PointFive's DeepWaste™ Detection Engine finds cloud waste that no other tool can — from quick wins to deep architectural inefficiencies — across AWS, Azure, GCP, OCI, Snowflake, Databricks, Kubernetes, and AI platforms. Our baseline identifies 15–30% waste reduction, and customers usually find significantly more.
PointFive categorizes every finding into five layers of depth — from common resource waste to deep architectural inefficiencies. At every layer, validated detection, rich context, and engineering-ready remediation turn findings into real savings.
Orphaned, inactive, and abandoned resources still incurring charges. Most tools flag some of these — but accurate identification across millions of resources requires validated detection, dependency mapping, and engineering-ready remediation to actually turn findings into savings.
Misaligned settings versus real usage — storage tiers, disk types, capacity modes, instance sizes, and caching configurations. Validating these at scale demands correlation of performance metrics, usage patterns, and workload context to produce recommendations engineers actually trust and act on.
Waste created by how applications use cloud services — API call patterns, model selection, transfer routing. Invisible without deep application-level analysis and multi-source data correlation.
Data that has aged out of its original purpose but stays in expensive storage tiers. Retention policies that no longer reflect actual access patterns. Requires continuous monitoring of data access frequency and intelligent lifecycle recommendations.
Suboptimal infrastructure design causing structural cost overhead — routing decisions, region placement, cluster sizing. Requires VPC Flow Logs, snapshot side-scanning, and virtual cost allocation techniques unique to PointFive. The hardest to find, often the most impactful to fix.
Real savings come from continuous detection across all five layers — with validated context, root-cause analysis, and engineering-grade remediation at every depth. Surface findings fund the deeper work. Deeper layers prevent waste from recurring.
Increasing detection depth
What sets PointFive apart isn't just finding waste — it's the validated context, multi-source correlation, and remediation tooling that turns findings into action across every layer. Deeper layers require VPC Flow Logs, snapshot side-scanning, and virtual cost allocation — techniques unique to PointFive.
DeepWaste analyzes 85+ cloud and AI services across all major providers, with new services added continuously.
Compute
5 services
EC2 · Azure VMs · GCP Compute
Kubernetes
9 services
EKS · AKS · GKE · ECS
Databases
13 services
RDS · DynamoDB · Cosmos DB
AI & ML
10 services
Bedrock · SageMaker · Azure OpenAI
Data & Analytics
6 services
Snowflake · Databricks · BigQuery
Storage
11 services
EBS · S3 · Blob Storage
Serverless
4 services
Lambda · Functions · Cloud Run
Networking
9 services
VPC · CloudFront · Load Balancers
Observability
7 services
CloudWatch · Datadog · Logging
Streaming
7 services
MSK · Kinesis · Pub/Sub
Security
4 services
Secrets Mgr · App Config
A curated sample of what our DeepWaste™ engine detects — each backed by usage patterns, configuration analysis, and validated remediation playbooks. The real catalog runs much deeper.
Detects workloads routing massive data through NAT Gateways when free alternatives like Gateway Endpoints exist. A single endpoint deployment can eliminate hundreds of thousands in annual data processing fees.
Identifies instances with low off-hours activity that can be automatically started and stopped on a schedule. Eliminates waste from resources running 24/7 when only needed during business hours.
Identifies low-usage provisioned OpenSearch domains that are candidates for serverless migration — eliminating fixed instance costs for intermittent workloads.
Universal detection across all Azure managed disk types — Premium SSD, Standard SSD, and Standard HDD — identifying disks that can be downtierred based on actual IOPS and throughput patterns.
Identifies GPU instances (P4d, P5, G5, Inf2) running ML training and inference workloads at low GPU utilization — recommending smaller instance types or spot-based alternatives without impacting throughput.
Detects Vertex AI custom training jobs using oversized machine types or running without preemptible/spot instances — common in ML experimentation workflows where cost discipline is often overlooked.
Showing 6 of 10 detections in this category. Want to see what's hiding in your infrastructure?
Example customer achieved full ROI in 10 days. Another saved $600K from a single NAT Gateway endpoint deployment. Our average customer ROI exceeds 500% — and the real number? You wouldn't believe it.
Run a free Proof of Value on your own cloud environment and see exactly which of the 500+ detections apply to your infrastructure — with dollar amounts attached.
Every optimization includes engineering-grade remediation — from AI coding agents that generate contextual fixes to built-in workflow integrations that keep your team moving. Detection without action is just a report. PointFive delivers outcomes.
Every finding ships with contextual, engineering-ready remediation — generated by AI coding agents that understand your infrastructure, not generic templates.
Connect directly to Jira, Slack, ServiceNow, and your existing ticketing systems. Findings flow into your team's natural workflow — no context switching required.
Terraform and CloudFormation fixes ready to merge. Every remediation maps to your IaC stack — so changes go through your existing review and deploy pipeline.
Root-cause analysis, impact assessment, and safe rollback paths — all included. AI generates the fix. Your engineers review, approve, and deploy with full confidence.
Traditional cost optimization ends with a spreadsheet. PointFive delivers validated findings with actionable remediation — so engineering teams ship savings instead of triaging alerts.
Not just visibility — DeepWaste™ analyzes usage patterns, access frequencies, and workload characteristics across 90+ services organized by infrastructure domain.
The only agentless Kubernetes optimization solution on the market. Read-only access, zero deployment overhead, minutes to first insight.
A dedicated Cloud Cost Research Team ships ~10 new optimization types weekly — using methodologies inspired by cybersecurity threat intelligence.
We don't just read your bill. We correlate billing data with CloudWatch metrics, VPC Flow Logs, CloudTrail activity, and direct API state — finding waste that single-source tools miss.
AI coding agents deliver contextual recommendations with human-curated remediation playbooks. Every opportunity includes root cause analysis, impact assessment, and engineering-grade execution paths—with full human oversight at every step.
Get a free assessment of your cloud waste. Most customers find 15–30% savings within the first week.