Insights

Insights is where nOps surfaces cost-saving opportunities and unexpected spend changes across your cloud infrastructure. It has two tabs: Recommendations for ongoing optimization opportunities, and Anomalies for cost spikes and drops that need a closer look.

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Get started

1. Sign in to nOps.
2. In the sidebar, open Inform → Insights, or go to clara.nops.io/insights.
3. Use the tabs at the top to switch between Recommendations and Anomalies.

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Recommendations tab

Recommendations helps you find and act on cost-saving opportunities across your cloud infrastructure. nOps continuously analyzes your resource usage and configurations, then surfaces actionable recommendations with estimated savings, an implementation effort level, and AI-generated guidance on how to implement each one.

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Summary cards

Three cards give you an at-a-glance view of your optimization program:

• Savings Summary — potential Annual savings and Monthly savings across all active recommendations.
• Recommendations by effort — active recommendations grouped by implementation effort (Low, Medium, High).
• Savings achieved — realized savings from recommendations you’ve already implemented, shown as current Monthly savings and cumulative Saved to date.

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Clara Insights

Clara Insights is an AI-powered carousel at the top of the tab that automatically analyzes your recommendation data and highlights the most impactful opportunities—for example, “You have 12 high-impact Compute recommendations worth $5,000/month in potential savings.”

• Click any insight card to instantly filter the list to matching recommendations. Click it again to remove the filter.
• Use the refresh action to regenerate insights from your latest recommendation data.

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Savings History

The Savings History chart visualizes your recommendation activity and savings over time:

• Switch between Spend and Count modes to see savings amounts or recommendation counts.
• Choose a time range: Last 7 days, Last 30 days, Last 90 days, Last 6 months, Last year, or All time.
• Hover any point for details, including savings achieved when a recommendation was resolved.

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Recommendations list

• Toggle between Grouped (by recommendation type) and Flat views.
• Search recommendations to filter by name or resource.
• Use Filters to narrow by Account, Region, Effort, Category, Action, or Service.
• Download CSV to export the currently displayed rows.
• Click any row to open its details.

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Recommendation details

Opening a recommendation shows three tabs:

• Overview — Monthly Savings, implementation effort, age since detection, and a Clara Summary explaining the opportunity in plain language.
• How to Implement — step-by-step guidance from Clara, plus Console & CLI quick actions with direct links and copyable commands.
• Technical Details — resource details and other considerations relevant to implementation.

If a console link is available, use View in AWS to jump directly to the resource.

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Saved views and email schedules

• Use Manage Reports to save your current filters as a view (Private, Shared, or All), so you and your team can return to the same slice of recommendations.
• Send a saved view now, or schedule a recurring email digest, from the Recommendations tab on the Reports page.

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Effort levels

Every recommendation is scored Low, Medium, or High effort based on factors like whether it requires engineering validation, a resource restart, operating system or application configuration changes, or code changes. Lower effort generally means fewer coordination steps and less risk of disruption.

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Recommendation types

nOps generates recommendations across a range of optimization categories, including:

• Rightsizing — resize underutilized EC2 instances, RDS instances, or Lambda functions.
• Idle resources — stop idle RDS instances, scale in underused Auto Scaling Groups, or remove unused Elastic Load Balancers.
• Storage — upgrade EBS volumes (for example, GP2 to GP3), delete unused EBS volumes, enable S3 Intelligent-Tiering, or clean up old EC2 and RDS snapshots.
• Graviton migration — move EC2 instances, RDS databases, or Auto Scaling Groups to AWS Graviton for better price-performance.
• Modernization — upgrade EC2 instances to newer generations, switch Aurora to IO-Optimized, or move from third-party AI models to AWS Bedrock.

Recommendations are refreshed regularly, so new opportunities appear as your usage patterns and cloud provider pricing change.

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Anomalies tab

Anomalies helps you spot unexpected cost spikes and drops as soon as they happen, and drill into what caused them—so surprises don’t wait until the end of the month to surface.

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Summary cards

• Anomalies — the total number of anomalies in the selected date range. Click this card to clear the high severity filter and show every anomaly.
• Total impact — the aggregate excess (or reduced) spend across all anomalies in the window.
• High severity — the count of Critical or High severity anomalies. Click to filter the table down to just those.

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Filters

• Date range — Last 7 days, Last 30 days, Last 90 days (default), or All time.
• High severity — toggle from the summary card to show only Critical/High severity anomalies.

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Anomalies table

Each row shows:

• Source — which detector found the anomaly.
• Severity — Critical, High, Medium, or Low, based on how unusual the spend pattern is.
• Start / End / Duration — when the anomaly began, ended, and how many days it lasted.
• Cost impact — the estimated excess spend (or savings, shown as a decrease) caused by the anomaly.
• Impact % — how much the anomaly changed spend relative to the expected baseline.
• Trend — a sparkline showing the spend pattern around the anomaly.
• Service — the affected AWS service, plus a short plain-language summary of the anomaly.
• Root causes — click to expand and see the specific accounts, regions, and usage types driving the anomaly.

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Root cause details

Expanding a row’s root causes shows a ranked list of contributors, each with:

• The account, region, and usage type or charge type responsible.
• A contribution bar showing that cause’s share of the total impact.
• The dollar impact of that specific cause.
• An Explorer button that opens Explorer pre-filtered to that anomaly’s date range, service, and root cause—so you can dig deeper into the underlying usage.

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Send and schedule anomaly reports

Use Manage reports to deliver the current anomaly view to your team:

• Send now — deliver immediately to email addresses, Slack channels, or Microsoft Teams channels.
• Schedule — set up a recurring digest (daily, weekly, or monthly) so your team gets anomaly updates automatically.
• Both options carry over the selected date range and high severity filter, and let you customize the subject and add a note.

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FAQs

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Related

• Reports — saved recommendation views and email schedules
• Dashboard — account-wide spend summary
• Explorer — analyze the cost impact of a recommendation or anomaly root cause in more detail