You can now format numbers, dates, bytes, and percentages directly inside your CloudFlow Notification node — making Slack and email messages clearer and more professional without any extra workflow nodes.

How it works

Wrap any referenced value in double curly braces with a formatting function:

{{ format(diskSize, "#,##0.00") }}

That's it. When the notification fires, raw values like 1234.5 become 1,234.50.

Available functions

Numbers & currency — Use Excel-style patterns to add thousands separators, decimals, or currency symbols:

• format(val, "#,##0.00") → 1,234.50
• format(val, "$#,##0") → $1,234

Percentages — Automatically multiplies by 100 and adds the % sign:

• format(val, "0.0%") → 85.6%

Bytes — Automatically picks the best unit (B, KB, MB, GB, TB):

• bytes(val) → 5.49 GB
• bytes(val, 1) → 5.5 GB (control decimal places)

Dates — Parse timestamps (Unix seconds, milliseconds, or ISO strings) and reformat them:

• date(val, "yyyy-mm-dd") → 2026-02-12
• date(val, "unix") → convert back to Unix timestamp

Text — Change case:

• upper(val) → HELLO
• lower(val) → hello

Inline math — Perform calculations on the fly:

• {{ val * 5 }} → 500

Multi-value support

When a referenced field returns multiple values (e.g., disk sizes across several VMs), each value is formatted individually and joined with commas:

{{ bytes(7665791702, 6046802307, 1) }} → 7.7 GB, 6.0 GB

Resilient by design

If one expression in your message has a formatting error, only that expression falls back gracefully — the rest of your notification renders normally.

In-editor reference

A new Formatting tab in the template tags dialog gives you a quick reference of all available functions with live examples, so you don't need to memorize the syntax.

Every CloudFlow trigger (scheduled, webhook, and system event) now automatically attaches a managed list of AWS accounts, Google Cloud projects and Azure subscriptions (e.g., "billing scopes"). All new and existing flows now expose  billingScopes - a structured variable containing all your billing scopes, grouped by provider. A companion billingScopesRowCount variable gives you the total count across all providers.

Why It Matters

Previously, if your flow needed to iterate over or reference your cloud billing accounts (e.g., to generate per-account reports, enforce policies across accounts, or conditionally branch based on which providers are active), you had to make separate API calls or hardcode account IDs. Now, this data is available out of the box as part of the trigger context; hence, no extra nodes are needed.

How It Works

When a CloudFlow executes, the trigger node automatically calls the DoiT Cloud Intelligence™ API and populates two new variables:

1. billingScopes: Contains three lists: amazon-web-services, google-cloud, and microsoft-azure. Each entry has id and name
2. billingScopesRowCount: Total number of billing scopes across all three providers

These variables are available for reference in any downstream node — use them in transformations, conditions, API call parameters, or code nodes just like any other trigger variable.

Example Use Cases

• Multi-account flows — loop through billingScopes["amazon-web-services"] to generate cost reports per AWS account.
• Provider-conditional logic — check billingScopesRowCount or the length of a specific provider's list to branch your flow based on which cloud providers are active.
• Dynamic API calls — use billing scope IDs as parameters when calling provider-specific APIs within your workflow.

The CloudFlow Widget is a new dashboard widget type that lets you visualize data from your CloudFlow and Datastore tables alongside your existing dashboards and reports. Build operational dashboards from the data your CloudFlow flows already produce — all without leaving the platform.

First, if you're not familiar with CloudFlow, it is a no-code FinOps automation platform. It is fully integrated with DoiT Cloud Intelligence™ to automate cost optimization opportunities and enforce governance in real time. You can think of it as a platform that automates repetitive FinOps tasks.

What's New

You can now add one or more CloudFlow widgets to any dashboard. Each widget connects to a CloudFlow node or Datastore table and renders the data in the visualization of your choice:

• Table — sortable, column-selectable data grid
• Column / Stacked Column — grouped or stacked bar charts with dual-axis support
• Line — trend lines over any dimension
• Combo (Column + Line) — mix bar and line series on the same chart
• Metric — a single headline number with sparkline trend and percentage change

Configuration is fully guided — select your data source, pick a display type, and the widget walks you through the rest. Widgets auto-refresh and show a "last updated" timestamp so your data is always current.

Use Case: Tagging Hygiene Dashboard

Imagine you have a CloudFlow that runs daily to analyze your cloud billing data for untagged resources. With the new widget, you can build a complete Tagging Hygiene dashboard:

1. "Tagging Coverage Change (30d)" — a single headline number showing how many percentage points your tagging coverage shifted over the last 30 days, with a sparkline trend and a ∧/∨ indicator so you know immediately if coverage is improving.
2. "Taggable vs. Untagged Costs" — a grouped bar chart comparing taggable and untagged costs for the current 30-day window against the previous 30 days, so you can see whether untagged spend is shrinking.
3. "Untagged Share %" — a trend line showing the percentage of spend that remains untagged, current vs. previous period.
4. "Time-to-Tag Leakage" — a sortable grid listing accounts with persistent tagging gaps: days with leakage, last leak date, max daily leak, and total untagged cost per service.
5. "New SKUs w/o Tags" — another table surfacing newly discovered SKUs that have no tags, with project ID, first-seen date, SKU description, and 7-day cost.

All five widgets appear on a single dashboard, even though their underlying data comes from different flows. They auto-refresh, giving your FinOps team a self-updating command center for tagging compliance, built entirely from a CloudFlow you already have.

Getting Started

1. Open any dashboard and click Edit → Add Widget
2. Select CloudFlow from the widget list
3. Choose your data source (Flow Step or Datastore Table) and a display type
4. Save — your data appears immediately

You can add as many CloudFlow widgets as you need, each connected to a different flow, step, or table.

All multicloud recommendations in one place – now including Azure Advisor

DoiT Insights now also includes Azure Advisor recommendations, alongside AWS Trusted Advisor, AWS Compute Optimizer, Google Cloud Recommender, and DoiT Cloud Intelligence™ Deep-Waste Insights recommendations, giving teams a single place to manage optimization across every cloud.

Instead of jumping between provider consoles, you can now identify, prioritize, and act on multicloud optimization opportunities from one unified experience.

With this update, Insights brings all recommendations together into a normalized, multicloud view, including:

• Azure Advisor
• AWS Trusted Advisor and Compute Optimizer
• Google Cloud Recommender
• DoiT Cloud Intelligence™ Deep-Waste Insights

All recommendations are categorized consistently across cost optimization, performance, reliability, security, and operational excellence, making it easier to compare impact and focus on what matters most.

But Insights shouldn’t just stop at recommendations, either. As with all of your existing Insights, Azure Advisor Insights allow you to take immediate steps to investigate and remediate the problem through three potential avenues:

• Launch an automated flow to automatically take the recommended action
• Create a Jira ticket to assign the task within your engineering team
• Raise a support ticket to get help from a DoiT Forward Deployed Engineer

Enabling Azure Advisor Insights

You can enable the Azure Advisor integration in your environment from the ‘Integrate’ tab in the DoiT Cloud Intelligence console. 

To learn more about the process, see our Help documentation, or raise a support ticket to speak to a DoiT expert.

Maintaining a clear record of infrastructure changes is critical for security audits, compliance, and general troubleshooting. Cloud Diagrams’ Version History delivers this by logging every resource creation, update, or deletion in your diagram.

We’ve introduced several updates to Version History, making it easier to find the infrastructure changes you’re looking for:

• Grouped service views: Changes are now categorized by cloud service (e.g., Compute Engine), allowing you to expand these groups to see specific resources. Additionally, clicking the name of a created or modified resource will take you directly to it on your diagram.
• Tag & label filtering: You can now filter version history by tags or labels to isolate modifications for specific environments, teams, or projects.
• In-resource history: When you select a resource in your diagram, a new History tab in the properties panel provides a timeline of every modification made to that specific resource, such as IP address or ID changes.

To get started, open a diagram to explore your infrastructure’s change history, read the documentation, or watch the short demo here.

Whether you're troubleshooting a performance alert, assessing the impact of a planned change, or auditing your security posture, understanding a resource’s context is essential. 

Cloud Diagrams’ Investigate feature delivers this visibility by visually isolating a resource and its dependencies in-diagram so you can:

• Trace root causes of cost spikes or performance bottlenecks.
• Analyze the impact of planned infrastructure modifications before you deploy.
• Audit security reachability to identify over-permissive paths between resources.

We’ve introduced two updates to improve root-cause and impact analysis in Cloud Diagrams with Investigate:

1. Extended blast radius: Previously, using Investigate would show you resources directly connected to your selection. You will now see second-order dependencies — connections of your selection’s connections — to visualize a wider impact area.
   
   Additionally, this view now maps potential reachability by analyzing your security and firewall rules, showing you which cloud resources can communicate with your target resource.
   
   
2. Search-to-investigate: Before, starting an investigation required manually locating and selecting a node on your diagram. Now, you can also enter the resource name, IP address, or any other unique identifier in the Investigate search bar to locate and isolate that resource and its dependencies.

To get started, open Cloud Diagrams and start exploring your cloud resources and their dependencies, read the documentation, or watch the short demo video here.

Infrastructure diagrams can get crowded, making it hard to find what matters. 

Filters in Cloud Diagrams help you cut through the noise and isolate specific resources, whether you’re hunting for a root cause during an incident or focusing solely on the infrastructure your team manages.

We’ve introduced two major updates to make filtering in Cloud Diagrams even more powerful:

• Saved filters: Save and name your filtered views to jump back into a specific context instantly without re-creating filter criteria from scratch.
• Advanced logic: Mix “AND” and “OR” logic for more surgical filtering. New search options like regex and exclusions make it simple to show (or hide) the exact services and tags/labels you're looking for.

Ready to try it out? Open Cloud Diagrams and save your first filter, or watch the 2 min. demo video here.

Allocations let you map cloud costs to the right teams and stakeholders, ensuring every dollar is assigned a clear owner. This transparency is the foundation for driving true cost accountability across your company. 

And now, building allocations in DoiT Cloud Intelligence is as simple as having a conversation. You can create complex allocations effortlessly using natural language prompts in your Agentic AI side panel.

For example, you can write a prompt like:

Allocate my spend using the values in the 'app' tag to define the groups. For each group, also capture resources in any Google Cloud projects or AWS accounts that contain those same application names.

And as shown below, using the above prompt we can build an allocation group consisting of nine allocations, one for each of our apps.

To get started, try your first prompt in DoiT Cloud Intelligence or watch our 2-min. demo video of this in action:

For too many organizations, the 'modern data stack' has become a financial black box. Today, we are changing that by acquiring SELECT, the premier platform for optimizing Snowflake performance and costs.

Snowflake costs grow fast when warehouses, clusters, and queries are not continuously tuned. PerfectScale for Snowflake (formerly SELECT) brings automated, workload-aware optimization directly into DoiT Cloud Intelligence™.

Get clear recommendations to right-size warehouses, reduce idle spend, and improve query efficiency without tedious manual tuning. See cost impact before you apply changes and keep teams in control with human-in-the-loop workflows. Read more about PerfectScale for Snowflake here.

Want to see it on your own Snowflake account? Book a live demo with the team that built it.

Book a private demo

Cloud Diagrams now supports the following cloud resources:

• Pub/Sub topics & subscriptions.
• Google Kubernetes Engine (GKE) namespaces
• Google Cloud Firewall rules
• Google Cloud service accounts
• Google Cloud Filestore instances
• Google BigQuery datasets
• Amazon RDS Proxy
• Amazon RDS clusters

These additions give you a more complete view of your infrastructure, allowing you to spot dependencies and troubleshoot connections that were previously missing from your diagrams.

Here’s how you can start taking advantage of these newly-visible resources:

1. Google Cloud Pub/Sub: Visualize topics, subscriptions, and message flows between services and event triggers.
2. GKE Namespaces: Trace network paths between GKE Namespaces and Load Balancers to clearly understand traffic flow and service connectivity.
3. Google Cloud Firewall rules: Validate security boundaries and troubleshoot connectivity by visualizing how firewall rules allow or block traffic between instances and external services.
4. Google Cloud service accounts: Keep track of where your service accounts are assigned and what they have access to.
5. Google Cloud Filestore instances: Track storage connections to see which instances or clusters are mounted to your file shares.
6. BigQuery Datasets: Map data dependencies to clearly see which services and projects interact with your BQ datasets.  
7. RDS Proxy: Verify traffic routing between your applications and RDS instances via the intermediate proxy layer.
8. RDS Clusters: See the direct connections between your database nodes to understand your failover and scaling setup.

Open Cloud Diagrams to start seeing these new cloud resources in the context of your broader architecture.