# Compute Optimizer expands EC2 and RDS instance coverage > Compute Optimizer adds newer EC2 and RDS options to recommendations, giving teams more candidates to validate against workload and migration constraints. Canonical URL: https://www.devobs.io/news/news-aws-compute-optimizer-instance-coverage/ By: Lena Fischer Published: 2026-09-06T11:58:54.634Z Updated: 2026-09-06T11:58:54.634Z Event date: 2026-04-23 Section: Cloud AWS expanded Compute Optimizer on April 23 to cover 162 additional EC2 instance types and 32 RDS database instance classes. The release broadens the set of infrastructure options the service can consider when making recommendations. ## A larger candidate set The [announcement](https://aws.amazon.com/about-aws/whats-new/2026/04/aws-compute-optimizer-ec2-rds/) includes newer compute, general-purpose, memory, and storage-oriented EC2 families. It also expands recommendations for several RDS and Aurora engine configurations. The launch excluded GovCloud and the two China Regions. For infrastructure teams, the change is an opportunity to revisit recommendations that were previously limited by instance coverage. It does not mean every newly included family is a suitable destination for every resource. Our analysis: rightsizing and migration compatibility are related but separate reviews. A recommendation based on resource demand still has to pass the team's checks for architecture, engine support, operational tooling, and the consequences of changing capacity. ## Review the observation window Before acting, ask whether the workload data includes the events that matter: month-end processing, a seasonal peak, a cache rebuild, or a recovery exercise. A quiet interval can describe normal usage accurately while missing the capacity requirement that drives the service's design. For a database, assess connection behavior, maintenance planning, and restoration procedures alongside compute demand. For an application fleet, check launch templates and deployment constraints before selecting a new family. Record the reason for accepting or rejecting each candidate. That turns recommendations into an auditable engineering decision instead of a series of console clicks. Pilot one low-risk change, measure completed work and user-visible latency, and update the comparison with observed results. Broader coverage is most useful when it feeds a repeatable evaluation process. ## Source references -