Some workflows need a deliberate delay to avoid API throttling or to wait for changes to propagate. For example, if you’re looping through cloud resources or applying IAM updates, a short pause can prevent rate-limit errors and reduce noisy retries.

A common operational pattern is also dev environment scheduling: start in the morning, run all day, then shut down in the evening. Sleep lets you build this into a single flow without external schedulers.

What’s new

You can add a Sleep node to pause a CloudFlow run for a configurable duration, then automatically resume execution. This helps you:

• Space out provider API calls to reduce throttling risk
• Add cooldown periods before re-checking or notifying
• Wait for eventual consistency (for example, new resources or IAM changes becoming effective)
• Orchestrate time-based actions in one flow, like dev environment start and end-of-day shutdown

How it works

When a run reaches the Sleep node, CloudFlow marks the run as Sleeping and pauses execution until the configured time elapses. After the duration completes, CloudFlow wakes the run and continues to the next node.

How to get started: https://help.doit.com/docs/operate/cloudflow/nodes/sleep

As CloudFlows grow, the same steps often get copied into multiple workflows. That makes fixes slower and outcomes inconsistent, because you end up updating the same logic in several places.

Use-case example: you run a daily workflow that detects cost anomalies, then triggers CloudOps remediation. Instead of duplicating “ownership resolution” and “notification formatting” steps across every anomaly flow, rightsizing flow, and incident flow, you put that logic in a single subflow and call it from each parent workflow, so updates propagate everywhere.

What’s new

• You can now use the Sub Flow node to run one CloudFlow from inside another, so shared logic lives in a single reusable flow.
• Parent flows can pass inputs as parameters (mapped to the sub flow’s local variables), so the same sub flow works across multiple use cases.
• Each invocation creates a child run you can open from the run history, making troubleshooting and auditing easier.

You build a reusable flow (the sub flow) and publish it. In your main workflow, add a Sub Flow node, select the flow you want to call, and provide the parameter values for that run. When the parent reaches that node, CloudFlow runs the selected flow as a child run and returns its output back to the parent so the workflow can continue.

Getting started

• Create the reusable flow you want to call and define the local variables it should accept.
• In the parent flow, add a Sub Flow node and select the flow to call from the Flow drop-down.
• Map parameters, then use the Sub Flow node output in downstream nodes.

Read more about subflows in CloudFlow Help Center.

CloudFlow notification nodes now support threaded Slack messages — post a summary to the channel and automatically follow up with detailed information in the thread.

What's new

When configuring a Slack notification node, you'll see a new Thread Message field below the main message. If you add content there, CloudFlow will:

1. Post your main message to the Slack channel
2. Automatically reply in a thread with the detailed follow-up

If you leave the thread message empty, notifications work exactly as before — just a single message to the channel.

Why use threads?

• Keep channels clean — post a brief alert to the channel, then put the full breakdown (cost tables, resource lists, detailed findings) in the thread where those who need the details can find them
• Organize related data — separate the "what happened" summary from the "here's everything you need to know" details
• Reduce noise — team members see the headline without being overwhelmed by data-heavy content

Full message formatting support

Both the main message and the thread message support all of CloudFlow's rich formatting options:

• Tables — rendered as native Slack tables with bold headers
• Lists — bullet points with nested indentation
• Buttons — clickable action buttons with URLs
• Images — embedded image blocks
• Links — inline clickable links

How to get started

1. Open any Slack notification node in your CloudFlow
2. Write your main channel message as usual
3. Add your detailed content in the new Thread Message field
4. Save and test — you'll see both messages appear in Slack, neatly organized

This is a fully backward-compatible addition. Your existing notification nodes continue to work without any changes.

Metrics is one of the most powerful tools in your FinOps toolkit. This latest release brings substantial usability improvements that make building, understanding, and acting on your data faster and easier than ever.

New Allocation previews give you clear visibility into the individual building blocks that make up your Metric, so you can understand exactly what's contributing to your data, not just the end result. This means no more jumping back and forth between Allocations and Metrics to piece together the full picture; everything you need is right there in context.

AI-powered allocation creation takes the manual effort out of the process. Instead of building allocations from scratch, you can now express your intent and let AI generate them for you; a meaningful step toward a more intelligent, streamlined workflow.

We've also made it easier to manage Metric variables. You can now remove variables you no longer need, and the experience for adding new ones has been streamlined so it gets out of your way.

Rounding out the update: direct links to use a metric in reports mean fewer clicks between insight and action, and improved interactions around generating previews make the whole experience feel more responsive and fluid.

As your CloudFlow flows grow in complexity, keeping context about why a flow was built, what it monitors, and how to modify it becomes essential. Instructions live alongside the workflow itself — so the documentation is always where your team needs it, not in a separate wiki or document.

You can now write rich-text instructions directly inside your CloudFlow flows — making it easy for your team to understand what a flow does, how to configure it, and what to expect.

What's new

Every CloudFlow now has a dedicated README node visible at the top of the editor canvas. Click it to open a side panel that displays your flow's documentation, or click Edit to open a full markdown editor where you can write and format your instructions.

The instructions editor supports full Markdown, so you can structure your documentation with headings, bullet points, code snippets, bold/italic text, and more — everything you need to clearly explain your workflow's purpose and logic.

Link directly to nodes

Use the special $nodes["Node Name"] syntax to create clickable references to specific nodes in your workflow. When someone reads your instructions and clicks a node reference, the editor automatically navigates to and highlights that node on the canvas. You'll also get autocomplete suggestions as you type, so you don't need to remember exact node names.

Always accessible

• README node on the canvas — a dedicated node is always visible at the top of your workflow, giving you one-click access to the instructions panel
• Top bar menu — you can also open the instructions editor from the workflow's top bar
• Side panel — read your instructions without leaving the graph view, in a convenient slide-out drawer

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.