Best GCP Cost Optimization Tools in 2026
Google Cloud Platform offers competitive pricing and unique features like sustained use discounts, but optimization still requires the right tools. Organizations typically overspend 25-35% on GCP resources. Here's our guide to the best GCP cost optimization tools for 2026.
Common GCP Cost Challenges and Leakage Areas
Before diving into tools, it's important to understand where GCP costs typically leak. These are the most common areas where organizations lose money:
Top GCP Cost Leakage Areas
- Oversized Compute Engine instances - VMs running at 10-20% CPU utilization while paying for full capacity
- Idle and orphaned resources - Unattached persistent disks, unused static IPs, idle Cloud SQL instances
- BigQuery inefficiencies - Full table scans, no partitioning, lack of slot reservations for predictable workloads
- Missing Committed Use Discounts - Paying on-demand rates for predictable, steady-state workloads
- GKE over-provisioning - Pods requesting more CPU/memory than they use, leading to cluster bloat
- Storage lifecycle gaps - Data sitting in standard storage that should be in Nearline, Coldline, or Archive
- Network egress costs - Unoptimized data transfer between regions and to the internet
Manual GCP Cost Optimization: How to Do It Yourself
You can optimize GCP costs manually, but it requires significant time and expertise. Here's how to approach each area:
1. Compute Engine Right-Sizing (Manual)
Export metrics from Cloud Monitoring to analyze CPU, memory, and disk utilization over 2-4 weeks. Identify instances consistently below 40% utilization and manually resize to smaller machine types. Check the Recommender API for suggestions.
Time required: 4-8 hours/week for a medium-sized environment
2. Identifying Idle Resources (Manual)
Use Cloud Console to find unattached disks, unused IPs, and idle instances. Write custom scripts to query the Asset Inventory API. Manually delete or stop resources after confirming they're not needed.
Time required: 2-4 hours/week for ongoing cleanup
3. BigQuery Optimization (Manual)
Review INFORMATION_SCHEMA queries to find expensive jobs. Implement partitioning and clustering on large tables. Set up slot reservations for predictable workloads. Train teams on query best practices.
Time required: 8-16 hours initial setup, 2-4 hours/week ongoing
4. CUD Planning (Manual)
Analyze 3-6 months of usage data to identify stable workloads. Calculate optimal commitment levels using spreadsheets. Purchase CUDs through the console and track utilization monthly.
Time required: 8-12 hours for analysis, quarterly reviews
Why Automated Tools Are Essential
While manual optimization is possible, it has significant limitations:
Manual Approach Challenges
- Time-consuming (20+ hours/week)
- Requires deep GCP expertise
- Point-in-time analysis misses patterns
- No real-time anomaly detection
- Error-prone manual implementations
Automated Tool Benefits
- Continuous 24/7 monitoring
- AI-powered pattern recognition
- Real-time cost anomaly alerts
- One-click optimization execution
- Cross-service correlation insights
Key GCP Cost Optimization Areas
- Compute Engine right-sizing - Match machine types to workloads
- Committed Use Discounts - Up to 57% savings with 1-3 year commitments
- Preemptible/Spot VMs - Up to 80% savings for batch workloads
- BigQuery optimization - Slot reservations and query efficiency
- GKE optimization - Pod right-sizing and Autopilot analysis
Top 5 GCP Cost Optimization Tools
1. DeepCost
AI-powered GCP cost optimization with Compute Engine right-sizing, CUD recommendations, GKE optimization, and BigQuery cost management.
Pros
- 45% average GCP savings
- CUD advisor
- GKE optimization
- BigQuery analysis
- Autopilot support
Cons
- Full automation requires Pro plan
2. Google Cloud Cost Management
Native GCP tool for cost visibility, budgets, and Recommender API insights. Free but limited automation.
Pros
- Free with GCP
- Native integration
- Recommender insights
Cons
- Basic automation
- Manual implementation
- Limited analytics
3. CAST AI
Kubernetes-focused optimization with strong GKE support and automated spot instance management.
Pros
- Strong GKE support
- Automated spot
- Cluster right-sizing
Cons
- Kubernetes only
- No BigQuery
- Limited non-K8s optimization
4. Spot.io by NetApp
Compute optimization with Preemptible/Spot VM management and GKE Ocean for container workloads.
Pros
- Preemptible management
- Ocean for GKE
- Automatic fallback
Cons
- Compute focused
- No storage/BigQuery
- Learning curve
5. Harness Cloud Cost Management
DevOps platform with cloud cost management module for GCP optimization and governance.
Pros
- DevOps integration
- Governance features
- CI/CD cost tracking
Cons
- Complex platform
- Steep learning curve
- DevOps focused
Our Recommendation
For comprehensive GCP cost optimization, DeepCost offers the best combination of features. It covers Compute Engine, GKE, BigQuery, and storage optimization with AI-powered recommendations, delivering 45% average GCP savings.
If you're exclusively focused on GKE, CAST AI provides excellent Kubernetes-specific optimization. For Preemptible/Spot management, Spot.io offers robust automation. Start with Google Cloud Cost Management for basic visibility, then add a dedicated tool for automation.