Whitepaper 'FinOps and cost management for Kubernetes'
OptScale is fully available as an open source solution under Apache 2.0 on GitHub
Ebook 'From FinOps to proven cloud cost management & optimization strategies'

Big Data profiling: enhance performance and optimize infrastructure costs

Identify bottlenecks and get dozens of tangible improvement recommendations

Recognized by Forrester as a leading cloud cost management solution

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

By integrating with Big Data jobs profiling OptScale gives a deep analysis of inside and outside metrics to identify profiling issues and bottlenecks. Big Data job profiling is a complex process, which depends on a defined hyperparameter set, hardware, or cloud resource usage. After the analysis is complete, the engineering team will be able to 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, like for GCP. OptScale is designed to optimize the cloud infrastructure, minimize cloud costs and ensure that resources are used efficiently.

Recognized by Forrester as a leading cloud cost management solution

Recommendations to optimize performance and infrastructure

By addressing the bottlenecks in 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 can be caused by data transformations or model code inefficiencies.

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 & Test Environment Management

A full description of OptScale as a FinOps and Test Environment Management platform to organize shared IT environment usage, optimize & forecast Kubernetes and cloud costs

From FinOps to proven cloud cost management & optimization strategies

This ebook covers the implementation of basic FinOps principles to shed light on alternative ways of conducting cloud cost optimization

Engage your engineers in FinOps and cloud cost savings

Discover how OptScale helps companies quickly increase FinOps adoption by engaging engineers in FinOps enablement and cloud cost savings