Jellyfish is an intelligence platform for AI-Integrated Engineering. According to the vendor, the solution assists over 1,000 companies—including DraftKings, Box, and Blue Yonder—in utilizing Artificial Intelligence to manage software development lifecycles. By aggregating engineering data and contextual intelligence, Jellyfish is designed to help Research and Development (R&D) organizations measure impact, adopt industry practices, and support decision-making across AI adoption, project…
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LaunchDarkly
Score8.7 out of 10
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LaunchDarkly provides a feature management platform that enables DevOps and Product teams to use feature flags at scale. This allows for greater collaboration among team members, and increased usability testing before full-scale feature deployment.
$12
per month
Pricing
Jellyfish
LaunchDarkly
Editions & Modules
No answers on this topic
Foundation
$12
per month per Service Connection per month, or $10 per 1k client-side MAU per mo
Enterprise
Custom
Guardian
Custom
Offerings
Pricing Offerings
Jellyfish
LaunchDarkly
Free Trial
No
Yes
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
Yes
Entry-level Setup Fee
No setup fee
Optional
Additional Details
—
Discount available on the Foundation plan for annual pricing.
Jellyfish allows us to understand the allocation of our efforts without time tracking. We are also able to integrate this platform with our existing ticket tracking system and can see analytics instantly! Jellyfish works well for our teams because it has an opinionated view. Jellyfish understands that the metrics for an engineering team should help you understand what your organization values and is actively working on. We can use Jellyfish in a strategic way compared to other tools like this.
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
If a new feature should be added but unsure of how it will actually work or how users will accept the new enhancement or change, this tool allows you test and measure initial results. This saves so much time and energy knowing the results before it is deployed and might have low user adoption or acceptance.
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/B or Multi Variant Testing as a methodology to gather insight from customer usage. Experimentation as a feature within LaunchDarkly offers information around the success of one variant over another and whether the experiment has reached statistical significance.
Being able to decouple deployment of code from the release of a feature is hugely valuable.
Development teams are empowered to manage features within their production applications for reliability or testing purposes.
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
UI and navigation aren't very intuitive and require additional research before being able to use.
The individual developer metrics are not very useful and make the interface feel cluttered.
Overall, it takes time for the end-user to truly learn how to use the platform and navigate. There is so much information/data available that although the above is a con, we felt it still made sense, despite the learning curve.
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. TR verified that a representative sample of customers was invited. More Info
It's very easy to create new feature flags and set them properly. It is more difficult to get LaunchDarkly integrated within a distributed system so that flags can be used. Especially on stateless servers where gating features by user is not easy. Overall though, it is very easy to get started and I like how simple it is to use.
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
From what I have seen, LaunchDarkly integrates well with your code and also services you might have in your tech ecosystem. We use Jenkins for automation and we were able to use it to build pipelines to automate the control of LaunchDarkly toggles in our 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. 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. TR verified that a representative sample of customers was invited. More Info
The really cool thing about Jellyfish is the integrations that it has with the other tools, which are also very common within the software development industry. Consolidating all this data and being able to see graphs, numbers, and percentages in one place gives you a better way to analyze and define better solutions to improve your software development processes.
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
Have used a custom feature flag application created inhouse. All the basic functionality was same as the ones that LaunchDarkly provides. But as time progressed, it required more and more tracking capabilities like which user has turned on/off a feature flag, what are the statuses of different feature flags that are being used across the application etc., So, all and all maintenance of such tracking has become cumbersome.
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
Improved developer experience with some teams moving to Trunk-based Development.
Increased deployment frequency due to smaller code releases.
Validation of the technical and business value of work is achieved more quickly through smaller pieces of work and through experimenting with a small group of users before a feature gets to 100% of customers.
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
