Datadog

We are evaluating an APM tool and would like to select between AppDynamics or Datadog. Our applications are largely hosted on Microsoft Azure but we would keep the option to move to AWS or Google Cloud Platform in the future.

In addition to core Azure services, we will be hosting other components - including MongoDB, Keycloak, PagerDuty, etc. Our applications are largely C# and React-based using frontend for Backend patterns and Azure API gateway. In addition, there are close to 50+ external services integrated using both REST and SOAP.

Looking for the most straightforward APM tool to use with a Python CRUD stack. We would like to have flame graphs of individual transactions. We'd also like to see SRE tools for monitoring, alerting, and incident response. We are leaning towards Datadog. Thoughts?

I'm considering moving from Flask to Quart, does anyone have some experience with this migration?

I expect possible problems with connexion which we use as OpenAPI specification.

Would be good if someone can point downsides of moving to the Quart framework so I can double-check if my plan is worth doing.

Other libs and tools used in the project: SQLAlchemy, alembic, PostgreSQL, Datadog

cons for now:

• Refactoring uncertainty (not sure how big of a task is it)
• Connexion might not work with Quart (moving to another library)
• ...

We are migrating from New Relic to Datadog... Is there a way I can export all existing alerts in an easy way from New Relic to Datadog?

I see StatsD is commonly used in conjunction with Datadog. In fact, Datadog even has their own StatsD daemon (called DogStatsD) embedded in the DataDog agent. Can someone explain to me what it is that StatsD gives you which you don't already have with Datadog's APM and distributed tracing functionality?

We build everything in AWS around microservices and are looking at Amazon CloudWatch, Datadog, and New Relic. Which one would work best for our situation?