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Why modern apps need autonomous management
Cut cloud cost and tune performance
Improve availability and minimize Failed Customer Interactions (FCIs)
Know how new releases perform in production
Optimize cloud costs with integrated engineering & financial optimization
Autonomously scale, continuously optimize resources and cut costs
Autonomously optimize Elastic Kubernetes Service
Autonomous cost and resource optimization
Continuous performance optimization and autonomous concurrency for serverless
Optimize EC2 costs with rightsizing
Optimize Intelligent Tiering and Storage Classes
Optimize storage class selection and unused volumes
Autonomously optimize Azure Kubernetes Service
Rightsizing & optimization
Autonomously optimize Google Kubernetes Engine
Autonomously optimize data processing on GCP
Set and optimize performance
Work with your existing tools
Optimize Databricks cost and performance
Find 30-50% cost savings by optimizing apps & infrastructure
Improve customer experience with 30-75% performance gains
Reduce Failed Customer Interactions (FCIs)
Cut manual toil
Understand the performance of every release
Drive cost, performance and availability with AI
Integrate AI into your tech stack for cost, performance and availability
Deliver reliability with the power of AI
Provide a powerful tool for engineers to optimize their applications
Optimize performance automatically
Drive site conversion & optimize margins
Meet the threat with high reliability & optimized costs
Improve traveler experience & optimize cloud costs
Improve online experience & optimize cloud costs
A quick and comprehensive guide to understanding essential cloud optimization terminology and concepts, clearly articulated.
The application of AI, particularly machine learning and data science, to automate and enhance IT operations processes.
The use of AI and machine learning to automate and optimize cloud operations with minimal human intervention.
The use of AI to automatically fine-tune cloud resources and configurations for optimal performance and cost-efficiency without human intervention.
The capability of a system to automatically detect, diagnose, and fix issues in cloud environments without human involvement.
The automatic adjustment of computational resources based on current demand.
A method to frequently deliver apps to customers by introducing automation into the stages of app development.
A framework for ensuring that an organization's cloud computing strategies align with its business objectives and comply with all regulations.
The process of moving digital assets, services, databases, IT resources, or applications to cloud infrastructure.
The process of selecting and allocating the most efficient cloud resources to maximize performance and minimize costs.
Measures, controls, and policies deployed to protect data, applications, and infrastructure associated with cloud computing.
Unused or underutilized cloud resources that result in unnecessary costs.
Adherence to regulatory standards and industry guidelines while operating in cloud environments.
The practice of packaging software code and all its dependencies together to ensure consistent operation across different computing environments.
Strategies and practices to reduce overall cloud spending while maintaining or improving performance and efficiency.
A set of practices that combines software development (Dev) and IT operations (Ops) to shorten the development lifecycle and provide continuous delivery.
The ability to quickly expand or decrease computer processing, memory, and storage resources to meet changing demands.
Horizontal scaling (scaling out) adds more machines to a network, while vertical scaling (scaling up) adds more power to an existing machine.
A computing environment that combines on-premises infrastructure with cloud-based services.
The practice of managing and provisioning infrastructure through machine-readable definition files rather than manual processes.
The distribution of workloads across multiple computing resources to optimize resource use, minimize response time, and avoid overload.
The application of ML algorithms to automate and enhance various aspects of cloud operations and decision-making.
An architectural style that structures an application as a collection of loosely coupled, independently deployable services.
The collection, analysis, and use of metrics, logs, and traces to understand and optimize system behavior in the cloud.
The use of multiple cloud computing and storage services in a single architecture to optimize performance, costs, and reliability.
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