Digital.ai (formerly CollabNet) aims to help enterprises and government organizations deliver high-quality software at speed with TeamForge, its application lifecycle management suite. According to the vendor, Digital.ai offers innovative solutions, provides consulting and Agile training services, supports more than 10,000 customers with 6 million users in 100 countries, and has been recognized for 13 consecutive years as SD Times 100 “Best in Show” winner in the…
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Optimizely Feature Experimentation
Score8.4 out of 10
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Optimizely Feature Experimentation unites feature flagging, A/B testing, and built-in collaboration—so marketers can release, experiment, and optimize with confidence in one platform.
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Pricing
Digital.ai TeamForge
Optimizely Feature Experimentation
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Digital.ai TeamForge
Optimizely Feature Experimentation
Free Trial
Yes
No
Free/Freemium Version
No
Yes
Premium Consulting/Integration Services
Yes
Yes
Entry-level Setup Fee
No setup fee
Required
Additional Details
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More Pricing Information
Community Pulse
Digital.ai TeamForge
Optimizely Feature Experimentation
Considered Both Products
Digital.ai (formerly XebiaLabs, CollabNet VersionOne, and Arxan)
Digital.ai (formerly XebiaLabs, CollabNet VersionOne, and Arxan)
CollabNet TeamForge is more well-suited for technically knowledgeable users such as developers, systems analysts, QA but not for non-technical users due to the technical data used throughout the tool. CollabNet TeamForge is appropriate for most applications development. In any industry especially suited for high demand software delivery environments.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
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 -
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
It is easy to use any of our product owners, marketers, developers can set up experiments and roll them out with some developer support. So the key thing there is this front end UI easy to use and maybe this will come later, but the new features such as Opal and the analytics or database centric engine is something we're interested in as well.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Digital.ai (formerly XebiaLabs, CollabNet VersionOne, and Arxan)
I would like to see personalization for managing artifacts, that is, allowing the user to customize pages and save personalized settings and then save them as bookmarks.
I would like an improved subversion browser such as a graphical user interface rather than a basic file explorer.
I would like built in features for CollabNet TeamForge integrated with subversion so that i can associate my private code branches and code changes.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Would be nice to able to switch variants between say an MVT to a 50:50 if one of the variants is not performing very well quickly and effectively so can still use the standardised report
Interface can feel very bare bones/not very many graphs or visuals, which other providers have to make it a bit more engaging
Doesn't show easily what each variant that is live looks like, so can be hard to remember what is actually being shown in each test
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Digital.ai (formerly XebiaLabs, CollabNet VersionOne, and Arxan)
No answers on this topic
Optimizely
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
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
Digital.ai (formerly XebiaLabs, CollabNet VersionOne, and Arxan)
They always answer the phone when we put a call in, any time. They have consistently sent out contractors when needed and were there every step of the way when we switched over from ClearCase to TeamForge years ago. The support is what you really pay for when you buy into TeamForge.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Digital.ai (formerly XebiaLabs, CollabNet VersionOne, and Arxan)
CollabNet TeamForge is superior to Mercury mainly because it has a subversion change management tool integrated with the product. CollabNet TeamForge offers more complexity but with more flexibility for managing and tracking projects. I think CollabNet TeamForge is more comprehensive than either Mercury and JIRA. They're all user friendly tools with short learning curves but CollabNet TeamForge is most efficient in my experience. JIRA was recently adopted for particular applications and is currently being evaluated by various technology teams.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
When Google Optimize goes off we searched for a tool where you can be sure to get a good GA4 implementation and easy to use for IT team and product team. Optimizely Feature Experimentation seems to have a good balance between pricing and capabilities. If you are searching for an experimentation tool and personalization all in one... then maybe these comparison change and Optimizely turns to expensive. In the same way... if you want a server side solution. For us, it will be a challenge in the following years
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
We have improved various metrics throughout the course of our experimentation program with Optimizely and therefore sharing numbers is tricky. Essentially we only implement versions of the product that perform the best in terms of CVR, revenue/visitor, ATV, average order value, average basket size and so forth dependent on the north star we are trying to move with each release.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
