AWS Compute Optimizer

AWS Compute Optimizer
Name	AWS Compute Optimizer
Developer	Amazon Web Services
Released	2019
Platform	Amazon EC2, AWS Lambda, AWS EBS, AWS Auto Scaling
License	Proprietary

AWS Compute Optimizer AWS Compute Optimizer is a cloud service from Amazon Web Services that provides automated resource right-sizing recommendations for compute resources. It analyzes historical utilization data, machine learning models, and configuration metadata to suggest instance types, sizes, and storage options aimed at cost reduction and performance optimization. Organizations using services from Amazon Web Services can combine Compute Optimizer with other infrastructure tools to align capacity with workload demands, often alongside operations teams at enterprises, startups, and research institutions.

Overview

Compute Optimizer leverages telemetry and performance signals collected from Amazon Elastic Compute Cloud, Amazon Elastic Block Store, and AWS Lambda to offer actionable guidance. The service uses machine learning techniques similar to those described in industrial research and practiced at technology companies such as Amazon (company), Google, Microsoft, Facebook, and Netflix to model workload behavior. It interacts with identity frameworks like AWS Identity and Access Management and inventory services comparable to AWS Config to map resources across accounts and regions. In practice, infrastructure teams coordinating with procurement groups or finance departments often use Compute Optimizer alongside cost-management offerings from firms such as Gartner, Forrester, and consultancies like Accenture and Deloitte.

Features and Recommendations

Compute Optimizer delivers a set of recommendation types, including instance type rightsizing, storage optimization, and Lambda memory sizing, each accompanied by projected savings and performance risk assessments. Machine learning models are trained on time-series utilization patterns and topology information similar to approaches used in OpenAI research and academic work from institutions like Massachusetts Institute of Technology, Stanford University, and Carnegie Mellon University. Recommendations enumerate trade-offs between cost and latency in the manner of capacity-planning analyses employed at Intel and NVIDIA. For multi-account organizations using frameworks such as AWS Organizations or governance practices championed by The Open Group, Compute Optimizer outputs can be integrated into governance workflows managed by teams that also interact with standards bodies like ISO and NIST.

Supported Resources and Metrics

The service supports several resource types and metrics, typically sourced from monitoring systems analogous to Amazon CloudWatch. Supported resources include Amazon EC2 instances, Amazon EBS volumes, Auto Scaling groups, and AWS Lambda functions. Metrics include CPU utilization, memory usage (when provided), disk I/O, and network throughput, comparable to observability signals employed by teams at LinkedIn, Twitter, and Dropbox. For block storage, recommendations consider throughput and IOPS characteristics similar to those specified by vendors like Seagate, Western Digital, and cloud infrastructure whitepapers from Institute of Electrical and Electronics Engineers. When used in regulated sectors—healthcare providers like Mayo Clinic or financial institutions like Goldman Sachs—organizations often combine those metrics with audit trails from systems similar to Splunk or Elastic (company).

Integration and APIs

Compute Optimizer exposes APIs that allow programmatic access to recommendation data, enabling integration with configuration management tools and orchestration systems used at organizations such as Red Hat, HashiCorp, Chef Software, and Puppet (software). The APIs follow RESTful patterns and can be invoked from continuous delivery pipelines orchestrated with systems like Jenkins, GitLab, or CircleCI. Outputs are commonly consumed by cloud management platforms produced by vendors like VMware, ServiceNow, and BMC Software to drive automated remediation or ticketing workflows. Identity and access for API calls are managed through AWS Identity and Access Management roles and permissions, often linked with enterprise single sign-on providers such as Okta and Azure Active Directory.

Pricing and Availability

Compute Optimizer is available across multiple AWS Regions and is offered under a pricing model that often includes a free tier for basic recommendations and charges for advanced features or extended retention of historical data, paralleling pricing strategies used by Amazon Web Services for services like Amazon CloudWatch and AWS Config. Availability can vary by region in a pattern similar to the staged rollouts of services like Amazon Aurora and AWS Lambda. Enterprises evaluate cost implications alongside cloud spend analytics products from Cloudability, CloudHealth by VMware, and Apptio when planning adoption. Regional availability considerations mean multinational corporations operating in jurisdictions overseen by authorities such as the European Commission or Federal Communications Commission coordinate deployment timing with legal and compliance teams.

Security and Compliance

Compute Optimizer integrates with AWS security controls and logging systems to maintain auditability and access governance. It relies on permissioned API access via AWS Identity and Access Management and can be used alongside logging services like Amazon CloudWatch Logs to capture activity for compliance programs referencing standards from NIST, ISO/IEC 27001, and regulatory frameworks including HIPAA for healthcare or PCI DSS for payment card data. Organizations subject to national data protection laws such as the General Data Protection Regulation in the European Union or sectoral regulators like the U.S. Securities and Exchange Commission incorporate Compute Optimizer outputs into their control evidence for internal audit and external assessments by firms like KPMG and PwC.

Category:Amazon Web Services