In-depth: PostHog vs Optimizely
Nov 07, 2023
Contents
PostHog and Optimizely are both multi-product tools to help you improve your apps and websites. Beyond experimentation and feature flags, they have significantly different focuses:
PostHog helps you build better products with tools like product analytics, session replay, and surveys.
Optimizely is an all-in-one system for marketing that includes content management, campaign planning, asset management, and checkout customizations.
This post compares their platforms, experimentation features, reporting, pricing, and more.
How is PostHog different than Optimizely?
1. Product and startup-focused
PostHog is built for high-growth startups working to build the best possible products. It's easy to get started and provides all the tools you need at an early stage. There's a generous free plan and startups get free credits.
Optimizely focuses on providing marketing, ecommerce, and content tools to massive enterprises. It has fewer product focused tools.
2. Transparent, free, and self-service
PostHog is open source. Everything from our code to roadmap to strategy is open for everyone to see.
Along with this, you can sign up for PostHog for free. You don’t need to fill out a contact form, add a credit card, or have a sales call. You can use PostHog for free forever if you want.
Optimizely is open about some areas, like roadmap and its SDKs, but you have to talk to sales before signing up.
3. Analytics and reporting built-in
PostHog links all its tools together. This means you get all the features and visualizations of product analytics for your feature flags and A/B tests. You can use them in trends, funnels, and even directly query related metrics with SQL.
Optimizely has some analytics, such as web marketing, but largely relies on Google Analytics for tracking and reporting. This limits the analysis you can do related to your feature flags and experiments.
Platform
Although both Optimizely and PostHog provide experimentation and feature flags, their overall platforms are significantly different.
PostHog | Optimizely | |
Open source Optimizely has open-source SDKs | ✔ | ✖ |
Self-service Start without talking to sales | ✔ | ✖ |
Experiments Run A/B tests | ✔ | ✔ |
Feature flags Manage and rollout features remotely | ✔ | ✔ |
Product analytics Native feature tracking | ✔ | ✖ |
Web analytics Get web stats like traffic | ✔ | ✔ |
Session replay Play back real user sessions | ✔ | ✖ |
Surveys Ask users questions and track responses | ✔ | ✖ |
CMS Manage content | ✖ | ✔ |
Cart optimization Optimize ecommerce checkouts | ✖ | ✔ |
Project management Manage projects related to experiments | ✖ | ✔ |
Ready to find out more?
The big platform difference between the two beyond marketing vs product features is analytics. Optimizely relies on external analytics providers like Google and Adobe Analytics to track feature flags and A/B tests. PostHog has a full analytics suite built-in, including autocapture, custom events, direct SQL access, and more.
Being a marketing-focused platform, Optimizely includes project and content management tools like request forms, asset libraries, and hypothesis briefs. PostHog leaves the planning to the other tools you are using but does include notebooks for analysis.
Web experimentation
Optimizely splits its experimentation features into two separate categories, web and feature. We will compare both separately against PostHog.
The core web experimentation features like traffic allocation, preview mode, cross-browser, dynamic website support, targeting, and more are available in both Optimizely and PostHog
PostHog | Optimizely | |
A/B/n tests Do tests with multiple variants | ✔ | ✔ |
Custom targeting Target users with custom attributes | ✔ | ✔ |
Custom goals Set experiment goals to any metric | ✔ | ✔ |
Single-page app support Use app frameworks like React and Vue | ✔ | ✔ |
No-code experiments Implement experiments without code | ✖ | ✔ |
Low-code experiments Implement experiments with a small amount of code | ✔ | ✔ |
Funnel tests Use funnels as a goal | ✔ | ✔ |
Native analytics Track experiments without third-party tools | ✔ | ✖ |
Scheduling Schedule experiments to run at specific times | ✔ | ✔ |
Use external data Use data from other platforms in experiments |