12 karma · joined October 5, 2013
twitter:https://twitter.com/syedahmedz
The aim is to eventually pass on that data or use it for smarter inference like catching duplicates, bug frequency, reintroduction of code and possibly identifying tech debt. Our vision has been to go ticketless, while we're supplying the tooling to make those tickets today, in the future contextual information from different systems could provide enough context to do that work.
For the Gitlab for the time being we only allow connection of issue tickets to MRs and some organized views of commits and MRs in our progress mode.
Other explorations we did were around finer grained permissions or a collaborator vs assignee. The reason the industry itself is geared towards seats based plans in the is to drive better revenue predictability and handle cost variability between free and paid users better but we’re seeing more and more creative changes to sass billing models.
Eventually we caved and pushed their account execs to support a “secure” private cloud that passed our IT teams compliance criteria. Fun times!
You are absolutely right about a self hosted solution being the better workaround which means it resides on an internally accessible network where the setup experience would be much more seamless.
The question about self hosted git servers, we do support them but not officially through the GitHub app which is on the App Store. We do have a fully compatible backend that can integrate with any self hosted git using webhooks, we haven’t exposed those to users as of yet.
When we were working on pricing, we looked into everything from compute costs per user, data storage per workspace, new feature development, marketing costs and much more over the span of time. We came up with our pricing based off those costs and hence on our lower plans we’re still under the price of similar tools.
We’d love to, in the future figure out more flexible ways to price our users but at the same time maintain the service and growing feature set. I can assure you that we definitely gave having a Spotify family plan like pricing model a thought. In the mean time we are working with startups to accommodate their needs as they move into paid plans by offering discounts on long term commitments, we’re always open to discussions with our users and helping them stay empowered on our platform.
We take privacy around your data very seriously. We're thinking about the ML training model as a "walled garden", ie recommendations are based on your past sprint activity, effort load, tasks completed during a sprint, etc. and are exclusive to your organization.
In the future, if we decided to do benchmarking (for eg quick recommendations on how companies in your industry are running sprints), we would have a double opt-in. This would mean anonymizing the data, and providing the recommendations an opt-in. Basically, very similar to how google's autocomplete email recommendations work.
All is not lost though, startups that make software for healthcare are slowly taking hold of the market, but all those startups that have been killed off in the process have made a change for the newer ones to get this far.
The only problem with server side analytics is that they're pretty limited with functionality unless you have your own internal analytics system which can track alot more information than just page views and general traffic stats.