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Use case

Stop paying for idle inference endpoints

Find managed inference endpoints running 24/7 with little or no traffic, and get the exact fix — delete or scale to zero — without losing the model.

Cost Optimization $6,932/mo found
1
Rightsize Downsize the prod EKS node group

eks · prod-workers

$4,800/mo

High

2
Idle Stop the idle staging database

rds · analytics-staging

$1,180/mo

Medium

3
Rightsize Trim the over-provisioned checkout API

ecs · checkout-api

$640/mo

High

4
Orphaned Delete 14 unattached EBS volumes

ec2 · 14 × gp3

$312/mo

High

The problem

Managed inference endpoints — SageMaker, Vertex AI, Azure ML — bill for the GPU behind them around the clock, whether or not anything is calling them. Endpoints spun up for an experiment, a demo, or a since-migrated service keep running, and a generic cost dashboard just shows “ML” going up. It’s some of the easiest waste to create and the hardest to notice.

How Lizrd fixes it

Lizrd reads endpoint traffic and GPU utilization read-only across all three clouds, finds endpoints with little or no invocation over the trailing window, and tells you exactly what to do — delete the endpoint (the model and config are retained), or move low-but-bursty traffic behind a scale-to-zero configuration. Each finding comes with the evidence (calls and GPU over 14–30 days), a confidence level, and the one command to run.

The outcome

The idle GPU bill goes to zero, the model stays safe, and you can bring the endpoint back any time. Lizrd validates the realized savings against your bill once the change lands.

“We had three SageMaker endpoints from experiments that nobody had torn down. They were quietly burning GPU hours every hour of every day.”
— ML platform lead, SaaS ~$2.2k/mo

More ways to save

Find this in your own cloud

Connect read-only and Lizrd surfaces the highest-impact fixes — with the exact change to make.