Operations | Monitoring | ITSM | DevOps | Cloud

Data engineering. Data science. Machine learning. Data governance. These are just a few of the many capabilities that make Databricks a favorite among developers and data engineers. Yet, most have one common complaint — its cost. Some say it’s too expensive, especially for small and medium-sized organizations. Others find it hard to justify using the platform due to budget limits. In addition, maintaining and running data warehouses can be costly.

So we all have commitment issues. Let’s face it, “commitment” is probably one of the most scary words in the English lexicon. Jokes aside, what does it actually mean when we talk about cloud commitments?

As someone deeply embedded in the cloud-native space, I’ve witnessed how the rapid evolution of cloud computing has unlocked remarkable opportunities—but also introduced significant challenges. Recently, I revisited some research we conducted back in 2022, which revealed a critical trend: despite the hundreds of services offered by the major cloud providers (the Big Three hyperscalers), most businesses use fewer than 10 services from their cloud provider.

If you are reading this, you likely understand how Machine Learning (ML) and Artificial Intelligence (AI) can improve your organization, from operational efficiency to boosting your bottom line. Yet, setting up and running a robust AI infrastructure, including a massive dataset for training and specialized tools, can be expensive. Amazon Bedrock wants to simplify this challenge, enabling you to build generative AI applications faster and easier. But is Amazon Bedrock pricing worth it?

In part 1 of this series, we introduced the key Cloud Run metrics you should be monitoring to ensure that your serverless containerized applications are reliable and can maintain optimal performance. In part 2, we walked through a couple of Google Cloud’s built-in monitoring tools that you can use to view those key metrics and check on the health, status, and performance of your serverless containers.

In Part 1 of this series, we looked at key Cloud Run metrics you can monitor to ensure the reliability and performance of your serverless containerized workloads. We’ll now explore how you can access those metrics within Cloud Run and Google’s dedicated observability tool, Cloud Monitoring. We’ll also look at several ways you can view and explore logs and traces in the Cloud Run UI and Google Cloud CLI.

Google Cloud Run is a fully managed platform that enables you to deploy and scale container-based serverless workloads. Cloud Run is built on top of Knative, an open source platform that extends Kubernetes with serverless capabilities like dynamic auto-scaling, routing, and event-driven functions. By using Cloud Run, developers can simply write and package their code as container images and deploy to Cloud Run—all without worrying about managing or maintaining any underlying infrastructure.