ZopNight optimizes non-production cloud spend by scheduling resources to your working hours, flagging the idle and oversized, and attributing the rest with showback. This is the full catalog, organized by service, cloud, industry, and role. Start with scheduling AWS EC2 or the FinOps discipline behind it, and compare ZopNight head to head under compare.
Optimize by service
Service-specific guides to scheduling and rightsizing across AWS, GCP, and Azure.
- AWS EC2
- AWS RDS
- AWS EKS
- AWS ECS
- AWS Lambda
- AWS ElastiCache
- AWS Redshift
- AWS SageMaker
- AWS EMR
- AWS Auto Scaling Groups
- AWS S3
- AWS EBS
- AWS CloudFront
- AWS DynamoDB
- AWS App Runner
- GCP Compute Engine
- GCP Cloud SQL
- GCP GKE
- GCP Cloud Run
- GCP Memorystore
- GCP BigQuery
- GCP Cloud Functions
- GCP Artifact Registry
- Azure Virtual Machines
- Azure AKS
- Azure SQL Database
- Azure Functions
- Azure VM Scale Sets
- Azure Databricks
- Azure ML Compute
Optimize by cloud
Optimize by use case
- Cost Optimization
- Resource Scheduling
- Idle Resource Detection
- Rightsizing
- FinOps Automation
- Orphan Resource Cleanup
- Cloud Cost Reporting
- Multi-Cloud Management
- Event Readiness
- Cost Anomaly Detection
- Showback and Cost Attribution
- Smart Tags
- Autoscaler Tuning
- AI Cloud Management
- One-Click Auto-Remediation
- Unit Economics
- Kubernetes Cost Management
- Security & Cost
- Pre-Flight Impact Analysis
- Workload Reliability
By industry and role
Questions we get a lot.
If yours isn't here, email us and we'll answer directly.
Where should I start?
Start with the FinOps overview for the discipline that ties it together, then use the specific guide, cloud, or location page for the task you are on.
Is production at risk?
No. Production is excluded by default; ZopNight acts only on the non-production resources you choose.
What access does ZopNight need?
A read-only role to start. Discovery, cost reporting, and recommendations all run read-only, and you enable actions when you are ready.
Which clouds are supported?
AWS, GCP, and Azure from one platform, including Databricks across all three. Schedules, groups, overrides, and recommendations work the same way regardless of provider.