Multi-Engine Cloud Database Cost Assessment
A deep-dive technical and economic evaluation of your database estate across Google Cloud Platform (GCP), Amazon Web Services (AWS), and Microsoft Azure to identify quantifiable savings, architectural risks, and long-term optimization paths across PostgreSQL, MySQL, Oracle, Microsoft SQL Server, and Neo4j.
What We Analyze in Your Estate
We don't just look at aggregate monthly cloud invoices. We evaluate granular database telemetry, query workloads, licensing structures, and underlying infrastructure configurations.
Compute & Core Utilization
We profile CPU peaks, sustained baseline utilization, and RAM caching efficiency across primary, standby, and replica nodes to eliminate idle core provisioning and unnecessary tier markups.
Storage & Non-Production Branching
We audit allocated vs actual disk consumption, provisioned IOPS throughput, and evaluate whether decoupled storage architectures like Neon can eliminate storage duplication across test environments.
Replica Topology & Sizing
We evaluate whether read replicas and multi-Availability Zone standby instances are right-sized for actual read traffic or wasting budget mirroring idle primaries.
Backup & Snapshot Retention
We inspect automated snapshot policies, Write-Ahead Log (WAL) archive accumulation, Point-in-Time Recovery (PITR) storage, and secondary region duplication costs.
Discounts & Core Licensing
We evaluate reserved capacity, committed-use discounts, and Oracle / SQL Server core software licensing multipliers to eliminate unnecessary enterprise fee markups.
Network Egress & Cross-AZ Traffic
We identify hidden cross-zone replication traffic, inter-service database calls, and public IP data egress costs that silently inflate monthly invoices.
What You Receive: Assessment Deliverables
You receive clear, executive-ready documentation and concrete engineering runbooks—not high-level generic advice.
Current-State Total Cost of Ownership (TCO) Model
A transparent financial breakdown mapping your current spend by engine, compute tier, storage volume, IOPS, replica, and network egress.
Immediate In-Place Optimization Opportunities
Concrete rightsizing, storage tuning, and commitment actions that reduce your cloud bill without migrating databases.
Architecture & Modernization Alternatives
Detailed technical evaluation comparing managed cloud services, hybrid models, and Kubernetes operator deployments (CloudNativePG, StackGres).
Risk Considerations & Migration Safeguards
Thorough evaluation of operational risks, team skillset requirements, Recovery Point Objective (RPO) and Recovery Time Objective (RTO) impacts, and rollback procedures.
Step-by-Step Implementation Roadmap
A prioritized execution plan designed for your platform engineering team, complete with estimated engineering effort and payback timelines.
Executive Briefing & Presentation
A dedicated 60-minute findings presentation with Chief Technology Officers (CTOs), Vice Presidents of Engineering, and technical leaders.
Frequently Asked Questions About the Assessment
How long does the assessment take?
Most assessments are designed to complete within approximately two weeks once required telemetry and billing information are available, requiring minimal engineering time from your internal team (typically 2–4 hours for discovery alignment and read-only access setup).
Does CloudDBOptimize require access to our sensitive customer data?
No. We do not require access to your database records or sensitive customer data. We only require read-only access to infrastructure metadata, cloud billing line items, performance telemetry (CloudWatch, Cloud Monitoring, Azure Monitor), and database engine configuration parameters.
Will this disrupt our production environments?
No. Assessment discovery is designed to be read-only and non-disruptive. We do not run intrusive performance benchmarks or modify any running database infrastructure.
What if our team doesn't have Kubernetes expertise?
That is completely fine. We do not force Kubernetes. If in-house Kubernetes skills are absent, we focus entirely on optimizing your existing Google Cloud SQL, Amazon RDS, Aurora, or Azure database instances to maximize efficiency without introducing operational complexity.