What Makes a Great Cloud Cost Optimization Platform: A Buyer's Guide
The cloud cost optimization platform market is crowded with vendors promising dramatic savings through automation, AI, and real-time insights. Most of them find real savings - the question is whether they find them safely, route them to the right people, and actually get them implemented.
This buyer's guide focuses on the factors that separate platforms that reduce your AWS bill from platforms that produce impressive dashboards while your costs continue climbing.
The Capabilities That Actually Matter
Continuous Discovery vs. Scheduled Scans
Platforms that run periodic scans (daily, weekly) miss cost waste created between scans. A development team that provisions a fleet of r5.4xlarge instances for a one-week load test and forgets to terminate them may not appear in your next weekly scan until they've already cost thousands.
Continuous discovery catches waste as it appears - not days or weeks later.
ML-Driven Right-Sizing vs. Threshold-Based Rules
The most common cost optimization action - right-sizing EC2 instances - is also the most risky if done incorrectly. A threshold-based rule ("flag anything under 10% CPU") will generate dozens of recommendations, many of which will be wrong because CPU utilization alone doesn't tell the full story.
ML-driven right-sizing considers CPU, memory, network throughput, storage I/O, and workload patterns together. It learns that your batch job runs at 80% CPU for 2 hours on Tuesday nights and correctly doesn't flag that instance as underutilized.
Remediation Safety Controls
| Feature | Why It Matters |
|---|---|
| Dry-run preview | See exactly what will change before it happens |
| Blast radius analysis | Understand dependencies before modifying |
| Scheduled remediations | Implement during approved maintenance windows |
| Rollback capability | Undo a change that caused unexpected issues |
| Evidence collection | Document what changed for compliance and audit |
Dedups.ai includes all of these. Every optimization recommendation can be previewed, scheduled, and tracked - so your engineering team can implement savings confidently.
Engineering Workflow Integration
An optimization platform that presents savings in a dashboard that nobody monitors isn't solving the workflow problem - it's just adding another dashboard.
Ask vendors: How does a right-sizing recommendation reach the engineer responsible for that instance? What does the Jira ticket look like? What information does the Slack notification include? If the answer is "engineers log into our portal to check," adoption will be low.
Realized Savings Tracking vs. Theoretical Savings
Any optimization platform can tell you the theoretical savings if all recommendations were implemented. Fewer track actual realized savings - the confirmed reduction in your AWS bill after each recommendation is implemented.
Realized savings tracking serves two purposes: it shows you which optimization types are actually being implemented (vs. accepted in the platform but never executed), and it provides the ROI data you need to justify continued investment in cost optimization.
Comparison: Platform Capabilities
| Capability | Basic Cost Tool | Dedups.ai |
|---|---|---|
| Continuous discovery | Sometimes | ✅ |
| ML right-sizing | Sometimes | ✅ |
| Reserved Instance optimization | ✅ | ✅ |
| Savings Plans optimization | ✅ | ✅ |
| Dry-run preview | ❌ | ✅ |
| Engineering workflow routing | ❌ | ✅ (Jira/Slack/email) |
| Realized savings tracking | Partial | ✅ |
| Security posture + cost combined | ❌ | ✅ |
| Compliance evidence | ❌ | ✅ |
| Multi-account support | ✅ | ✅ |
Questions to Ask Vendors
- How are recommendations routed to the engineers responsible for implementation?
- What safety controls exist before a remediation is applied?
- How do you measure and report realized savings (not theoretical)?
- How does your right-sizing account for workload variability (not just average CPU)?
- Does the platform cover security posture alongside cost, or only cost?
- What's the implementation timeline from first connection to first savings?
Ready to Get Started?
A cloud cost optimization platform should reduce your AWS bill and improve your team's efficiency - not add another tool to manage. Dedups.ai provides ML-driven cost optimization with safe, scheduled remediations, engineering workflow integration, and realized savings tracking - integrated with cloud security posture management in a single platform.