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

Security and engineering teams shouldn’t need five tabs to find one problem. Security Insights is now available in the DoiT platform, bringing your security findings into a single, prioritized view so you can focus on what matters.

With Security Insights, you can:

• Group & de-duplicate findings across sources into one unified Insight for less noise, and no double work.
• Prioritize what to fix first with a Priority Score that weighs spend patterns, account/tag context, resource criticality, source severity, and known false positives.
• Use the right lens for the job with pivots by Insight, Resource, or Account - plus fast filtering and drill-downs to affected services and ownership.
• Investigate and act in one place on the Insight Detail page. Review evidence and remediation steps, then Contact a DoiT expert or Create a Thread to open a Jira ticket.

Initial sources: Wiz and AWS Security Hub (we preserve original vendor context and fixes).

Get started
Go to Optimize → Insights, then under Insights coverage → “See what you are missing” to connect Wiz and AWS Security Hub. Review your grouped Insights, tweak Priority Score weights, triage top items, and trigger actions directly from the detail page.

DoiT Cloud Intelligence™ now includes Kubernetes Intelligence, giving you visibility into Kubernetes cost and resource utilization alongside your cloud billing data. 

By integrating utilization metrics collected by DoiT PerfectScale technology, Kubernetes Intelligence provides a comprehensive dashboard of K8s cost and utilization data across AWS and Google Cloud environments, helping you monitor spend and plan capacity more effectively.

What’s new:

• Preconfigured dashboard showing aggregated optimization metrics that highlight areas for improvement
• Per-cluster drilldowns into CPU, memory, and GPU utilization to track waste and efficiency
• Utilization data is also available in Cloud Analytics for custom reports and dashboards
• Seamless integration with the PerfectScale platform to get specific optimization recommendations or autonomous remediation actions

Powered by the PerfectScale agent, Kubernetes Intelligence is quick to enable. Once the agent is deployed in your environment and connected to your clusters, DoiT Cloud Intelligence automatically surfaces utilization data in the dashboard, highlighting areas for optimization. 

And if you want specific recommendations or autonomous remediation actions, you can seamlessly activate a free trial of PerfectScale without needing to reinstall the agent. Click here to learn more about starting a PerfectScale trial.

Kubernetes Intelligence works alongside existing EKS and GKE dashboards (with a roadmap to consolidate later this year).

To learn more about Kubernetes Intelligence, please consult our documentation in the DoiT Help Center. 

We're excited to introduce Connections, a new way to securely manage how CloudFlow accesses your cloud resources.

What’s new

• Role-based access control (RBAC): Define Owner, Editor, and User roles to control what users can do in CloudFlow.
• Reusable roles and permissions: Configure a small set of roles once and apply them across multiple flows, eliminating the need to manage permissions for each individual workflow.
• Separation of duties: Users who create or execute a flow don’t need elevated cloud permissions; the automation runs with the Connection role’s permissions only.
• Improved governance and auditability: Every action taken by a CloudFlow is tied to a defined role, reducing the risk of excessive privileges and simplifying compliance reviews.

Why it matters

Connections provide the foundation for scaling FinOps automation. They make it easier to roll out new flows, enforce security best practices, and maintain confidence that your automations are running with the right level of access – no more, no less.

To learn more about Connections, check out our Help documentation, or click here to see a step-by-step demo for how to set up new Connections.

Are you building GenAI features and services with Amazon Bedrock? 

You can now track detailed AWS Bedrock cost and usage information in GenAI Intelligence (formerly “GenAI Lens”), which gives you a comprehensive view of all your AI costs and usage across supported providers like Bedrock, OpenAI, and Anthropic Claude.

Additionally, your Amazon Bedrock cost and usage data is now included in GenAI system labels, which categorizes your GenAI data into consistent categories (ex. Model, Usage Type, Media Format) across all supported GenAI providers. 

This makes it easier for you to drill down into your GenAI costs and usage across providers without having to filter through service-specific SKUs and resources.

To get started, explore GenAI Intelligence and start using GenAI system labels in reports to drill down into your GenAI spend across reports and allocations.