Whitepaper 'FinOps and cost management for Kubernetes'
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OptScale — FinOps
FinOps overview
Cost optimization:
AWS
MS Azure
Google Cloud
Alibaba Cloud
Kubernetes
OptScale — MLOps
ML/AI Profiling
ML/AI Optimization
Big Data Profiling
OPTSCALE PRICING
Acura — Cloud migration
Overview
Database replatforming
Migration to:
AWS
MS Azure
Google Cloud
Alibaba Cloud
VMWare
OpenStack
KVM
Public Cloud
Migration from:
On-premise
Acura — DR & cloud backup
Overview
Migration to:
AWS
MS Azure
Google Cloud
Alibaba Cloud
VMWare
OpenStack
KVM

Kubernetes cost management and FinOps

Kubernetes cost management and optimization require a different approach
K8s cost optimization and management
Cloud FinOps for Kubernetes

OptScale is a powerful solution, reducing Kubernetes cost by 35%

Monitor cloud costs within Kubernetes container infrastructure to keep the same performance while reducing your cloud bill and forecasting IT spending.

Cost visibility and transparency for Kubernetes

Cost transparency across teams, applications and individual PODs

Kubernetes cost management and forecast

TTL rules, budget constraints & cost anomaly detection

Kubernetes bill reduction and optimization

How to optimize Kubernetes cost and reduce expenses

When it’s time to analyze a Kubernetes cost, you may find it challenging as your bill won’t include all the necessary workload information. For example, the cloud bill will not match the price for specific business purposes. The cost of a cluster consists of the cost of each active server on which it runs. However, it’s crucial to have a detailed Kubernetes cost analysis to run a business effectively.

OptScale offers complete cost transparency and hundreds of optimization scenarios so that your IT budget is under control and Kubernetes expenses are significantly reduced.

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Kubernetes cost visibility and transparency

Boost your awareness with complete visibility. Get real-time resource detection and map out your K8s cost to a specific business unit, team, individual engineer, or project within your organization, enabling you to achieve complete cloud cost transparency. Analyze expenses across multiple clusters on a single dashboard. Identify the cost of each deployment, service, or namespace. Receive real-time alerts and notifications to reduce costs continuously.

Kubernetes cost management enables you to simplify daily tasks and optimize cloud bills

Kubernetes cost optimization insights

Enable various optimization scenarios involving all the common ways to manage your Kubernetes cloud costs. OptScale offers the best option for each cluster, including the following variants: applying cloud discounts such as reserved instances, pod rightsizing, node rightsizing (VMs rightsizing), autoscaling, and rebalancing fragmented nodes. OptScale’s real-time rightsizing hints and recommendations help track underutilized resources and downgrade them before they result in a vast budget overrun.

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TTL rules, budget constraints, alerts and notifications

Immediately catch your budget overruns with a solid TTL rules system, budget constraints, and cost anomaly detection. OptScale enables your IT team to track all events and actions for audit purposes, set budget thresholds and receive real-time notifications. Slack integration helps  engage engineers in FinOps implementation and cloud cost savings.

News & Reports

FinOps and MLOps

A full description of OptScale as a FinOps and MLOps open source platform to optimize cloud workload performance and infrastructure cost. Cloud cost optimization, VM rightsizing, PaaS instrumentation, S3 duplicate finder, RI/SP usage, anomaly detection, + AI developer tools for optimal cloud utilization.

FinOps, cloud cost optimization and security

Discover our best practices: 

  • How to release Elastic IPs on Amazon EC2
  • Detect incorrectly stopped MS Azure VMs
  • Reduce your AWS bill by eliminating orphaned and unused disk snapshots
  • And much more deep insights

FinOps and cloud cost optimization for ML/AI workloads

Join our live demo on 27th 
March and discover how OptScale allows running ML/AI or any type of workload with optimal performance and infrastructure cost.