Whitepaper 'FinOps and cost management for Kubernetes'
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Ebook 'From FinOps to proven cloud cost management & optimization strategies'
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

Big Data profiling: enhance performance and optimize infrastructure costs

Identify bottlenecks and get dozens of tangible improvement recommendations
Big-Data-task-profiling-cost-optimization-Hystax

Big Data profiling and identifying bottlenecks

Big-Data-tasks-with-minimal-infrastructure-cost

Run Big Data tasks with minimal infrastructure cost

Recommendations_for_optimization_performance_and_infrastructure

Recommendations to optimize performance and infrastructure

Support-Big-Data-technologies-and-programming-languages

Support of various Big Data technologies and programming languages

Big Data profiling and identifying bottlenecks

Integrating with Big Data jobs profiling OptScale analyzes inside and outside metrics to identify profiling issues and bottlenecks. Extensive Data job profiling is a complex process that depends on a defined hyperparameter set, hardware, or cloud resource usage. After the analysis, the engineering team can identify the bottlenecks to optimize performance and infrastructure.

Big-data-profiling-identifying-bottlenecks
Run-Big-Data-tasks-with-minimal-infrastructure-cost

Run Big Data tasks with minimal infrastructure cost

Infrastructure costs for running Big Data tasks depend on different factors such as the data size, the resources’ geographic location (network traffic between cloud regions could be paid), the complexity of the algorithms, and the specific configuration needs in data processing, for example, for GCP. OptScale is designed to optimize the cloud infrastructure, minimize costs, and ensure that resources are used efficiently.

Recommendations to optimize performance and infrastructure

By addressing the bottlenecks in the Big Data job profiling process, OptScale helps improve cloud infrastructure for better performance. OptScale highlights the issues and offers clear recommendations to optimize cloud usage. The recommendations include utilizing Reserved/Spot instances and Saving Plans, rightsizing and instance family migration, network traffic optimization, and IOPS inconsistencies that data transformations or model code inefficiencies can cause.

runsets to identify efficient ML-AI model training results
Support-various-Big-Data-technologies-programming-languages

Support of various Big Data technologies and programming languages

OptScale is a solution for engineering teams using different big data technologies (such as Apache Hadoop or Apache Spark) and programming languages, that offers an opportunity to deliver OptScale to a wide range of companies. Moreover OptScale is built as an open-source platform to help businesses of any size improve Big Data job profiling by getting optimal performance and infrastructure costs.

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 25th October and discover how OptScale allows running ML/AI or any type of workload with optimal performance and infrastructure cost.