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Average Ratings 0 Ratings

Total
ease
features
design
support

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Write a Review

Description

Customers are required to provide high-quality services while minimizing expenses and maintaining business agility. Modern applications, whether deployed on-premises or in the cloud, and regardless of being monolithic or microservices-based, present a complex landscape with thousands of parameters and numerous instance types that must be adjusted to pinpoint the ideal configuration for balancing performance, resilience, and cost-effectiveness. With Akamas, clients can articulate their specific optimization objectives and constraints, such as service level objectives (SLOs), enabling them to optimize their applications and IT infrastructures effectively. Users of Akamas can experience significant advantages, including a 60% reduction in infrastructure and cloud costs without sacrificing application performance, a 30% boost in transactions per second using the same resources, a 70% reduction in response times while minimizing peaks and fluctuations, and an impressive 80% savings in tuning time. Ultimately, Akamas' AI-driven optimization empowers organizations and online enterprises to enhance service quality, bolster resilience, and achieve substantial cost reductions, paving the way for a more efficient operational framework.

Description

Pepperdata autonomous, application-level cost optimization delivers 30-47% greater cost savings for data-intensive workloads such as Apache Spark on Amazon EMR and Amazon EKS with no application changes. Using patented algorithms, Pepperdata Capacity Optimizer autonomously optimizes CPU and memory in real time with no application code changes. Pepperdata automatically analyzes resource usage in real time, identifying where more work can be done, enabling the scheduler to add tasks to nodes with available resources and spin up new nodes only when existing nodes are fully utilized. The result: CPU and memory are autonomously and continuously optimized, without delay and without the need for recommendations to be applied, and the need for ongoing manual tuning is safely eliminated. Pepperdata pays for itself, immediately decreasing instance hours/waste, increasing Spark utilization, and freeing developers from manual tuning to focus on innovation.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

AWS Marketplace
Amazon EKS
Amazon EMR
Apache Spark
Google Cloud Managed Service for Apache Spark

Integrations

AWS Marketplace
Amazon EKS
Amazon EMR
Apache Spark
Google Cloud Managed Service for Apache Spark

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Akamas

Founded

2019

Country

Italy

Website

akamas.io

Vendor Details

Company Name

Pepperdata, Inc.

Founded

2012

Country

United States

Website

www.pepperdata.com

Product Features

Application Performance Monitoring (APM)

Baseline Manager
Diagnostic Tools
Full Transaction Diagnostics
Performance Control
Resource Management
Root-Cause Diagnosis
Server Performance
Trace Individual Transactions

Product Features

Application Performance Monitoring (APM)

Baseline Manager
Diagnostic Tools
Full Transaction Diagnostics
Performance Control
Resource Management
Root-Cause Diagnosis
Server Performance
Trace Individual Transactions

Cloud Cost Management

Cost Reduction Optimization
Dashboard
Data Import/Export
Data Storage
Data Visualization
Resource Usage Reporting
Roles / Permissions
Spend and Cost Reporting

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