosting costs more in hours than the subscription saves
Developer-first growth team with a mature warehouse	GrowthBook self-hosted	Statistics computed over definitions your data team already owns
Product team with no consolidated analytics yet	PostHog	Solves funnels, replay and experiments in one account instead of stacking tools
Already running one of them for another purpose	Turn on the experimentation module of the one already live	Avoids a second variant-assignment system competing over the same truth
Marketing team testing pages without developer support	Neither, most likely	Both are developer-first; a client-side tool with a visual editor fits better

That last row is the one most often discovered late. Both of these products assume a deploy pipeline and an engineer. A team whose bottleneck is “we cannot ship a page change without a sprint” is buying the wrong category, not the wrong vendor, and the client-side versus server-side comparison is the more useful decision to make first.

Common mistakes when choosing between them

Mistake	Why it backfires
Picking PostHog for the analytics suite without checking the experiment engine meets your rigor bar	The depth is there, but the product is designed around analytics generally; confirm it covers what your stakeholders will question
Picking GrowthBook expecting native funnels, session replay or surveys	It deliberately does not try to be an analytics suite; those come from another tool
Self-hosting either without budgeting continuous maintenance	Neither is install-and-forget, and PostHog in particular asks for a serious data stack
Comparing them on a price figure copied from a third-party article	Both revise plans and free tiers often, and blog numbers age quickly
Treating “both are open source” as meaning “both are interchangeable”	The shared licence model hides opposite product centers of gravity

Automate this with Donnu

If what keeps you from GrowthBook or PostHog is not the product but the weight of standing up and maintaining a self-hosted stack, the docker-compose, the database, the version upgrades, the security of the environment, Donnu is worth a look as a lighter managed option: no infrastructure to operate, honest Bayesian statistics with the interval reported next to the effect, and per-account data isolation from the start. It is not a replacement for GrowthBook-style product feature flagging, nor for PostHog’s full analytics suite, and claiming otherwise would be dishonest. It solves the experimentation part without asking someone on your team to become a database operator at the weekend.

Start a free 14-day trial and check whether dropping the infrastructure maintenance pays off in your case, especially if engineering time is the scarcest resource you have.

References

• GrowthBook. Statistics overview. Bayesian as the documented default, the frequentist engine with two-sample t-tests and sequential testing to mitigate peeking, and CUPED available for both engines. Checked 18 August 2026. docs.growthbook.io/statistics/overview.
• GrowthBook. Self-hosting. Single Docker image with frontend, API and Python stats engine, MongoDB for credentials, cached results and metadata, docker-compose quick start and official Helm chart. Checked 18 August 2026. docs.growthbook.io/self-host.
• PostHog. Experiment statistics. Bayesian analysis engine as default, results expressed as probability statements, and CUPED variance reduction at the metric level. Checked 18 August 2026. posthog.com/docs/experiments/statistics.
• PostHog. Self-host. Openly distributed but officially unsupported, limited to free-tier features, with cloud recommended for most users. posthog.com/docs/self-host.
• Kohavi, R., Tang, D. and Xu, Y. Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press, 2020. Chapters on platform choice, instrumentation and variance reduction. Companion material at experimentguide.com.

Read also: CRO tools compared · Open-source A/B testing tools · GrowthBook review · PostHog review · GrowthBook vs Statsig