ConfigCat vs. Optimizely Feature Experimentation

If you are looking for an experimentation/feature flag style tool that is quick to adopt and provides enough functionality for light/medium use cases, then this is the tool for you. Additionally, they are growing and expanding their functionality and feature set so they can grow alongside you and your needs. The publicly accessible roadmap is also a great benefit to see where time is being spent on which feature next.

Based on my experience with Optimizely Feature Experimentation, I can highlight several scenarios where it excels and a few where it may be less suitable. Well-suited scenarios: - Multi-Channel product launches - Complex A/B testing and feature flag management - Gradual rollout and risk mitigation Less suited scenarios: - Simple A/B tests (their Web Experimentation product is probably better for that) - Non-technical team usage -

Incentivized

• Fast and easy feature toggling that gets developers developing
• World class support in both speed and quality for how to best use the service
• Great ear for customer needs and fast paced development

• Splitting traffic between variants and enabling you to scale up or down the amount of traffic in each one
• Giving a standardised report that you can share with a huge number of users
• Showing a large variety of results/metrics you can then dive into

Incentivized

• Make it easier to pull all configs in a snapshot with a timeout so apps can use this as a precursor to allowing the user access.

• Difficult integration if your data is not front end
• Costly MAU model needs to be based on experiments not on site visits
• It's not easy to understand how to build an Experiment
• Onboarding team is more focused on punching through their slides and not focused on your needs or understanding.

Incentivized

ConfigCat has done the job, and has been great to work with.

Competitive landscape

Incentivized

Just everything from the website to locating what i need (docs) specific to my platforms is super easy. Not to mention the support articles answering previous questions

Easy to navigate the UI. Once you know how to use it, it is very easy to run experiments. And when the experiment is setup, the SDK code variables are generated and available for developers to use immediately so they can quickly build the experiment code

Incentivized

They have a community Slack channel that is open to anyone. They always seem to have people in there, even over the weekends and are always happy to answer any questions you have,

Support was there but it was pretty slow at most times. Only after escalation was support really given to our teams

Incentivized

It’s straightforward. Docs are well written and I believe there must be a support. But we haven’t used it

Incentivized

At iBinder we searched for and vetted several suppliers of a feature toggle service to handle feature toggling in our production environment. In addition to our functional requirements, it was crucial for us to find a partner that could deliver an EU-compliant service. We finally decided to sign a service agreement with ConfigCat. This has been a real success story for us – in addition to being compliant, ConfigCat delivers an amazing, flexible, and reliable service. They continue to impress by also being very transparent and having a fantastic support and they are very solution oriented and accommodating when it comes to our feature requests etc. We have now used ConfigCat for approximately 2 years and we give our warmest recommendations to anyone who needs a stable, reliable and EU-compliant feature toggle service.

In previous companies I've used Monetate which is a similar A/B testing kind of feature experimentation engine that is very similar from my memory, but again, back to the point of these new features of the analytics engine and Opal, it kind of cuts it above Monetate from my experience. Obviously Monetate may have improved since when I lost use it, but from what I can see, yeah.

Incentivized

had troubles with performance for SSR and the React SDK

Incentivized

• Allowed us to migrate seamlessly from a major customer communication system to another, reducing end-user friction and production bugs by being able to turn features off if they didn't work as intended.
• We went from zero experimentation to running 10-20 experiments concurrently across systems. Engineering teams are thinking in an experimentation mindset.

• We have a huge, noteworthy ROI case study of how we did a SaaS onboarding revamp early this year. Our A/B test on a guided setup flow improved activation rates by 20 percent, which translated to over $1.2m in retained ARR.

Incentivized