Google Cloud provides a wide range of services and tools to help engineering teams reduce the complexity of migrating and deploying applications in the cloud. As engineering teams work to improve the performance, reliability, and security of their applications, they also need to be conscious of cloud costs. But engineers often don’t have access to cost data, or they only see cost data in monthly reports. This limited visibility makes it difficult to understand the relationship between cost fluctuations and infrastructure changes across the services they own. In order to empower engineers to take ownership over the costs of the resources they’re deploying, everyone across your organization needs access to cost and observability data in the same platform.
To help teams address this challenge, we’re pleased to announce that Datadog Cloud Cost Management now supports Google Cloud, which complements our existing support for AWS and Azure. In this post, we’ll explore how Cloud Cost Management provides total cost observability for engineering teams. We’ll also show you how you can use Cloud Cost Management to:
- Get granular visibility into Google Cloud costs
- Uncover cost savings opportunities
- Avoid cost overruns
Cost observability for engineering teams
As DevOps engineers are increasingly responsible for provisioning their own infrastructure, they need to understand the total cost to run their services, share that cost data with other engineering teams, and ultimately optimize their services. That’s why Datadog Cloud Cost Management provides total visibility into cost data from Google Cloud and other providers and unifies it with telemetry data from your environment, all in one platform. By incorporating cost data into the same platform that engineers already use to monitor their services, teams can take ownership of their own costs without disrupting their existing workflows.
Engineers can understand their service costs and view this cost data alongside telemetry data from their resources, deployments, and more, allowing them to understand how changes—such as code deployments and infrastructure upgrades—actually impact costs. Datadog collects detailed telemetry data from key components of your Google Cloud environment, including:
- CPU, memory, and other resource metrics from services like Google Compute Engine (GCE) and Google Kubernetes Engine (GKE)
- BigQuery and Cloud SQL storage metrics
- Usage data from serverless applications running on Cloud Functions and Cloud Run
By correlating this observability data with cost data, engineers can get granular insights into their teams’ contributions to costs, uncover savings opportunities, and avoid cost overruns.
Unlock cost visibility
Granular visibility into cost data is essential for empowering engineers to take control over their own spend. However, technologies like Kubernetes operate on shared resources, which makes it difficult to precisely attribute costs to individual teams. Instead of tracking the total cost of a Kubernetes cluster, it can be more helpful to understand the cost of individual services you’re running, or see which teams are contributing the most to the cluster’s costs.
In Datadog Cloud Cost Management, you can use tags to isolate resources from Google Kubernetes Engine (GKE) and then drill down further by grouping by tags like team and service. For example, the following screenshot indicates that the platform team’s GKE costs have increased 27 percent over the past few days, compared to the previous period.
Engineers on each team can use Cloud Cost Management to gain a clearer understanding of how much their team has specifically contributed to the cost of shared resources, and investigate unexpected changes in costs.
Uncover cost savings opportunities
Incorporating cost data into the same platform as observability data also empowers you to proactively uncover inefficiencies and reduce wasteful spend. By directly comparing cost data with metrics like CPU utilization, you can quickly spot underutilized resources that you can downsize or remove without affecting customers.
In the following example, the cost breakdown table on our out-of-the-box Google Cost Savings Opportunities dashboard shows that the shopist service is consistently underutilizing its Compute Engine instances, indicating that you may be able to downsize. If you decide to take action, you can pivot to track key performance metrics, such as checkout latency, to ensure that this change does not negatively impact your end-user experience.
This dashboard can also help you identify opportunities to clean up abandoned resources in your environment. For example, the following graph visualizes Cloud SQL databases’ CPU and memory utilization. The size of each circle corresponds to its cost. By focusing on larger circles in the lower-left corner of the graph (i.e., databases that have relatively higher costs and are utilizing a small percentage of CPU and memory), you can identify potential opportunities to reduce waste. In this example, it looks like a database was created for testing, but it is not using much of its resources. In this case, you can easily reach out to the owner of this database to confirm that it is no longer needed. By taking action after identifying these kinds of cost savings opportunities, teams can promote a culture of cost ownership and take more control over their own cloud spend.
Avoid cost overruns
To automatically stay on top of cost changes over time, you can also set up automated monitors. Cost monitors can help you track the cost of resources you spin up, but they can also monitor a combination of costs and any other data that matters to your business.
For example, if you’re building an ecommerce app, you may find it useful to create a monitor that notifies you when the week-over-week change in the cost per customer checkout exceeds a certain percentage. These monitors can help engineering teams proactively avoid cost overruns as they scale their infrastructure and mitigate unexpected cost increases before they spiral out of control. With this data in hand, teams can also build features with an eye on cost efficiency and promote a culture of cost ownership.
Full observability for your Google Cloud environment
Datadog Cloud Cost Management enables engineers to view cost data in context with observability data, so they can ownership of their costs. With Cloud Cost Management, FinOps and DevOps teams can collaborate more efficiently to optimize cloud spend and strategically improve the cost efficiency of their services without impacting their customer experience. To learn how to get started with Cloud Cost Management, visit our documentation. If you’re not already using Datadog, you can sign up for a 14-day free trial.