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")

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

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