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'

An open-source FinOps solution with ML/AI profiling and optimization capabilities

Improve ML/AI profiling process by getting optimal performance and minimal cloud costs for ML/AI experiments

Recognized by Forrester as a leading cloud cost management solution

OptScale ML-AI Optimization
Hystax-OptScale-ML-task-profiling-optimization

ML/AI task profiling and optimization

Dozens of tangible ML/AI performance improvement recommendations

Hystax-OptScale-runsets-ML-model-training-simulation

Runsets to simulate ML/AI  model training 

Minimal cloud cost for ML/AI experiments and development

OptScale is available on cloud marketplaces

AWS-logo
azure
Alibaba Cloud marketplace
Digital-ocean
Yandex-cloud-market

ML/AI task profiling and optimization

With OptScale ML/AI and data engineering teams get an instrument for tracking and profiling ML/AI model trainings and other relevant tasks. OptScale collects a holistic set of inside and outside performance and model-specific metrics, which help to give performance and cost optimization recommendations for ML/AI experiments or production tasks. OptScale integration with Apache Spark makes Spark ML/AI task profiling process more efficient and transparent.

Hystax OptScale ML-AI profiling and optimization
OptScale-tangible-performance-improvement-recommendations

Dozens of tangible performance improvement recommendations

By integrating with an ML/AI model training process OptScale highlights bottlenecks and provides clear recommendations to reach ML/AI performance optimization. The recommendations include utilizing Reserved/Spot instances and Saving Plans, rightsizing and instance family migration, Spark executors idle state, detecting CPU/IO, IOPS inconsistencies that can be caused by data transformations or model code inefficiencies.

Recognized by Forrester as a leading cloud cost management solution

Runsets to simulate ML/AI model training on different environments and hyperparameters

OptScale enables ML/AI engineers to run a bunch of training jobs based on pre-defined budget, different hyperparameters, hardware (leveraging Reserved/Spot instances) to reveal the best and most efficient results for your ML/AI model training.

OptScale-runsets_ML_model_training_simulation_on_different_environment_hyperparameters
OptScale-minimal-cloud-cost-for-ML-experiments-and-development

Minimal cloud cost for ML/AI experiments and development

After profiling of ML/AI model training OptScale gives dozens of real-life optimization recommendations and in-depth cost analysis, which help minimize cloud costs for ML/AI experiments and development. The tool delivers ML/AI metrics and KPI tracking, providing full transparency across ML/AI teams.

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 saving

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