We’re excited to share that DoiT Cloud Intelligence™, our intent-aware automation-first FinOps platform, has earned the “Deployed on AWS” designation on AWS Marketplace. This confirms that our SaaS offering is fully hosted on AWS and aligned with the latest AWS Marketplace eligibility requirements.

Starting May 1, 2025, AWS requires that SaaS products be entirely deployed on AWS and carry the “Deployed on AWS” badge to qualify for spend retirement under Private Pricing Agreements (PPA). DoiT Cloud Intelligence™ meets these new criteria, meaning customers can continue retiring their PPA commitments when purchasing DoiT Cloud Intelligence via AWS Marketplace and retain the financial benefits included in those agreements. This update makes it even easier for AWS customers to adopt DoiT Cloud Intelligence while maximizing the value of their AWS commercial terms.

Make the cloud work. Without slowing down.

DoiT Cloud Intelligence™ is purpose-built for fast-moving teams operating in complex AWS environments. It provides real-time cost visibility, optimization insights, and policy-based automation to reduce waste and enforce standards at scale, without adding friction to your engineering workflows.

With CloudFlow™, you can codify FinOps practices, automate remediations, and integrate with your tooling, all while respecting your existing AWS architecture and operational velocity. DoiT Cloud Intelligence™ also includes real-time anomaly detection to catch cost spikes as they happen, Threads™  to drive accountability by operationalizing FinOps across teams, and PerfectScale™ to ensure your Kubernetes, Compute, and Spot infrastructure continuously self-optimizes for efficiency, performance, and availability.

Simplified procurement, aligned incentives

Through AWS Marketplace, customers benefit from faster onboarding, reduced procurement overhead, and the ability to align DoiT Cloud Intelligence spend with their existing AWS PPA, with full compliance under AWS’s updated guidelines.

What’s next

We’ll continue to deepen our integration with AWS-native services, expand automation capabilities, and provide the tools needed to scale FinOps maturity across organizations.

Sankey charts are a type of flow diagram in which the width of the arrows is proportional to the flow quantity. In FinOps context, they visually represent how cloud spend moves from a source (like cloud accounts or projects) to a destination (such as services or labels). They are particularly effective at showing cost transfers and transformations.

Starting today, you can now use Sankey charts in DoiT Cloud Intelligence™  to visualize how your cloud spend flows across services, projects, labels, and other dimensions. This new visualization helps you understand cost distribution, identify unexpected patterns, and surface high-impact cost drivers in complex environments.

What you can do with Sankey charts:

• Trace spend across multiple dimensions: See how costs move from source to destination - such as from cloud accounts to services, or projects to labels.
• Identify inefficiencies: Quickly detect cost concentrations or flows that don’t align with expectations.
• Customize the view to your business: Choose any combination of source and target dimensions to reflect your internal org or architecture.

No configuration changes are needed. Just select your Report dimensions and explore the flow as a Sankey chart.

CloudFlow — Variables for repeatable, low-friction automations

CloudFlow now supports workflow-level variables.

• Global variables sit in one central file and are visible to every CloudFlow in the org.
• Local variables live inside a single flow and override globals for that flow only.

Here is why this is important:

• Eliminate hard-coded values: Replace literals (account IDs, regions, bucket names) with named parameters. Edit once, redeploy, and every pipeline inherits the new value, no wide-scale manual search-and-replace.
• Promote safely across environments: Ship the same CloudFlow file from dev → staging → prod by swapping variable sets instead of editing the flow itself, which reduces config drift.
• Constrain blast radius: Keep experimental or high-risk values quarantined with local variables, while global variables let you roll out org-wide changes.
• Reduce manual error: Fewer touch points mean fewer typos, missing regions, or mis-tagged resources—improving reliability and cost-allocation accuracy.
• Speed up multi-region replication: Clone a flow, update the region variable list, and publish. The deployment logic stays intact; only the parameter set changes.
• Automate cost governance: Tie spend thresholds, budgets, or anomaly detectors to variables. When account structures evolve, you update the variable once, and all alerts/remediations inherit the new scope automatically.

To learn more about variables and how to create them, check out our Help documentation or raise a ticket with the DoiT support team.

You already know the power and precision of our time-based filters in DoiT Cloud Intelligence™ . Today, we're excited to announce that we've extended this capability to your custom dashboards.

Now, you can use a time filter to instantly update every widget on a dashboard. This enhancement lets you zero in on a specific timeframe for your analysis, ensuring all your graphs and reports are perfectly synchronized. No more clicking into individual widgets or reports to analyze specific time frames, just a single, unified view.

By bringing DoiT Cloud Intelligence date filtering to the dashboard level, you can accelerate your analysis and make data-driven decisions faster.

Zero In on Cost Anomalies

When a cost spike occurs, open your custom dashboard and use the time filter to isolate that specific time period. Every widget will quickly update, giving you a complete, multi-faceted view of what happened across all services, helping you find the root cause in seconds.

Accelerate Reporting Cycles

Simplify your showback and reporting process. If you have a dashboard of reports set to "Last Quarter," but want to broaden your view, the new time filters quickly let you do this across all of your widgets.

Validate Optimization Efforts

Easily measure the impact of a recent migration or optimization project. Toggle the dashboard's view between the timeframes before and after the change to get an immediate, consistent comparison of your costs and usage patterns.

We’re happy to release our new experience for investigating and remediating cost anomalies. Now, when you get an alert about a new anomaly and review it for further investigation, the process of Assessment → Investigation → Remediation is more intuitive, making it easier to quickly take the necessary actions to fix and report on the issue.

Assessment

The anomaly screen clearly displays the most pertinent information about the cost spike, including the date and time it was detected, the service and account that have been impacted, and the individual SKUs and resources contributing to the spike.

Investigation

In addition to opening a Cost Analytics report to investigate the anomaly in more detail on your own, we’ve also made it easier to engage with a DoiT Cloud Architect directly from the anomaly screen or get a complete AI analysis.

You can also more easily foster cross-team collaboration by acknowledging it, and improve future detection capabilities by providing feedback to DoiT.

Remediation

Once the root cause of the cost spike has been identified, the next step is to fix the underlying issue. Here again, you can get expert advice from both Ava and, for customers on an Enhanced, Premium, or Enterprise tier, a DoiT expert who can help provide further analysis and recommend specific remediation steps.

To learn more about DoiT Anomaly Detection and how to respond to cost spikes, check out our Help documentation or contact your DoiT account team.

CloudFlow steps that call AWS APIs can now include “Waiters” – built-in polling mechanisms that block execution until a resource reaches a specified state (for example, an EC2 instance enters “running,” or a CloudFormation stack reaches “CREATE_COMPLETE” state). 

The flow does not advance until the waiter confirms the target condition, eliminating timing-related race conditions and “false-positive” successes.

Why this matters:

• Deterministic pipelines: By making each step state-aware, you remove arbitrary sleep statements and ad-hoc retries. That yields predictable, repeatable deployments and fewer flaky builds.
• Lower failure rates: Waiters leverage AWS service-specific back-off logic and error handling, so you avoid cascading errors caused by resources that are still provisioning.
• Tunable latency: The default poll interval is 15 seconds, but you can override MinDelay, MaxDelay, and MaxAttempts to balance pipeline speed against API call volume.
• Cost-efficient polling: Purpose-built waiters replace tight loops or aggressive custom polling, reducing superfluous API calls and the associated request charges.
• Optimized resource billing: Downstream tasks start only when prerequisites are truly ready, so you don’t keep compute, container, or runner capacity idle while waiting for eventual consistency.
• Predictable spend: More deterministic execution times make it easier to forecast run-time costs and spot anomalies.

To enable a waiter, add the waiter configuration block to any CloudFlow step that invokes an AWS API. 

To learn more about how to apply AWS Waiters, check out our Help documentation or raise a ticket with the DoiT support team.

We're launching the DoiT Cloud Intelligence™ remote MCP server, making it easy for you to access your real-time cloud cost and usage data through AI assistants like Claude, ChatGPT, and Cursor. 

Anyone can now ask plain-language questions about their cloud usage, connect it to business context  — like “Can you connect our recent database cost spike to any Jira tickets?” (see example below 👇 ) — and get instant, context-rich answers.

What's different with remote access?

Our remote MCP server eliminates the technical setup that our previously-launched local version required, making conversational cloud cost analysis accessible to finance teams, executives, and other business stakeholders. 

Here's what makes the remote approach different:

• Easy setup: Connect AI tools to your cost data without any technical setup—no Node.js installations or configuration troubleshooting that come with locally-hosted MCP servers.
• Real-time data access: Our remote server maintains live connections to your DoiT Cloud Intelligence data, ensuring everyone accesses the same, current information without having to manually update to the latest version.

Democratizing cloud intelligence beyond technical teams

This remote approach brings cloud intelligence directly to non-technical roles across your company. 

And for even richer insights, you can connect with MCP servers from other tools you use (ex. Jira, Slack, GitHub) to understand the full business context behind your cost data:

Finance teams can analyze budget variance and forecast accuracy during monthly reviews.

For example: You can use Claude to get the full business context behind a 30% budget overrun highlighted in Budgets by connecting it to an approved GPU increase documented in Jira.

Product managers can evaluate feature profitability and unit economics for roadmap decisions.

For example: When analyzing a new feature costing $350/day (15% over budget), you can correlate it with performance issues listed in Jira tasks and Zendesk tickets and enable more-informed cost/performance trade-off conversations.

Operations teams can correlate infrastructure changes with spending pattern.

When you ask Claude about a 25% EC2 cost spike highlighted by Anomaly Detection, you can determine whether or not it was a planned scaling event by connecting it to a recent deployment in Jira and engineer discussions in Slack.

Ready to understand the full business story behind your cloud costs?  Connect to DoiT’s remote MCP server and start asking questions.

Your Home Dashboard now includes four new widgets to complement your existing optimization widgets to give you a complete financial picture for faster decision-making.

What's added:

1. Current month spend: See exactly where you stand with real-time spend data for the current month.
2. Current month forecast: View projected costs based on your current usage patterns to anticipate your month-end spending.
3. Current month savings: Track your realized savings from Flexsave, Spot Scaling, and PerfectScale (coming soon).
4. Security insights: Get visibility into security risks requiring attention, with recommendations for access control, encryption, identity management, and compliance.

With the additional context added to your Home Dashboard, you can better identify what needs attention and prioritize your optimization efforts.

Find your updated dashboard by clicking Home in the top navigation (or whenever you log in to the DoiT console).

Once your own team, or DoiT Cloud Architects during an Expert Inquiry, has completed technical diligence, you can label the Insight with the status that truly reflects its outcome. From the Insight detail view, pick one of:

• Actionable
• Acknowledged
• In Progress
• Optimized
• Dismissed

Choosing the correct status after diligence keeps tracking honest, shows clear progress, and fits straight into your existing workflow.

You’ll find the status menu in the top-right corner of the Insight detail view.

Starting from a blank report just got a whole lot easier.

We’ve integrated AI—directly into the new Report user interface to help you go from zero to insight in seconds.

You can use natural language prompts like:

• “Show my top five cost drivers across all projects for Q2 2025”
• “Break down Kubernetes cluster spend by namespace for the last 30 days”
• “Compare my costs for Compute Engine and Cloud Storage for the last 7 days.”

It understands your request, queries your billing data, and automatically generates the report visualization, all within the same view.

Once the report is ready, it leaves you with a fully interactive chart and all standard filters available to refine further.

This is just the beginning of AI in Cloud Cost Analytics, designed to accelerate and simplify complex report creation. More capabilities are coming soon.

Head to Analyze > Reports and try it for yourself.