SEE PRICING & PACKAGES

Wednesday, September 23, 2026 - 1:30pm to 2:30pm

From Local to Cloud: Scaling Your Load Tests with AWS (Without Blowing the Budget)

Many teams begin load testing on a local machine or inside their own network, but quickly hit limits with CPU, bandwidth, realism, and scale. This session addresses the challenge of moving from local load testing to cloud-based execution in a practical, cost-conscious way using AWS. The session will walk through how to spin up EC2 instances as load generators, create and manage SSH keys, transfer and run tests remotely, and collect results without needing deep cloud expertise. You’ll learn how to use Spot Fleets to reduce costs, structure your test setup for repeatability, and safely generate traffic from outside your network to better simulate real users. As your tests scale, Nestor will examine the common limitations teams encounter, such as networking bottlenecks, IP constraints, and infrastructure ceilings, and how to recognize them before they derail your effort. By the end of this session, attendees will understand how to move their load testing into the cloud, scale it responsibly, avoid common mistakes, and save both time and money while increasing the practicality and value of their performance testing.

Paciolan

Nestor Valenzuela is a Sr. Quality Engineering Manager at Paciolan with 17+ years of experience in software quality and performance engineering. He specializes in scalable test automation, load testing, and AI-driven quality practices. Nestor focuses on building collaborative, high performing teams and serves as a Gatling Ambassador, contributing to the performance testing community through mentorship and knowledge sharing.

Heather Thacker
Gatling

Heather Thacker is a Developer Advocate at Gatling focused on making performance testing approachable and essential. With four years as a software developer and two years in DevOps advocacy, she’s an international speaker who bridges theory with practice. She helps engineers build confidence to prioritize performance in their workflows.