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The 4 Biggest Challenges of Scaling Cloud-Native AI Workloads

When working with #AI in cloud environments, traditional data provisioning and software testing methods don't work because of the behavior of AI and LLM APIs. In this Cloud Native Computing Foundation (CNCF) webinar recording, we discuss the top 4 challenges of scaling cloud-native AI workloads, and the solutions developers are turning to instead.

Intro to Speedscale

Speedscale is a Y-Combinator backed startup that helps Kubernetes engineering teams build resilient and performant containerized apps. Our production traffic replication platform is a more reliable, cost-effective, and scalable way to test and deliver cloud-native software applications. Unlike other tools, we use agents/sidecars to record and playback sanitized traffic that you see in prod. With Speedscale, engineers can generate load, simulate production conditions, and mock third party backends modeled after real traffic patterns.

eCommerce Load Testing (Step 3): Build the Environment

In this webinar clip from "Ensuring performance: How major retailers leverage user traffic to validate code changes", Speedscale Co-founder, Nate Lee, explains what to consider when building the environment, including backend dependencies and data. He covers how service mocking can help companies test at a higher velocity in today's complex development environments.

Replicating Traffic: Use Cases & Benefits

In this webinar clip from "Ensuring performance: How major retailers leverage user traffic to validate code changes", Speedscale Co-founder, Nate Lee, talks about why traffic-based testing (unlike manually writing script tests) can help companies move faster and test at the speed of development. He covers the top use cases and benefits of leveraging traffic as the new way to test.