GCP OptimizationCoverage across Compute Engine, GKE, Cloud SQL, BigQuery, Cloud Run, Vertex AI and more. See every dollar, find waste others miss, and prove the savings.
See every dollar across GCP services, compute, storage, databases, BigQuery, and networking.
DeepWaste™ uncovers what GCP Recommender misses, from persistent disk waste to logging bloat.
Findings flow to where engineers work, with tracking to prove what was fixed.
Storage class, lifecycle, and configuration choices silently inflate GCS bills. PointFive analyzes tiering, lifecycle, and configuration at the bucket level, including usage metrics and operation patterns.
AI/ML spend compounds quickly across endpoints, notebooks, and training pipelines, often without anyone noticing. PointFive detects idle endpoints, underutilized notebooks, and oversized training jobs across the full Vertex AI surface.
GCP Recommender handles CPU/memory rightsizing but doesn't analyze storage provisioning vs. actual usage. Over-provisioned Cloud SQL storage is invisible to native tools.
Recommender only targets individual VMs. PointFive analyzes MIG templates and recommends machine type changes at the template level, rightsizing across all instances in the group.
Log routing and storage tier configurations that silently accumulate cost. PointFive analyzes Cloud Logging bucket architecture for cost efficiency, routing verbose logs to cheaper storage.
Agentless Kubernetes optimization for Deployments, DaemonSets, StatefulSets, and CRD-managed workloads on GKE, no sidecar or agent deployed on your clusters.
BigQuery is often the fastest-growing, least-owned line on a GCP bill. PointFive maps spend from the organization down to the project, dataset, table, and job level.
One billing-model optimization across 435 datasets generated $330K+ in annualized savings, enough to cover the cost of PointFive. PointFive analyzes storage behavior over time to identify when a different billing model can reduce costs, rather than relying on a single point-in-time comparison.
PointFive sees your GCP environment and every layer above it: Kubernetes workloads on GKE, AI training on Vertex AI, analytics on BigQuery. We coordinate Cloud Storage lifecycle policies with application patterns, detect logging architecture inefficiencies, and find savings at the intersection that no point tool ever could.
Agentless deployment in under 15 minutes. No agents, no code changes, no downtime.
Continuously monitors and acts on your environment with intelligent automation.
Full coverage across cloud, PaaS, data & AI. No blind spots, no missed savings.
Engineers fix issues in the tools they already use: Slack, Jira, Teams, ServiceNow.
PointFive uses a read-only service account with minimal IAM permissions scoped to your project. Setup takes under 15 minutes through our guided onboarding.
PointFive covers 120+ detection types across Compute Engine, GKE, Cloud SQL, BigQuery, Cloud Run, Cloud Storage, Cloud Functions, Vertex AI, and more.
GCP Recommender offers surface-level suggestions. PointFive uses DeepWaste™ to find architectural waste, like Cloud Logging bucket inefficiencies and cross-service optimization, that Recommender doesn't detect.
Yes. PointFive breaks down BigQuery compute and storage spend by project, dataset, table, and job. It finds unused tables and pipelines still writing to data nobody reads, mapped from the jobs that actually ran, and flags datasets better suited to logical or physical storage billing. Time-travel and fail-safe optimization, plus slot, reservation, and commitment optimization, are coming soon.
PointFive analyzes BigQuery at the dataset level and looks at storage behavior over time to identify where a different billing model can reduce cost. In one customer environment, a billing-model optimization across 435 datasets cut storage costs 82% within five days, generating more than $330K in annualized savings, enough to cover the cost of PointFive.
Connect your GCP project in under 15 minutes. See your first optimization opportunities within 48 hours.