AWS NextGen Cost Optimization & Well-Architected Review

Apply well-architected best practices to drive cost efficiencies in AWS environments

Realize Significant Savings by Moving Beyond Traditional Cost Optimization

Companies waste about one-third of cloud spend. Wasted cloud spend caused by idle resources and over-provisioning exceeded $14B in 2019, and it is projected to grow up to $18B in 2020. Organizations need to act fast to optimize costs and improve efficiency of their cloud environments.

Drivers of Ineffective Cloud Spend

1

Legacy applications moved to the cloud as-is, without being
re-architected for performance gains and cost efficiency

2

Uncontrolled use of cloud resources due to lack of tracking and monitoring systems

3

Mismanaged workloads and poorly optimized environments that leak cloud resources

General Principles and Holistic Optimizations

Cost-Effective
Resources

Use the right services, resources, and configurations for your workloads through appropriate provisioning, right-sizing, and data transfer optimizations

Matching Supply
with Demand

Eliminate the need for costly and wasteful over-provisioning for IT services while accounting for resource failures, high availability, and provision time

Usage and Expenditure
Awareness

Attribute resource costs to the systems, individual business, or product owners to make more informed decisions about where to allocate resources within your business

Workload-Specific Optimizations

Data Processing and Storage

Start from RDS-level cost optimizations and move to optimize your Big Data processing pipelines. Make your Data Warehouse consume less cloud resources on reporting and data analysis while cutting on spend caused by Databricks, Snowflake, or other 3rd party systems.

Serverless

Though considered by many to be the next generation of cloud infrastructure, serverless is neither faster nor cheaper. In fact, inappropriate usage of serverless can cause huge waste and inefficiencies. Cost-aware changes to architecture could reduce serverless costs by up to 50%.

Machine Learning

ML infrastructure can cause waste and lead to bloated cloud spend. Modernize training and inferencing pipelines, move to Elastic Inference, use Spot instances for training, and introduce early stopping techniques to cut your machine learning infrastructure costs by up to 60%.

Microservices

The costs of supporting the microservices infrastructure of production, development, and test environments can grow at scale. Take advantage of the best practices of deployment, autoscaling, and downscaling for your infrastructure to reduce TCO by no less than 50%.

Well-Architected Review for AWS Cost Optimization

A workshop engagement led by Provectus is designed to help you compare your workload against best practices and obtain step-by-step guidance for achieving cost savings. We start holistically with the infrastructure cost optimizations and go deep into the most cost-inefficient workloads of your architecture.

Engagement Workflow

  • 1. Сhoose workloads, environments, or solutions for review

  • 2. Participate in a well-architected review workshop

Fully funded engagement
4-8 hour customer commitment

  • 3. Evaluate a list of action items for implementation

  • 4. Apply the best practices and recommendations

Take advantage of a cost-optimized system
achieving 10-50% AWS cost reduction

As a result of the engagement, you receive a structured report with recommendations on how to produce stable and cost-efficient systems.
You may then opt for Provectus to proceed with the implementation.