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.

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 on-demand. For the first time, 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

As DoiT’s BigQuery expert Sayle Matthews will tell you, 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.”

Real-time detection for BigQuery on-demand 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.

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. To enable it, take the following steps:

1. Locate the service account of interest on the Google Cloud access & features page.
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

Enable BQ real-time anomaly detection

Next steps

Up next, we’ll be releasing the same support for BigQuery reservations to bring the same real-time intelligence across your full BigQuery footprint.

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

For customers sending (or planning to send) Snowflake cost & usage data to DoiT Snowflake Intelligence, key-pair authentication is now supported and required for new connections, and existing password-based LEGACY_SERVICE users must migrate by June 2026.

Snowflake is deprecating password-only LEGACY_SERVICE users, with full removal planned for June 2026. In response, we’ve added support for key-pair authentication. This removes passwords from the flow and gives you stronger security and a cleaner audit trail for your Snowflake connection.

What this means:

• Starting today, key-pair authentication is the only way you’ll be able to set up the Snowflake Intelligence
• If you’ve already set up the integration with a LEGACY_SERVICE user, migrate to key-pair authentication before June 2026 to avoid interruption

To get started, view our documentation on setting up and updating your Snowflake connection.

You can now use DoiT's Agentic FinOps AI, directly from any Insight detail page to get guided analysis and ready-to-use actions without leaving the view.

• Estimate the impact of implementing the Insight, including cost savings and business or technical implications based on the context.
• Break down the remediation tasks with a step-by-step plan tied to the affected resources.
• Surface downstream dependencies & hidden costs, callouts for related risks or follow-up work to expect.
• Estimate effort to execute, with time/complexity guidance that’s persisted in the UI for later reference.
• Generate a Terraform configuration, then one-click copy option for the snippet to streamline applying the fix.

Why it matters:

• Cut MTTR by getting impact, steps, and effort in seconds.
• Reduce handoffs and tabs. Everything you need lives beside the evidence.
• Improve consistency. AI gives repeatable plans and Terraform you can reuse.

No configuration changes are needed. To get started, select the AI bubble in the bottom right corner of any Insight detail page.

MongoDB provides teams with powerful elasticity, but that flexibility often comes with fragmented visibility into what drives costs. MongoDB Intelligence brings structure to that chaos.

This new module ingests Atlas billing and usage data across organizations, projects, and SKUs, turning raw cost exports into an actionable view of your Atlas estate. You can instantly see how spend breaks down across clusters, backups, and storage, identify which projects are trending up or down, and trace deltas to specific Atlas SKUs like ATLAS_AWS_INSTANCE_* or ATLAS_BACKUP_*.

Analytics for organization-level costs, project distribution, and SKU analytics reveal patterns previously hidden in CSVs, helping FinOps and engineering teams align on the same data when evaluating scaling, tiering, or retention decisions.

On the right, you’ll find an Agentic FinOps AI assistant. Ava isn’t just a chatbot; it’s a reasoning layer that interprets the same data visible on the dashboard. Ask questions like “Which projects or SKUs changed the most this month?” or “Where am I overspending on backups?”, and Ava will analyze cost trends, isolate anomalies, and suggest next best actions.

Setting up MongoDB Intelligence takes only a few minutes — connect your Atlas organization, and DoiT Cloud Intelligence™ will automatically ingest cost and usage data. Follow the step-by-step guide in our Help Center article to enable the integration securely and start visualizing your MongoDB Atlas spend with zero manual exports.

MongoDB Intelligence extends the DoiT Cloud Intelligence™ platform’s FinOps coverage to Atlas, providing your teams with a precise, explainable, and actionable understanding of MongoDB spend. MongoDB Intelligence is available on all DoiT Cloud Intelligence™ tiers

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.

You can now track detailed costs and token usage for Google Vertex AI and Databricks GenAI workloads in GenAI Intelligence. GenAI Intelligence gives you a single, comprehensive view of AI costs and token usage across your stack, including other supported providers like Amazon Bedrock, OpenAI, and Anthropic Claude.

For Databricks, coverage includes:

• Databricks-hosted foundation models
• Custom models on Mosaic AI Model Serving
• Model serving–related network egress
• SQL AI Functions usage

Additionally, cost and token data for these AI providers will populate in GenAI system labels. These system labels standardize your GenAI data into consistent categories (ex. Model, Usage Type, Media Format) across all supported GenAI providers, making it easier for you to drill down into your GenAI costs and usage across providers without going through service-specific SKUs and resources.

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

Note: In order to view Databricks GenAI workloads in GenAI Lens, you’ll need to first connect your Databricks account to DoiT Cloud Intelligence.

Tracking and monitoring commitments can be hard, especially when you’re juggling multiple contracts and need quick, consistent updates. 

We’ve added two ways to keep commitment progress front and center without needing to log in or dig through reports.

What’s new:

• Subscribe to commitment updates. Receive a regular summary of your commitment’s progress via email or Slack, including spend to date, remaining value, and forecast. Set your preferred schedule, time zone, and frequency. The digest keeps you continuously informed, automatically.
• Add commitments to dashboards. Gain visibility where you need it by adding any Commitment Manager widget directly to your dashboards. Track spend and performance alongside your other key metrics, and jump into the full commitment view with one click.

To get started, go to Monitor → Commitment Manager, or Dashboards → Add Preset Widget.

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