Author: Ayush Kulkarni

We recently announced the launch of AWS Lambda MicroVMs, a new serverless compute primitive that provides VM-level isolation, near-instant startup performance, and state retention. You can now give each user or job their own execution environment to securely run just-in-time code – either user or AI generated – without managing virtualization infrastructure or choosing between […]

Users usually package their function code as container images when using machine learning (ML) models that are larger than 250 MB, which is the Lambda deployment package size limit for zip files. In this post, we demonstrate an approach that downloads ML models directly from Amazon S3 into your function’s memory so that you can continue packaging your function code using zip files.

AWS Lambda cold start latency can impact performance for latency-sensitive applications, with function initialization being the primary contributor to startup delays. Lambda SnapStart addresses this challenge by reducing cold start times from several seconds to sub-second performance for Java, Python, and .NET runtimes with minimal code changes. This post explains SnapStart’s underlying mechanisms and provides performance optimization recommendations for applications using this feature.