Google Cloud Cost Optimization
LevelFour finds and fixes waste across 35 Google Cloud services. It connects with read-only access, analyzes real usage, then opens infrastructure-as-code pull requests that rightsize Compute Engine and Cloud SQL, tune GKE node pools, optimize BigQuery, and flag Committed Use Discount gaps. Your team reviews and merges; savings land the next billing cycle. SOC 2 Type II, setup in under 15 minutes.
How does LevelFour reduce Google Cloud costs?
LevelFour connects to your GCP projects with read-only access and analyzes utilization across Compute Engine, Cloud SQL, GKE, BigQuery, Cloud Run, persistent disks, and 29 other services. For each opportunity, an oversized VM, an idle Cloud SQL instance, an uncommitted workload eligible for a Committed Use Discount, or a BigQuery on-demand query that should use slots, it drafts the Terraform or Pulumi change and opens a pull request with the estimated savings. You review and merge; the savings appear on your next bill. Optimizations average up to 30%.
- ✓Compute Engine and Cloud SQL rightsizing from real usage
- ✓Committed Use Discount (CUD) coverage analysis
- ✓GKE node pool and autoscaler tuning
- ✓BigQuery slot vs on-demand and storage optimization
- ✓Idle VM, disk, and static IP cleanup
- ✓Cloud Run min-instance and concurrency tuning
Where Google Cloud (GCP) costs add up
Most Google Cloud waste is structural, not malicious. Compute Engine and Cloud SQL instances are sized for peak or copied from a template, so they idle at low CPU and memory most of the day. Steady-state workloads run at on-demand rates because Committed Use Discount coverage is thin or expired. BigQuery bills on-demand per terabyte scanned when predictable pipelines would be cheaper on slot reservations, while old partitions sit in active storage instead of long-term. Add idle VMs, unattached persistent disks, reserved static IPs that are no longer routed, and GKE node pools with loose autoscaler bounds, and the monthly bill drifts well above what utilization justifies.
Google Cloud cost optimization best practices
Five habits cover most of the savings. Rightsize Compute Engine and Cloud SQL from real P95 usage, not from the instance type someone picked at launch. Cover steady-state usage with Committed Use Discounts and let on-demand absorb the spiky tail. Control BigQuery by moving predictable workloads to slot reservations, partitioning and clustering tables, and letting cold data fall to long-term storage. Clean up idle VMs, unattached disks, and unused static IPs on a schedule, and tighten GKE node pools so the autoscaler bin-packs instead of over-provisioning. Finally, version-control every change as infrastructure-as-code so adjustments are reviewable and reversible rather than ad hoc console edits.
Manual vs automated Google Cloud cost optimization
Manual optimization means an engineer exports billing data, reads Cloud Monitoring, and hand-edits Terraform or the console during a quarterly cleanup. It works once, then drifts as workloads change and nobody re-checks. Automated optimization runs continuously: LevelFour connects with read-only access, analyzes real utilization, and ships each fix as a merge-ready infrastructure-as-code pull request by default, in Terraform, CloudFormation, Pulumi, or CDK. For cloud, you choose how each change lands: automated apply, an IaC pull request (the default), or manual. Your team keeps the review gate and the audit trail, while the analysis that surfaces oversized VMs, CUD gaps, and idle resources never goes stale between cleanups.
How LevelFour measures and de-risks GCP savings
A savings number is only useful if it does not cost you reliability. LevelFour measures opportunities at P95 utilization, so a rightsized Compute Engine VM or Cloud SQL instance keeps headroom for real peaks instead of being trimmed to the average. Access is read-only by default, so analysis never touches running infrastructure. Every recommendation arrives as a reviewable diff in a pull request, with the estimated savings attached, so an engineer can read exactly which machine type, node pool bound, or BigQuery setting changes before anything merges. GCP optimizations reach up to 30%, measured at that same P95 line, and you can opt into supervised automated apply once a class of change has earned trust.
Google Cloud Platform services LevelFour optimizes
35 services, each with the optimization LevelFour applies and the typical savings.
Compute Engine
Latest machine families deliver better price-performance.
GKE
Kubernetes cost allocation by namespace/team. Efficiency scores.
Cloud SQL
Idle instances detected. HA right-sized to actual needs.
Cloud Storage
Tier infrequently accessed data. 40–60% savings on archive-eligible data.
Persistent Disks
Eliminate orphaned disk costs. Optimize disk type.
CUDs
Coverage gaps filled. Unused commitments flagged.
Cloud Functions
Over-provisioned functions are a common waste pattern.
BigQuery
Reservation management for predictable workloads.
Cloud Run
Right-size concurrency and memory. Reduce idle min-instance costs.
Memorystore
Right-size Redis/Memcached instances. Detect idle caches.
Cloud Spanner
Autoscaler tuning prevents over-provisioning on high-cost instances.
Dataproc
Preemptible workers save 60%. Ephemeral clusters for batch jobs.
Cloud CDN
Higher cache hit ratio reduces origin egress costs.
Pub/Sub
Remove orphaned topics. Reduce retention for non-critical pipelines.
Filestore
Right-size file shares. Switch from Premium to Basic when IOPS allows.
Artifact Registry
Image lifecycle policies reduce storage costs.
AlloyDB
Read pool optimization and idle instance detection.
Bigtable
Autoscaling prevents over-provisioning. HDD for cold data.
Firestore
Operation optimization and storage cleanup.
Cloud NAT
NAT gateway rightsizing. Port allocation optimization.
Cloud DNS
Eliminate orphaned DNS zones. Consolidate where possible.
Cloud Load Balancing
Remove unused load balancers. Forwarding rule fees add up.
Cloud Armor
Right-size WAF tier. Standard is sufficient for most workloads.
Cloud Interconnect
Attachment capacity rightsizing for dedicated connections.
Cloud VPN
Idle tunnel charges add up across regions.
Dataflow
Worker rightsizing and autoscaling for streaming/batch pipelines.
Vertex AI
Idle notebooks and Spot training for AI workloads.
Cloud Composer
Environment sizing optimization for Airflow workloads.
Looker
Unused licenses are a common overspend.
Cloud Tasks
Remove orphaned task queues.
Cloud Logging
Log exclusion and routing reduce ingestion costs significantly.
Cloud Monitoring
Custom metric costs scale with cardinality. Cleanup saves.
Cloud Build
Right-size build compute. Caching reduces minutes.
App Engine
Instance class rightsizing and idle instance optimization.
Cloud Backup and DR
Backup policy optimization reduces storage costs.
Google Cloud Platform cost optimization FAQ
Which GCP services does LevelFour optimize?
35 Google Cloud services, including Compute Engine, Cloud SQL, GKE, BigQuery, Cloud Run, Memorystore, persistent disks, and more. The full list with the optimization applied to each is below.
How much can I save on Google Cloud?
GCP optimizations average up to 30%. Actual savings depend on your current rightsizing, Committed Use Discount coverage, and idle resource levels.
Does LevelFour apply GCP changes automatically?
No. LevelFour is read-only by default and proposes every change as an infrastructure-as-code pull request. Nothing is applied until you review and merge, or opt into supervised automation.
How long does GCP setup take?
Under 15 minutes. You connect your projects with read-only access; there are no agents to install and no code changes required.
Cost optimization by platform