Overview
Cloudamize Express is designed to help customers and partners produce a quick, directional analysis of their infrastructure. Cloudamize Express aggregates data directly from flat-file uploads (such as RVTools, CUR, or CMDB) to rapidly generate actionable cost optimization insights.
Cloudamize Express vs. Cloudamize Standard
While Cloudamize Express enables rapid turnaround with minimal setup, it is important to understand its assumptions and limitations compared to Cloudamize Standard.
|
Feature / Analysis |
Cloudamize Standard(Live Data Collection) |
Cloudamize Express(Flat-File Aggregated) |
|
Data Source |
Live data collection via Agent/Agentless |
Data aggregated from flat-files |
|
Like-for-Like TCO Recommendations |
Baseline |
Baseline |
|
Right-Sizing Optimization |
High Confidence (Live Data Analytics) |
Low Confidence (Estimated from CUR Usage) |
|
FSxN Storage Optimizations |
Live drive data |
Drive data from RVTools |
|
Graviton Savings Analysis |
OS & app-level analysis |
OS analysis only |
|
Zombie Server Analysis |
High confidence mix of telemetry & app dep. data |
Low confidence estimate based on RVTools |
|
VMware Licensing Analysis |
Supported |
Supported via flat-file mapping |
|
EOL (End of Life) Analysis |
Contains both Server and SQL |
Limited to Server Only |
|
Application Dependency Mapping |
Live mapping with density data |
Assumes CMDB file is up to date (no density info) |
|
SQL License Optimization |
Agent to ID Prod/Dev + app dep data for SQL location |
RVTools to ID Prod/Dev + CMDB for SQL location |
|
SQL Core Optimization |
Access to core-level optimizations |
N/A |
What to Expect Based on Input Files
Each flat-file type contains different levels of information, which Cloudamize uses to infer specific analyses. Future releases will enable simultaneous loading of multiple flat-file types (such as RVTools and CMDB) to expand the scope of analysis.
Flat-File Definitions
-
RVTools: A single snapshot in time containing OS-level and drive-level information.
-
CUR (Cost and Usage Report): Financial-based average usage data used to extrapolate right-sizing estimates.
-
CMDB: Server-to-application data used to identify SQL databases and map dependencies.
|
Analysis Capability |
RVTools |
CUR |
CMDB (Coming Soon) |
|
Like-for-Like TCO Recommendations |
✔ |
✔ |
✔ |
|
Right-Sizing Optimization |
|
✔ |
|
|
FSxN Storage Optimizations |
✔ |
✔ |
|
|
Graviton Savings Analysis |
✔ |
✔ |
✔ |
|
AMD Optimization Analysis |
|
✔ |
|
|
Zombie Server Analysis |
✔ |
|
|
|
VMware Licensing Analysis |
|
|
✔ |
|
Server EOL Analysis |
✔ |
|
✔ |
|
Application Dependency Mapping |
|
|
✔ |
|
SQL License Optimization |
|
|
✔ |
Assessment Overview & Key Metrics
The Assessment Overview condenses key findings, current TCO projections, and total potential savings across the three main savings paths.
-
Total Assessed Infrastructure: Summarizes the total number of active servers/virtual machines.
-
Current Annual TCO: Overall baseline spend before optimizations.
-
Design Strategy: Benchmarks derived from specific CPU/architecture optimizations (e.g., AMD Optimized benchmarks built on FFmpeg, NGINX, Redis, Stream, MySQL, and SQL Server Workloads).
Note: Servers can reside within multiple Savings Paths. Once you have executed a path, rerun the assessment analysis to receive updated insights on remaining paths.
Optimization Motions & Mechanics
Cloudamize Express projects financial savings and performance gains across three distinct optimization paths:
Motion 1: Hourly Cost Optimization
-
Goal: Achieve similar or higher performance at the lowest available cost.
-
Mechanics: Generally applied to pre-3rd Gen AMD instances (or competitive equivalents) by upgrading to 3rd Gen (Milan) AMD EPYC chipsets.
-
Implementation Strategy: Ideal first-step / quick-win because it typically requires no revalidation of applications.
-
Example:
r4.16xlarge→r6a.16xlarge
Savings: $5.4K | Performance Uplift: +81%
Motion 2: Modernize
-
Goal: Maintain instance size while reducing estimated runtime with higher-performance options.
-
Mechanics: Keeps core sizing identical while leveraging faster generation processors to dramatically increase performance, thereby reducing required running hours.
-
Example:
m5.24xlarge(730 hrs) →m8a.24xlarge(179 hrs)
Savings: $27.5K | Performance Uplift: +309%
Motion 3: Modernize & Downsize
-
Goal: Reduce instance size by ~50% when a ∼2x or greater performance option is available.
-
Mechanics: Upgrade hardware generation while cutting total instance footprint. Helps build the framework for long-term operational savings.
-
Example:
m6i.32xlarge→m8a.12xlarge
Savings: $27.8K | Performance Uplift: +21%
Assessment Implementation Workflow
-
Step 1: Execute Quick Wins
Start with Hourly Cost Optimization to realize immediate savings without application disruption. (Note: If preferred, teams can jump directly to Modernize & Downsize). -
Step 2: Transition to Standard Analysis
To validate Modernizing and Downsizing estimates with complete accuracy, run a Free Standard Analysis to collect live telemetry and app dependency data. -
Step 3: Iterative Execution
Servers can qualify for multiple savings paths. Executing one path alters the baseline, so re-run the assessment after changes to recalculate remaining opportunities.
Sample Report for Cloudamize Express Summary Deck cloudamize-express-amd-summary-test-demo-express-2026-07-01.pptx
If you have any queries, please get in touch with the helpdesk via our Helpdesk Portal or by email at helpdesk@cloudamize.com.