Data from 500+ DeepWaste™ detections across AWS, Azure, GCP, and Kubernetes. Real optimizations actioned — not theoretical projections.
Each layer detects an entirely new category of waste that other tools structurally cannot find.
Goes beyond idle checks to analyze how services are configured — storage classes, tiering policies, engine types, and retention settings that silently compound waste.
First-to-market detection for GenAI infrastructure — Bedrock inference, Azure OpenAI provisioned throughput, model-task mismatch, and GPU idle time.
Recommends when the resource type itself is wrong — serverless migration, database consolidation, scheduling patterns. Native tools size resources; PointFive questions the architecture.
Workload-level rightsizing for Deployments, DaemonSets, StatefulSets, and CRD-managed workloads. 100% agentless — no sidecar, no agent, no code on your clusters.
NetApp ANF, App Service Plans, Cloud Logging, MSK, FSx — the services that every other tool skips because they're too specialized. That's where the biggest per-finding savings hide.
Every layer matters. Every layer compounds.Most tools only cover the surface. PointFive goes five layers deep.
Highest reward. Least effort. Lowest risk.
These are the optimizations that are consistently overlooked — and the easiest to action.
DynamoDB tables using Standard storage when Standard-IA would save 60% on storage costs. No native tool detects this.
Seen in 85% of connected environments
Over-provisioned CPU/memory requests on Kubernetes workloads. Detected 100% agentlessly — no sidecar or DaemonSet deployed on your clusters.
Seen in 91% of connected environments
Standalone SQL databases that should consolidate into Elastic Pools or Managed Instances. Average savings of $32K per finding.
Seen in 35% of connected environments
GCS buckets without Autoclass enabled. GCP Recommender suggests per-object class changes but never recommends enabling Autoclass itself.
Seen in 78% of connected environments
Overpaying for GenAI inference through suboptimal model selection, oversized provisioned throughput, or missing batch processing opportunities.
Seen in 48% of connected environments
See the remaining opportunities with step-by-step remediation for each — plus the competitive comparison showing what other tools miss.
Includes free AI assessment of your infrastructure
Over-provisioned Azure NetApp Files capacity pools. Completely invisible to Azure Advisor and every competitor on the market.
Seen in 28% of connected environments
S3 Intelligent-Tiering configured without Deep Archive or Archive Access tiers enabled. Average $2.5K per finding.
Seen in 62% of connected environments
Expensive soft-delete retention policies on high-churn GCS buckets. A relatively new GCP feature that defaults to 7-day retention — costs compound silently.
Seen in 42% of connected environments
Most tools ignore DaemonSets entirely. But over-provisioned DaemonSet requests waste resources on every single node — the waste multiplies with cluster size.
Seen in 67% of connected environments
VMs with clear on/off usage patterns (dev/test environments) that should be auto-scheduled. Azure Advisor recommends shutdown — not scheduling.
Seen in 55% of connected environments
Provisioned OpenSearch domains that should migrate to Serverless based on actual workload patterns. No native tool suggests architecture migration paths.
Seen in 42% of connected environments
Azure OpenAI provisioned throughput units (PTUs) running in non-production environments where pay-as-you-go would be significantly cheaper.
Seen in 22% of connected environments
These numbers are from our customer base. Your environment has its own hidden waste. Get a free, agentless assessment — no code deployed, no commitment.
Average time to ROI: 10 days · 1200%+ average customer ROI · Net Zero Guarantee