Datadog Cloud Cost Management
for Engineering Teams
Cloud cost and observability are the same conversation. Datadog Cloud Cost Management brings AWS and Azure spend into the same platform as your metrics, traces and logs, so engineering teams can see cost where they already work.
What Datadog Cloud Cost Management helps teams understand
Most engineering teams work with two entirely separate systems for cost and performance: the AWS or Azure billing console on one side, their observability platform on the other. When a service starts costing more, or when a deployment changes spending patterns, correlating the two requires manual effort across tools that were never designed to speak to each other.
Datadog Cloud Cost Management closes that gap. It ingests cost and usage data from AWS and Azure and makes it available alongside your infrastructure metrics, APM traces and logs. A cost spike appears in the same timeline as the deployment that caused it. A service's cloud spend appears next to its latency and error rate. Attribution is built in, not bolted on.
For platform and SRE teams, this means they can prioritise cost optimisation work the same way they prioritise reliability work: with data, context and a clear view of what is driving the numbers.
How cloud cost and observability cost overlap
There are two dimensions of cost for any team running Datadog on AWS or Azure. The first is the cloud infrastructure cost: compute, storage, data transfer, managed services. The second is the Datadog platform cost: the logs, metrics and traces generated by that infrastructure and the applications running on it.
These two dimensions are not independent. A decision to run more Kubernetes nodes adds cloud cost and Datadog infrastructure cost simultaneously. A verbose logging configuration adds Datadog log cost that does not appear in the cloud bill at all. Cloud Cost Management makes the first dimension visible inside Datadog; Observability FinOps governance addresses the second. Both matter. Treating them in isolation leaves gaps in your cost governance.
AWS, Azure, Kubernetes and service-level visibility
Datadog Cloud Cost Management surfaces cost across the layers of your infrastructure, from cloud accounts down to individual services and deployments.
AWS
AWS cost in Datadog
Ingest AWS Cost and Usage Reports into Datadog to see spend by service, account, region and tag. Correlate EC2, RDS, EKS, S3 and other service costs with the performance metrics for the workloads running on them. Cost changes surface alongside configuration changes and deployments in the same timeline.
Azure
Azure cost in Datadog
Azure cost data from Azure Cost Management is ingested into Datadog alongside your Azure infrastructure observability. Teams running on Azure or operating across both AWS and Azure can manage cloud spend from a single platform rather than switching between cloud billing consoles and observability tools.
Kubernetes
Kubernetes cost allocation
Kubernetes makes cost attribution harder by sharing compute across workloads. Datadog Cloud Cost Management includes Kubernetes cost allocation, which distributes compute and node cost to the namespaces, deployments and pods that consume it. Teams can see what each application actually costs to run, not just what the cluster costs.
Tagging and cost allocation
Cloud cost attribution is only as good as the tags applied to your resources. Getting this right is a prerequisite for meaningful cost governance.
Why tagging is the foundation
Datadog Cloud Cost Management uses resource tags to attribute cloud spend to teams, services, environments and products. Without consistent tagging (team, service, environment and cost-centre at minimum) spend cannot be broken down below the account level. A tagging standard enforced across AWS and Azure is a prerequisite for meaningful attribution, not a nice-to-have.
Designing a tagging standard
A good tagging standard is one that teams can apply consistently without ambiguity. It covers the tags needed for cost allocation (team, service, environment, cost-centre), the tags needed for operational context (owner, application, criticality) and a process for enforcing them on new resources. We help teams design and implement tagging standards as part of cost governance engagements.
Chargeback and showback
Once tagging is in place, Datadog Cloud Cost Management supports chargeback (allocating actual costs to business units) and showback (reporting costs without direct allocation, for transparency). Either model gives engineering teams and budget owners a shared view of what infrastructure costs and who is responsible for it.
Rightsizing from cost data
When cloud cost data and infrastructure performance metrics are in the same platform, rightsizing decisions become data-driven. A service with low CPU utilisation and high spend is a rightsizing candidate that is visible from the cost side, not just the performance side. Platform teams can prioritise optimisation work using cost data alongside the reliability signals they already monitor.
Cost anomalies and spend governance
Cloud cost anomalies (unexpected spend from a new resource, a misconfigured service, a data transfer pattern that changed) are far easier to address when they are caught early. Left undetected, they compound across billing periods. Datadog Cloud Cost Management supports cost anomaly detection that alerts when spend deviates from expected patterns.
Anomaly alerts in Datadog follow the same routing and notification patterns as reliability alerts: they can go to Slack, PagerDuty, email or any other notification channel your team already uses. Cost governance becomes part of the operational loop rather than a separate monthly review.
What anomaly detection catches
- A new service or resource provisioned outside normal deployment patterns
- Data transfer costs that spike following a configuration change
- A team's spend growing faster than expected during a sprint or release cycle
- Storage or compute left running from a terminated project
- On-demand usage exceeding committed capacity in ways that trigger higher rates
Full guide
Datadog pricing and cost optimisation
The complete guide to Datadog cost governance: all cost drivers, the Observability FinOps framework, optimisation levers and the 30-day review plan.
Read the full guideRelated Datadog cost guides
Frequently asked questions
What is Datadog Cloud Cost Management?
Datadog Cloud Cost Management is a Datadog capability that ingests cost and usage data from AWS and Azure and presents it alongside your observability metrics, traces and logs. Engineering teams can see cloud spend in the same context as performance and reliability data, making it possible to attribute cost to services, teams, deployments and environments without switching tools.
How does Datadog Cloud Cost Management help with AWS cost?
By ingesting AWS Cost and Usage Reports and connecting them to your infrastructure observability data, Datadog Cloud Cost Management lets you see which services, teams and accounts drive AWS spend, correlate cost changes with deployments and configuration changes and set anomaly alerts when spend deviates unexpectedly. It removes the gap between the AWS billing console and your engineering observability platform.
Does Datadog Cloud Cost Management work with Azure?
Yes. Datadog Cloud Cost Management supports Azure cost data alongside AWS, allowing teams running on Azure or operating multi-cloud environments to see spend from both providers in a single view correlated with their observability data.
How does tagging affect cloud cost attribution in Datadog?
Cloud cost attribution in Datadog depends on the tags applied to your cloud resources. Without consistent tagging (team, service, environment, cost-centre) it is not possible to break down a cloud bill by engineering unit. Establishing a tagging standard and enforcing it across your AWS and Azure environments is a prerequisite for meaningful cost attribution, and it is one of the first things we address in a cost governance engagement.
See your cloud cost in context
We set up and operate Datadog Cloud Cost Management as part of your wider Datadog practice: attribution, anomaly detection and cost governance built in from the start.