Digma Preemptive Observability - Preempt performance and scaling issues in pre-production

Hi @valentyn_dudinov , in the pricing page https://digma.ai/pricing/, we mention that we only do annual billing.

@valentin_nalivayko Hey Valentin, thanks for your comment::) You’ll need to upgrade to the Digma for Teams plan, which supports unlimited users and endpoints.

Hi @celine_borsberry1 , please check our pricing page https://digma.ai/pricing, there is a plan for a single team limited by 5 microservices and then for the enterprise plan, it is prices per the amount of microservices, so you can pay as you grow.

@eran_artzi1 Hey Eran we are no longer just IDE bound - you can use our web interface to see al of the info even if the IDE isn't supported

@roni_dover from the pricing page it seems that the web interface is only available on the paid tiers?

@eran_artzi1 yes we have a standalone version that just installs to the IDE (IntelliJ) and is more toward experimentation but since we don't run any sophisticated data science on the local laptop using our central install provides the most capabitlies. We have a trial tier for that as well

@evantal great question - Digma can alert to pre-production issues by identifying them in the pre-production observability

Hi @roypovar , yes it definitely can. in most performance testing environment engineers are correlating metrics from different version, trying to catch regressions, yet they have no means to identify the root cause of the regressions, which leads to hours or days of troubleshooting, while versions keep updating in production. Also, since it is all about manual defining and recording thresholds, they might not define a metric in a place that experience a regression, an issue that will eventually materialize in production. With Digma performance testing looks completely different: 1. Digma finds issue with no need to pre-define metrics/ thresholds. 2. Per every issue, an RCA is automatically given at the code level. 3. the performance testing environment doesn't have to mirror the production load, as Digma identifies issues by their patterns which doesn't change because of too much or too little load.