Understanding cloud spend often means looking at more than one metric at a time, such as how cost relates to usage, list price, or savings.

You can now add multiple metrics, such as cost, usage, list price, and savings, to a single report. This makes it easier to validate data, reduce duplicate reporting, and deliver clearer insights without exporting or stitching together multiple views.

What this enables

• Compare cost, usage, and list price in one report
• Validate data more easily across metrics
• Reduce the need for duplicate or parallel reports
• Communicate insights more clearly to finance, engineering, and leadership
  
  

Getting started

• To add multiple metrics: open a new or existing report and select “+” next to Metrics

For more details, consult our Help documentation or raise a support ticket.

We’re excited to roll out a major upgrade to how DoiT helps you stay in control of your Google Cloud spend: real-time anomaly detection for Google BigQuery, including both on-demand and reservation workloads. Once enabled, you’ll receive alerts about unexpected BigQuery cost spikes in under an hour.

What’s new

Until now, anomaly insights for BigQuery relied on next-day billing-file ingestion. That meant if a bad query ran at 2 PM today, you wouldn’t know until tomorrow.

With our new real-time detection pipeline for BigQuery on-demand, we continuously ingest and analyze live BigQuery usage metadata, flag unusual usage patterns, and send you Slack or email alerts in less than an hour-not the next day.

Why it matters

Real-time detection for BigQuery means that you can:

• Catch runaway queries in minutes: Prevent accidental or inefficient queries from racking up costs before anyone notices.
• Protect against operational mistakes: Get alerted when abnormal query activity starts impacting spend.
• Strengthen your security posture: Real-time cost changes can signal unauthorized data access or compromised systems.

As DoiT’s BigQuery expert Sayle Matthews can attest, the risk of unchecked queries racking up significant costs in a short period of time is very high:

“One of the largest issues is seeing how much their bill is at any given moment and being able to alert them when a ‘runaway query’ hits. We have seen some examples where customers have single queries that cost $2,000 USD and run in less than a minute, and of course, these were run multiple times in quick succession. These mistakes lead to massive bills that aren't caught for days or weeks later.”

Watch this video for a more detailed breakdown of one such example of a runaway query that could have cost a customer thousands in unnecessary charges.

What you need to do

This feature is available for all DoiT customers with a paid Enhanced, Premium, or Enterprise subscription and a connected Google Cloud account. 

You can enable real-time anomaly detection for BigQuery when connecting a new project or editing an existing one. For a new project, take the following steps:

1. Sign in to the DoiT Cloud Intelligence console, select Integrate from the top navigation bar, and then select Google Cloud.
2. From the Connect drop-down, select Project.
3. Select the Real-time Anomalies – BigQuery checkbox. You can expand the feature to view its required permissions
4. Select Generate gcloud commands
5. Follow the instructions displayed in the side panel to configure your service account. See Connect a Google Cloud project for more information.
6. If the connection has been set up successfully, the status of the Real-time Anomalies – BigQuery feature will show Healthy.
   
   

To enable the feature on multiple projects, you'll need to repeat the process for each one. To add real-time anomalies to a connected project:

1. From the Integrate tab, select Google Cloud and find the project you want to enable it on.
2. Select the kebab menu (⋮) next to the project connection, and then select Edit.
3. Select the Real-time Anomalies – BigQuery checkbox to add the feature.
4. Select Generate gcloud commands.
5. Follow the instructions displayed in the side panel to update your custom role.
6. Select Done to enable the feature

Next steps

For more information about real-time anomaly detection, consult our Help documentation or raise a support ticket.

Data tells you what happened, but it doesn't always tell you why. We know that when you spot a cost anomaly or a usage spike in your cloud cost, usage or your business metrics, you need to capture that context immediately—before it’s lost in the shuffle of daily operations.

Starting today, you no longer need to navigate to a separate settings menu or lose your place in your analysis to leave a note. You can now create annotations directly from your reports by simply selecting a specific data point (using the right-click menu).

Manage custom metadata for your DoiT Cloud Intelligence™ resources with the new Labels API. Align governance objects, such as annotations, allocations, and reports, directly with your org structure, managed environments, cost centers, and other relevant entities.

Simple Taxonomy. Labels use single-value identifiers (like Production or Engineering) rather than complex key-value pairs. Filter and categorize resources faster with this flat structure.

Key Capabilities

• Centralized Management: Build a standard label library for your organization.
• Improve Governance: Label Annotations and Reports to properly classify them.
• Automate Workflows: Apply labels to new Reports or Annotations automatically during provisioning.

Supported Resources

• Annotations
• Allocations
• Reports
• Metric
• Budget
• Alert

API Reference

Google Cloud is updating its spend-based Committed Use Discount (CUD) program to a new data model that replaces credit-based offsets with direct discounted costs. 

In response, we have updated DoiT Cloud Analytics to fully support this new schema, ensuring your reporting remains accurate once Google Cloud automatically applies this change on existing spend-based CUDs beginning January 21, 2026.

This update simplifies billing by utilizing "Consumption Models" to represent discounted rates rather than applying separate credits to list prices. While your total costs will not increase, the structure of your billing data, and how CUD-related savings are displayed, will change.

What’s changing:

• CUD savings display: To align with Google’s new methodology, we are modifying how savings are displayed in Cloud Analytics Reports. Reports will shift from showing savings as an in-line calculation to a distinct savings presentation, reflecting the difference between on-demand prices and your final discounted cost
• Automatic SKU Conversion: Google Cloud will automatically convert your current CUD SKUs to the new ones. Consequently, any unique processing or presentation of your current SKUs within DoiT will be seamlessly transitioned to the new SKUs.

While this format is optional until Google applies it universally to all customers on January 21, 2026, you can opt into the new CUD format early via the Google Cloud console and start exploring your savings in DoiT Cloud Intelligence.

To learn more about the new CUD data model, submit a support ticket or reach out to your DoiT account team.

You use DoiT Cloud Intelligence™ to understand cloud spend and usage. The next step is making that insight repeatable and actionable in the systems where work actually happens: scripts, pipelines, and runbooks.

That's why we are introducing the DoiT Cloud Intelligence™ CLI, a straightforward way to interact with DoiT from your terminal and automate the work that keeps teams aligned and accountable.

With DoiT Cloud Intelligence CLI, you can:

• Shorter time from question to answer. Standardize recurring analyses and data pulls so stakeholders get consistent, up-to-date cloud cost reports without manual exports. 
• Reliable governance. Apply budgets, annotations, and allocation logic consistently across environments so ownership does not depend on one person remembering “the right way.”

For example, you can use Annotations directly from the CLI to capture the "why" behind the charts (deployments, migrations, incident response, policy changes), so anyone looking at a report later sees context immediately.

Practical examples:

• Create an annotation automatically when a production deployment completes, so cost deltas are clearly tied to releases.
• Add annotations when an incident is declared or resolved to connect reliability events to cost changes.
• Record business events (campaign launches, new customer onboardings) so spend is understood in business context, not just infrastructure terms.

Get started

If you can run a command in a terminal, you can get the value quickly. Install the CLI, authenticate, and then explore the available commands and actions. Docs and full command reference: https://help.doit.com/docs/cli

Start using this template by navigating to Operate -> CloudFlow -> Templates

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.

If you’re using Azure AI to build and run LLM-powered applications, you’ll now see those associated costs in GenAI Intelligence alongside any AI spend from other platforms you use.

GenAI Intelligence gives you a single, comprehensive view of AI costs and token usage across your AI stack, including other supported providers like Amazon Bedrock, OpenAI, and Anthropic Claude.

Azure AI usage will also populate GenAI labels, so you can easily build your own reports and allocations on top of this AI spend data. GenAI labels turn provider-specific data into consistent dimensions (like Model, Feature, Media Format) across your AI stack, making it much easier to break down GenAI costs and usage without digging through SKUs and services for each platform.

To get started, explore GenAI Intelligence and use GenAI system labels to explore and allocate your GenAI spend.

GenAI Intelligence is available on all DoiT Cloud Intelligence™ tiers.