Compute
Services include EC2, Azure VMs, GCP Compute
Hundreds of ways to cut your cloud and AI bill.
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. Evaluate opportunities in the context of your workloads and verify the outcome after remediation.
HundredsOptimization Types
Multi-cloudCoverage
DozensServices Covered
5Layers of Waste
Five layers of infrastructure waste.
Explore five layers of waste, from common resource issues to architectural inefficiencies. Use detection findings and available context to investigate opportunities, review remediation, and measure savings after changes.
Layer 1 / Surface-Layer Waste
Orphaned, inactive, and abandoned resources still incurring charges. Investigate usage and dependencies before deciding which resources to change, then measure the effect of the implemented remediation.
Layer 2 / Configuration Optimization
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.
Layer 3 / Application Driven Waste
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.
Layer 4 / Data & Lifecycle Drift
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.
Layer 5 / Architectural Inefficiencies
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.
Every layer matters. Every layer compounds.
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.
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.
The broadest and deepest coverage.
DeepWaste analyzes dozens of cloud and AI services across all major providers, with new services added continuously.
Kubernetes
Services include EKS, AKS, GKE, ECS
Databases
Services include RDS, DynamoDB, Cosmos DB
AI & ML
Services include Bedrock, SageMaker, Azure OpenAI
Data & Analytics
Services include Snowflake, Databricks, BigQuery
Storage
Services include EBS, S3, Blob Storage
Serverless
Services include Lambda, Functions, Cloud Run
Networking
Services include VPC, CloudFront, Load Balancers
Observability
Services include CloudWatch, Datadog, Logging
Streaming
Services include MSK, Kinesis, Pub/Sub
Security
Services include Secrets Mgr, App Config
A taste of hundreds of optimizations.
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.
Expensive VPC NAT Gateway Deployment
Detects workloads routing massive data through NAT Gateways when free alternatives like Gateway Endpoints exist. Estimate the impact using applicable routing, endpoint, and data-transfer charges.
Schedulable EC2 Instances
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.
Serverless-Ready OpenSearch Domains
Identifies low-usage provisioned OpenSearch domains for a serverless comparison. Evaluate capacity charges, supported features, workload patterns, and migration requirements before assuming a saving.
Suboptimal Azure Disk Types
Investigates Azure managed-disk configuration and usage to identify potential tier changes. Validate latency, IOPS, throughput, and platform compatibility before a change.
GPU Instance Rightsizing
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.
Vertex AI Training Job Optimization
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?
Real customer impact
Turn detection findings into prioritized engineering work, then measure the effect of each implemented change against actual usage and billing.
Finding waste is step one. Fixing it is the whole point.
Investigate findings, prepare fixes for review, and delegate approved remediation to Coworkers in PointFive OS. Measure savings after changes are implemented.
AI Coding Agents
AI coding agents use available infrastructure context to generate proposed remediation for engineering review.
Built-In Workflows
Connect directly to Jira, Slack, ServiceNow, and your existing ticketing systems. Findings flow into your team's natural workflow, no context switching required.
Infrastructure-as-Code
Terraform and CloudFormation fixes ready to merge. Every remediation maps to your IaC stack, so changes go through your existing review and deploy pipeline.
Human Oversight at Every Step
Root-cause analysis, impact assessment, and safe rollback paths, all included. AI generates the fix. Your engineers review, approve, and deploy with full confidence.
From finding to fix in minutes, not months.
Traditional cost optimization ends with a spreadsheet. PointFive delivers validated findings with actionable remediation, so engineering teams ship savings instead of triaging alerts.
Dozens of services. Deep analysis on every one.
Not just visibility: DeepWaste analyzes usage patterns, access frequencies, and workload characteristics across dozens of services organized by infrastructure domain.
Compute
Kubernetes & Containers
Databases
Data Warehouses & Analytics
Storage
Serverless & Functions
Networking & CDN
Monitoring & Observability
Streaming & Messaging
AI & Machine Learning
Security & Configuration
Built different. Detects different.
Agentless architecture
Agentless discovery uses read-only access. Infrastructure changes require customer-authorized write permissions and approval before execution.
Research-driven detection
PointFive Labs researches infrastructure inefficiencies and develops detections using configuration, usage, and cost evidence.
Multi-source data correlation
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.
Agentic Remediation
AI coding agents deliver contextual recommendations with human-curated remediation playbooks. Teams can investigate causes, assess estimated impact, and review remediation plans before approving execution.
Frequently asked questions
PointFive's DeepWaste™ detection engine identifies over 500 types of cloud and AI waste across compute, storage, databases, networking, serverless, AI/ML, data & analytics, observability, streaming, security, and containers. Each detection is backed by usage patterns, configuration analysis, and validated remediation playbooks, not just billing thresholds.
Opportunities are prioritized, actionable savings recommendations surfaced by the DeepWaste engine. Each opportunity includes the resource affected, estimated savings, risk level, remediation steps, and can be assigned to a team member or pushed to Jira/ServiceNow. Opportunities are continuously updated as your environment changes.
PointFive's anomaly detection uses machine learning to establish baseline spending patterns and alerts you when costs deviate significantly. Each anomaly includes the affected service, cost impact, and detailed analysis showing actual vs. expected spending, so you can investigate and resolve cost spikes before they compound.