Recommendations

Overview

Recommendations represent pending savings opportunities identified by LevelFour across your cloud accounts. Each recommendation includes the affected service, estimated monthly and annual savings, and available actions (accept, reject, execute).

Methods

Get Savings by Provider

Returns a summary of potential savings grouped by cloud provider (AWS, GCP, Azure, K8s).

summary = client.recommendations.get_savings_by_provider()

Get Potential Savings

Returns potential savings summary grouped by provider.

potential = client.recommendations.get_potential_savings()

Get Overview

Returns overview metrics across all providers: total spend, available savings, pending savings, and saved-to-date.

overview = client.recommendations.get_overview()

List Recommendations

Returns a paginated list of all recommendations. Supports sorting by multiple fields.

page = client.recommendations.list(
    page=1,
    page_size=50,
    sort_by="monthly_savings",
    sort_order="desc",
)

for rec in page:
    print(f"{rec.service}: ${rec.monthly_savings}/mo")

Parameters

Parameter	Type	Description
`page`	`int`	Page number (1-indexed)
`page_size`	`int`	Items per page (max 100)
`sort_by`	`string`	Sort field: `recommendation_id`, `service`, `environment`, `monthly_savings`, `annual_savings`, `savings_percentage`, `analysis_period`, `created_at`
`sort_order`	`string`	`asc` or `desc`

Get Recommendation

Returns complete details for a specific recommendation by ID.

detail = client.recommendations.get("rec_123")

Get Provider Overview

Returns high-level KPI metrics for recommendations scoped to a single provider: total spend, available savings, pending savings, and saved-to-date. Useful for rendering per-provider dashboard headers.

overview = client.recommendations.get_provider_overview("aws")

List By Provider

Returns a paginated per-provider recommendations list with filtering by service, environment, account, tag, and display status. Each row includes monthly/annual savings, current spend, and a savings percentage.

page = client.recommendations.list_by_provider(
    "aws",
    page=1,
    page_size=20,
    service=["EC2", "RDS"],
    environment=["production"],
    display_status=["available"],
    sort_by="monthly_savings",
    sort_order="desc",
)

for rec in page:
    print(f"{rec.service}: ${rec.monthly_savings}/mo")

Parameters

Parameter	Type	Description
`page`	`int`	Page number (1-indexed)
`page_size`	`int`	Items per page (max 100)
`sort_by`	`string`	Sort field: `recommendation_id`, `service`, `environment`, `account`, `tag`, `monthly_savings`, `savings_percentage`, `status`
`sort_order`	`string`	`asc` or `desc`
`service`	`string[]`	Filter by service
`environment`	`string[]`	Filter by environment
`account`	`string[]`	Filter by account ID
`tag`	`string[]`	Filter by tag
`display_status`	`string[]`	Filter by status: `available`, `pending`, `processing`, `optimized`, `rejected`, `unavailable`

For filter recipes using these parameters — including the "RDS delete candidates" pattern, top-N-by-savings ranking, and CSV export — see Filtering and sorting.

List In-Progress

Returns all recommendations currently in processing status.

processing = client.recommendations.list_in_progress()

Response Types

RecommendationItem

Returned in paginated list responses.

Field	Type	Description
`recommendation_id`	`string`	Unique business identifier
`service`	`string`	Cloud service name
`environment`	`string?`	Environment tag
`analysis_period`	`string?`	Analysis time window
`monthly_savings`	`float`	Estimated monthly savings
`annual_savings`	`float`	Estimated annual savings
`current_spending`	`float`	Current monthly spend
`savings_percentage`	`float`	Savings as percentage of current spend
`actions`	`object?`	Available actions for this recommendation

Example: Find High-Value AWS Recommendations

from levelfour import LevelFour

client = LevelFour()

for rec in client.providers.list_recommendations(
    "aws",
    page_size=20,
    sort_by="monthly_savings",
    sort_order="desc",
    display_status=["available"],
):
    if rec.monthly_savings > 100:
        print(f"{rec.recommendation_id}: ${rec.monthly_savings}/mo")

Recommendations

On this page