Overprovisioned Memory in Cloud Run Services

Service Category

Compute

Cloud Provider

GCP

Service Name

GCP Cloud Run

Inefficiency Type

Overprovisioned Resource

Explanation

Cloud Run allows users to allocate up to 8 GB of memory per container instance. If memory is overestimated — often as a buffer or based on unvalidated assumptions — customers pay for more than what the workload consumes during execution. Unlike in VM-based environments where memory might be shared or underutilized without direct cost impact, in Cloud Run, you're billed precisely for what you allocate. This inefficiency often results from: * Defaulting to high memory values for “safety” * Not using monitoring tools to assess actual memory usage * Lack of clear ownership over service tuning

Relevant Billing Model

Charged based on: * Allocated memory and CPU per instance * Execution duration (rounded up to the nearest 100ms) * Number of requests and networking egress (if applicable) Even unused allocated memory is fully billed per 100ms of execution time, making memory overprovisioning a direct driver of excess cost.

Detection

Identify Cloud Run services with high memory allocation (e.g., \>1 GB)
Compare against actual memory usage (visible in Cloud Monitoring or Cloud Trace)
Review historical memory usage variance across multiple invocations
Flag workloads with stable memory use but large memory headroom
Check for default templates or configurations that may enforce high memory settings

Remediation

Reduce memory allocation to match observed memory usage with a buffer for spikes
Continuously monitor function-level memory metrics to right-size allocations over time
Set up proactive alerts for services with memory allocation far exceeding usage
Refactor container images or code to optimize memory consumption
Establish governance policies or templates that encourage conservative starting values

Relevant Documentation

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