Recommendations: Triage Azure Advisor and Savings

Overview

The Recommendations page in Spotto is where you triage cloud optimization (including Azure Advisor recommendations and Spotto-generated guidance) across one or more subscriptions. It shows category, impact, effort, estimated savings, and how many resources are affected, so you can prioritise work without rebuilding the same spreadsheet every week.

Feature overview

The Recommendations page is a list view for finding “what should we fix next?” items: unused/orphaned resources, outdated SKUs, overprovisioned services, and best-practice gaps. Each recommendation can be drilled into for affected resources, context, and actions like dismissing, prioritising, or sharing.

Why use this? (Jobs, pains, gains)

Jobs to be done

• When I’m trying to reduce cloud spend, I want to find the highest-impact savings opportunities, so I can prioritise work that actually moves the bill.
• When I’m working across many subscriptions, I want a single, deduplicated view of recurring recommendations, so I don’t chase the same issue N times.
• When I need to route work to owners, I want to filter by tags/resource types and export/share, so platform teams get actionable lists instead of “please optimize Azure”.
• When a recommendation affects many resources, I want to see resource count and scope, so I can estimate effort and blast radius before committing.

Common pains

• Provider-native recommendations are useful, but they’re easy to ignore when they’re scattered across subscriptions.
• “What’s worth doing?” gets lost between low-value nitpicks and high-impact changes.
• Ownership is unclear without tag-based slicing (cost center, product, environment, team).
• Savings estimates are often presented without enough context to decide whether to act now.

What you gain

• A consistent triage view with impact, effort, and savings range side-by-side.
• Filtering that lets you zoom in on the right slice (resource types, locations, tags, minimum cost, “created” window).
• Cross-subscription deduplication: a single recommendation affecting many resources is treated as one item with a combined resource list.
• A detail view that supports planning actions (share/export, track status, document rationale).

Key capabilities

Scope the view to the right subscriptions

Use the subscription selector at the top of the page to choose one or more subscriptions. This is the fastest way to move between “show me everything” and “show me just the subscriptions my team owns”.

Triage recommendations with the right signals

The table is optimized for triage, not trivia:

• Recommendation name (and a short summary)
• Category (often aligned to well-architected pillars like Cost, Performance, Reliability, Security, Operational Excellence)
• Impact (High / Medium / Low)
• Resources (how many items are affected)
• Effort (High / Medium / Low, sometimes with an approximate hours hint)
• Savings (a range when available)

Spotto defaults sorting toward “do the big things first”: higher impact, lower effort, and cost-first categories.

How ranking works (default sort)

When you first open the page (or clear all sorts), Spotto applies a default ranking designed to surface work that matters most:

• Priority tier first: “must‑do” recommendations bubble to the top before normal optimization work.
• Score next: recommendations are scored using a blend of impact, effort, risk, confidence, and exposure so you see high‑value items early.
• Savings as a tie‑breaker: when two recommendations are otherwise similar, higher savings (when available) is preferred.
• Category nudges: cost‑saving categories are slightly prioritised in the default view to reduce spend quickly.

You can always override this by sorting any column (Impact, Effort, Savings, Resources, etc.). When you apply a manual sort, the table follows your chosen order.

The score is a blended signal (impact, effort, risk, confidence, exposure). The breakdown is shown with the ranking view:

Filter and search like you mean it

Use search plus filters to narrow down from “everything” to “the work I can actually do this sprint”.

Common filters include:

• Category and impact (quick prioritisation)
• Resource type and location (team routing)
• Tags (cost attribution and ownership slicing; uses key: value style tags)
• Created window (today / last 7/30/90 days, etc.)
• Effort, minimum resource count, and minimum cost
• From Spotto (show Spotto-generated recommendations)

Export and share

• Export the current view to CSV for backlog creation, reporting, or change planning.
• On a recommendation detail page you can share via channels configured for your company (for example email, Jira, Slack/Teams, depending on integrations).

Drill into details and take action

Select a recommendation to open the detail view, which focuses on answering:

• What is the recommendation and why does it matter?
• Which resources are affected?
• What are the quick steps / remediation guidance?
• What status should we assign (prioritise, dismiss, etc.) and what’s the reason?

Technical reference

Component	Details
Inputs	Recommendation data per subscription (including provider-native guidance like Azure Advisor) plus Spotto’s resource inventory (type, location, tags, spend) and any Spotto-generated (custom) recommendations.
Outputs	Summary cards, a filterable recommendations table, recommendation detail view (resources + guidance), CSV export, and share/actions (prioritise/dismiss/restore).
Defaults	Cross-subscription recommendations are de-duplicated by recommendation ID and resources are combined. Default ranking prioritises must‑do items, then higher score (impact/effort/exposure/risk), with savings and cost categories as secondary nudges.
Limitations	Savings ranges are estimates and may be missing for some recommendations; treat them as prioritisation inputs, not invoice guarantees. Some recommendations may have “unknown” impact/effort depending on the source data.

How it works (high level)

• You select one or more subscriptions.
• Spotto loads recommendations for those subscriptions and shows them in a single list.
• Recommendations with the same recommendation ID across subscriptions are de-duplicated; their affected resources are combined.
• Filters are applied using recommendation fields (category/impact/effort) and resource fields (type/location/tags/spend/created time).

How it differs from Retirement Tracker (and why both matter)

Recommendations are mostly about continuous optimization: reduce waste, improve performance, and tighten security posture over time.

Retirement Tracker is about deadline-driven risk: Azure announces that something will stop being supported (or stop existing), and you need to migrate/upgrade before the date.

In practice:

• Use Recommendations for “what’s worth doing next?” work.
• Use Retirement Tracker for “what will break if we ignore it?” work.

They’re complementary: one keeps your cloud healthier, the other keeps it from surprising you during a change freeze.

Troubleshooting

The page is empty / I don’t see any recommendations

What you’re seeing: No rows, or an empty state prompt. Likely causes:

• No subscriptions selected.
• Subscriptions are still onboarding or not fully synced. How to fix:

1. Select at least one subscription in the subscription picker.
2. If the subscription is still onboarding, wait for ingestion to complete and refresh.

I can’t dismiss or implement a recommendation

What you’re seeing: Actions fail, or you don’t see the expected effect. Likely causes: Some actions require write permissions in the cloud provider (you may see errors like 403 Forbidden / “Permission denied”). How to fix: Review the Portal write-permissions guide for Advisor actions: Advisor Recommendations.

Savings is blank or looks “too good to be true”

What you’re seeing: Savings shows -/empty, or the range seems off. Likely causes: Not every recommendation source includes savings; estimates can be directional and depend on spend history and pricing assumptions. How to fix: Use savings as a ranking hint, then validate against resource-level cost and your own constraints before changing production.

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