Google Cloud
Google Cloud Cost Optimization Services
We find and remove waste across Compute Engine, GKE, BigQuery, storage, networking and AI services, then put guardrails in place so the savings last.
Pay for the Cloud You Use, Not the Cloud You Forgot
Google Cloud bills are made up of thousands of line items, and waste hides in many of them: virtual machines sized for a peak that never comes, disks left behind after instances are deleted, development environments running all weekend, BigQuery queries that scan entire tables, and data moving between regions without reason. Each item is small, but together they often represent a meaningful share of spend. Google provides strong tools to surface them, including Recommender, FinOps hub and cost anomaly detection, yet someone still has to analyze, prioritize and implement the changes safely.
Hanoi Technology runs cost optimization engagements that combine analysis with engineering. We review billing exports and utilization data, validate each recommendation against application requirements, and implement changes with your teams through infrastructure as code. Savings are tracked against a baseline, so you see what was achieved rather than what was promised. Where an optimization involves risk, we test it first and agree on a rollback plan.
Our analysis looks beyond individual resources to architecture. Sometimes the largest saving comes from moving a scheduled batch workload to Cloud Run jobs, consolidating small Cloud SQL instances, keeping data processing in the same region as storage, or caching results that dashboards request repeatedly. We present these options with their effort and risk so you can decide which changes are worth making now and which belong on the roadmap.
The Challenge
Where Google Cloud Money Leaks
The cost patterns we find most often in Google Cloud reviews.
Overprovisioned Compute
Machine types are chosen once at launch and never revisited. Average CPU and memory utilization stays low, but nobody wants to risk resizing a production system without data and a test plan.
Unoptimized BigQuery Usage
Queries without partition filters, SELECT star on wide tables, repeated dashboard refreshes and unsuitable pricing models drive analytics costs. A few heavy users or jobs often account for a large portion of the bill.
Idle and Orphaned Resources
Unattached persistent disks, old snapshots, unused static IPs, idle load balancers and abandoned projects keep billing quietly. They accumulate fastest after migrations and proofs of concept.
Commitments and Pricing Models Unused
Steady workloads run at on-demand rates because committed use discounts were never analyzed, while batch jobs that could use Spot VMs pay full price.
Business Value
What Cost Optimization Delivers
Measured, Verified Savings
Every change is tracked against a baseline in billing data, so savings are reported as achieved results.
No Performance Trade-Offs Without Agreement
Recommendations are validated against utilization and service requirements, and risky changes are tested first.
Better Commitment Coverage
Analysis of steady-state usage supports confident purchase of resource-based or spend-based committed use discounts.
Leaner Analytics Spend
BigQuery table design, query patterns and capacity models are tuned to the way your teams actually use data.
Cleaner Environments
Removing orphaned resources and enforcing lifecycle policies also reduces security exposure and clutter.
Savings That Stay
Budgets, policies, scheduled shutdowns and regular reviews stop waste from building up again.
Optimization Is Engineering Work
Recommendations are easy to generate and harder to apply. The real work is validating them with application owners, changing infrastructure code, testing and rolling out safely. We do that work alongside your teams so savings appear on the next invoice, not just in a report.
We keep a shared savings register that lists every opportunity, its owner, estimated and realized savings, and status. It turns optimization into a visible, manageable backlog that finance and engineering review together.
Our Services
Hanoi Technology’s End-to-End GCP Cost Optimization Services
Optimization levers we analyze and implement across your Google Cloud estate.
Cost Assessment and Savings Plan
We analyze billing export, utilization metrics and Recommender output across projects, then build a prioritized savings backlog with estimated impact, effort and risk for each item.
- Spend baseline
- Prioritized savings backlog
- Implementation plan
Compute Right-Sizing and Scheduling
We right-size Compute Engine instances, move suitable workloads to newer or custom machine types, introduce Spot VMs for fault-tolerant jobs and schedule non-production environments to stop outside working hours.
- Right-sizing changes
- Spot VM adoption
- Instance schedules
Commitment Strategy
We model steady-state usage and recommend the mix of resource-based and spend-based committed use discounts and BigQuery reservation commitments, with a review cadence to keep coverage aligned.
- Commitment analysis
- Purchase recommendations
- Coverage tracking
BigQuery Optimization
We identify expensive queries and jobs, add partitioning, clustering and materialized views, review on-demand versus edition pricing, and set custom quotas.
- Query and table tuning
- Pricing model review
- Cost guardrails
GKE and Container Efficiency
We tune requests and limits, enable cluster and node autoscaling, evaluate Autopilot and enable GKE cost allocation to see costs by namespace.
- Resource request tuning
- Autoscaling configuration
- Namespace cost reporting
Storage and Network Optimization
We clean up orphaned disks and snapshots, apply Cloud Storage classes and lifecycle rules, and reduce cross-region and internet egress where architecture allows.
- Storage cleanup
- Lifecycle policies
- Egress reduction plan
Our Approach
Our Cost Optimization Process
A structured cycle from baseline to sustained savings.
- 01
Collect and Baseline
We enable or review billing export and gather utilization metrics, establishing a clear baseline by service, project and team.
Deliverables: Cost baseline · Data access setup
- 02
Analyze and Prioritize
We combine Google recommendations with our own analysis and rank opportunities by savings, effort and risk.
Deliverables: Opportunity register · Prioritization matrix
- 03
Validate with Owners
We review each change with application owners, confirm performance needs and agree on test and rollback plans.
Deliverables: Approved change list · Test plans
- 04
Implement Safely
We apply changes through Terraform or your deployment process, starting with low-risk items and monitoring after each change.
Deliverables: Implemented changes · Monitoring results
- 05
Verify and Sustain
We report realized savings against the baseline and set up budgets, policies and a recurring review to keep costs down.
Deliverables: Savings report · Ongoing guardrails
Use Cases
Cost Optimization Examples
Typical patterns from Google Cloud optimization work.
Post-Migration Cleanup
After a lift-and-shift, a company right-sizes hundreds of VMs based on several weeks of utilization data and removes disks left over from migration testing.
BigQuery Spend Reduction
A data team finds a handful of dashboards scanning full tables on every refresh, adds partition filters and materialized views, and moves stable workloads to reservations.
Non-Production Scheduling
Development and test environments are shut down at night and on weekends using instance schedules, with an easy override for late-running work.
Batch Jobs on Spot VMs
A rendering and data processing pipeline moves to Spot VMs with checkpointing, accepting preemption in exchange for much lower compute rates.
Kubernetes Efficiency
Platform engineers lower over-generous container requests and enable autoscaling, allowing GKE clusters to run with fewer nodes.
Storage Lifecycle Management
Logs and backups older than their retention period move automatically to colder storage classes or are deleted according to policy.
Technology
Cost Optimization Tools
Google Cloud capabilities and tools we rely on.
- Cloud Billing Reports
- Billing Export to BigQuery
- FinOps hub
- Recommender
- Active Assist
- Cost Anomaly Detection
- Committed Use Discounts
- Spot VMs
- BigQuery Reservations
- INFORMATION_SCHEMA
- GKE Autopilot
- GKE Cost Allocation
- Cloud Storage Lifecycle
- Cloud Monitoring
- Terraform
- Looker Studio
Platforms
Deliver GCP Cost Optimization on Your Preferred Platform
Why Hanoi Technology
Why Organizations Choose Hanoi Technology
Senior, Cross-Functional Teams
Strategy, data, AI, cloud and engineering specialists work as one team, so nothing is lost between hand-offs.
Proven Delivery
More than 50 projects delivered for clients in North America, Europe and Asia-Pacific.
Radical Transparency
A written update every week and a demo of working software every sprint, so you always know where things stand.
Cost-Effective Global Delivery
Experienced engineers from our Hanoi delivery hub, with working-hour overlap for every major time zone.
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Find Your Google Cloud Savings
Start with a cost assessment that shows where you can save and how much effort it takes. Book a free consultation and we will reply within 24 hours.
GCP Cost Optimization FAQ
How much can we expect to save?
It depends on how your environment was built and how long it has gone without review. Recently migrated environments and those with heavy on-demand analytics usage typically show the most opportunity. We do not quote a percentage before seeing your data. The assessment produces an itemized savings estimate per opportunity, with effort and risk, so you can decide what to implement based on evidence.
Will optimization affect performance or reliability?
It should not, if done carefully. We validate recommendations against utilization history and service requirements, review changes with application owners, and implement them gradually with monitoring. Risky items such as resizing databases are tested in non-production first, with a clear rollback plan. If a change would reduce headroom below what the business needs, we flag the trade-off instead of making it.
Do you need admin access to our Google Cloud organization?
No. The assessment needs read access to billing data, Recommender and monitoring metrics, which can be granted with predefined viewer roles. Implementation can be done through your own pipelines and change processes, or with scoped roles for specific projects. We follow your access policies and document every permission requested and why.
How do committed use discounts work, and are they risky?
Committed use discounts lower prices in exchange for a one- or three-year commitment, either to specific resources or to a level of spend on eligible services. The risk is committing to more than you use. We analyze steady-state usage, growth plans and planned architecture changes, recommend a conservative coverage level, and review it regularly so commitments grow with confidence rather than guesswork.
How long does a cost optimization engagement take?
An assessment usually takes two to three weeks. Implementation of quick wins, such as cleanup and scheduling, starts immediately, while larger items such as BigQuery redesign or GKE tuning run over four to eight weeks. Many clients then keep a monthly review with us to catch new waste and adjust commitments, which typically takes a few hours each month.
How is this different from your FinOps service?
Cost optimization focuses on finding and implementing specific savings in your current environment. FinOps builds the long-term operating model: cost allocation, dashboards, budgets, forecasting and shared accountability between finance and engineering. Optimization delivers results quickly, and FinOps keeps them in place. Many clients start with optimization to fund the effort and then extend into FinOps.