Today, we are introducing the CloudFlow SQL node – a way to turn the data you already have into live FinOps and CloudOps automations, using the language your teams already know: SQL.

Instead of exporting data to a BI tool, building a dashboard, and hoping someone remembers to check it, you can now write a SQL query inside CloudFlow and wire the result directly into alerts, workflows, and policies across all of your clouds. The CloudFlow SQL node natively connects to our unified data lake, using the same normalized schema you already use in DoiT Cloud Intelligence™.

While we’re excited to see what you build using the SQL node in CloudFlow, we've helped get you started with several pre-built templates available in the CloudFlow template library (use the Source: Billing Data filter to quickly find them). 

These templates also require no configuration to start running them. Simply click one to open it, and hit publish to start generating meaningful alerts and workflows. Read on for more details about one example you can start implementing:

Example: Time-to-Tag Leakage Analysis (Tag Lag Drift)

Here's a common FinOps problem: even when teams consistently tag resources, tags often appear days after the resource starts incurring costs. The spend generated during that gap shows up as “untagged,” creating leakage that can’t be attributed to the correct owner or workload. Dashboards can display daily untagged spend, but they do not quantify the lag effect or identify services where the lag is worsening.

1. Scans billing data for each resource and computes the first timestamp where a non-system tag is present
2. Compares that timestamp to the resource’s earliest usage cost and calculates leakage per resource, per service, per day
3. Aggregates to daily untagged leakage, total untagged spend, and the percent of spend impacted by tagging delay
4. Computes trend direction (improving or degrading) over a defined window
5. Filters out services that do not support cost allocation tags to avoid noise

In CloudFlow, you can then:

• Trigger an alert only when leakage crosses a threshold or when the trend degrades week over week
• Notify owning teams of the specific services or resources driving the lag (or create a Jira task)
• Feed the data into an CloudFlow's LLM node to generate a human-readable explanation of what caused the degradation and which teams should act

This converts “some of our spend is untagged” into an operational metric with direction and ownership.

What you can build next

This example is only a starting point. With the CloudFlow SQL node, you can also design automations for:

• Budget guardrails and burn rate monitoring at the project, account, or business unit level
• Policy checks for label compliance are tied directly to your own label taxonomy
• Identification of noisy SKUs, high-cost regions, or underutilized data services
• Custom CloudOps SLOs driven by spend and usage patterns, not just uptime

If you already use DoiT Cloud Intelligence, the CloudFlow SQL node is available in your environment as a new node type inside CloudFlow. Start by taking one of your existing FinOps queries and turning it into an automated Flow that runs on a schedule, posts to Slack, opens Jira tickets, or chains into additional nodes such as LLM-based explanations.

Learn more about the SQL Node in DoiT Cloud Intelligence Help Center. We can also help you build your automation free of charge! Just ask at support.doit.com.

We’re excited to launch a new enhancement to Cost Anomaly Detection that gives you more control over the alerts you receive.

Until now, anomaly detection has automatically surfaced unexpected cost spikes. Today, you can fine-tune the experience with a new Sensitivity setting: a simple drop-down that lets you choose how aggressively we surface anomalies for each service in your cloud environment, including a change log that allows you to see and review all changes.

These settings will also be visible when you open a specific anomaly report:

Why this matters

Different services have different tolerance levels for noise. With sensitivity controls, you can now tailor alerting to match your operational needs – whether you want to catch every small fluctuation or focus only on the most material cost movements.

What you can do

• Adjust sensitivity levels to determine how strict anomaly thresholds should be
• Reduce alert noise in high-variance environments
• Prioritize only high-impact events when cost stability is strong
• Dial up sensitivity when monitoring new workloads or emerging spend patterns

Getting started

Head to your anomaly detection settings and select the Sensitivity option that best fits your use case. No configuration or data prep required, just choose from the new drop-down and your anomaly detection model adjusts automatically.

To learn more about the sensitivity controls, check out our Help documentation.

Data is powerful, but data with context is actionable. The new Annotations feature in Cloud Analytics closes the gap between your charts and the real-world events that shape them. Now, you can move beyond just what happened and show your entire team why it happened. Explain a sudden spike, mark a new feature deployment, or record a key decision. You can create Annotations directly on your reports, ensuring everyone who sees the data gets the full story.

How it works:

• Add an annotation with a date, label (e.g., infrastructure change, deployment, general note), and context
• Choose visibility: keep it to the current report or show on all reports
• See it one place with markers on time-series charts and indicators in tables 
• Hover over to read details, edit or remove

There’s no setup required. Open any report and click Add annotation in the top-right report toolbar to get started.

Cloud Diagrams automatically generates comprehensive infrastructure maps from your live cloud environment, providing both account/project-level architecture views and global network flow visualization.

And now, Cloud Diagrams supports Google Cloud, bringing automated infrastructure visualization, network troubleshooting, and always-current architecture documentation to Google Cloud environments.

Connect your Google Cloud projects to see real-time maps of your infrastructure, trace dependencies, and troubleshoot network issues across VPCs and regions. 

For multicloud teams, Cloud Diagrams unifies AWS and Google Cloud in a single automatically-generated map to simplify network troubleshooting and accelerate issue resolution.

Additional resources:

• Explore use cases in the blog post
• Watch our how-to videos
• Read the documentation
• Take a step-by-step tour for setting up Cloud Diagrams

Are you using multiple GenAI service providers? The GenAI Lens gives you a comprehensive view of all your AI costs and usage across supported providers like OpenAI and Anthropic Claude, with support for Amazon Bedrock and Google Vertex AI coming soon.

Instead of switching between separate views or piecing together data from different sources, you can use the GenAI Lens to:

• Compare costs across different GenAI providers to identify cost-effective options for your use cases
• Track your GenAI spend trends across all connected providers in one place
• Identify usage patterns that indicate opportunities to optimize your provider and model mix
• Get token usage insights across all your connected GenAI services

To start understanding your complete GenAI picture, connect your OpenAI account(s) and/or Anthropic organization(s).

The GenAI Lens is available for all customers on the DoiT Cloud Intelligence Enhanced or Enterprise tiers, as well as on the DoiT Cloud Navigator Enhanced, Premium or Enterprise tiers.