Category: Application Services

Today, we announced Amazon API Gateway REST API’s support for private integration with Application Load Balancers (ALBs). You can use this new capability to securely expose your VPC-based applications through your REST APIs without exposing your ALBs to the public internet.

Modern generative AI applications often need to stream large language model (LLM) outputs to users in real-time. Instead of waiting for a complete response, streaming delivers partial results as they become available, which significantly improves the user experience for chat interfaces and long-running AI tasks. This post compares three serverless approaches to handle Amazon Bedrock LLM streaming on Amazon Web Services (AWS), which helps you choose the best fit for your application.

Today, AWS is announcing tenant isolation for AWS Lambda, enabling you to process function invocations in separate execution environments for each end-user or tenant invoking your Lambda function. This capability simplifies building secure multi-tenant SaaS applications by managing tenant-level compute environment isolation and request routing, allowing you to focus on core business logic rather than implementing tenant-aware compute environment isolation.

In this post, we will show how you can use the new portal feature to create customizable portals with enhanced security features in minutes, with APIs from multiple accounts, without managing any infrastructure.

In this post, we explore using AWS Step Function state machine with asynchronous AWS services, look at some scenarios where the processing time can be unpredictable, explain when traditional solutions such as polling (periodically check) fall short, and demonstrate how to implement a generalized callback pattern to handle asynchronous operations into a more manageable synchronous flow.

In this post, you’ll learn how to use AWS Step Functions Distributed Map to process Amazon Athena data manifest and Parquet files through a step-by-step demonstration.

In this post, we explore how to optimize processing array data embedded within complex JSON structures using AWS Step Functions Distributed Map. You’ll learn how to use ItemsPointer to reduce the complexity of your state machine definitions, create more flexible workflow designs, and streamline your data processing pipelines—all without writing additional transformation code or AWS Lambda functions.

In this post, you’ll learn how to process Amazon S3 objects at scale with the new AWS Step Functions Distributed Map S3 prefix and transformation capabilities.

You can use AWS Step Functions to orchestrate complex business problems. However, as workflows grow and evolve, you can find yourself grappling with monolithic state machines that become increasingly difficult to maintain and update. In this post, we show you strategies for decomposing large Step Functions workflows into modular, maintainable components.

In this post, you learn how to implement blue/green deployments by using Amazon API Gateway for your APIs. For this post, we use AWS Lambda functions on the backend. However, you can follow the same strategy for other backend implementations of the APIs. All the required infrastructure is deployed by using AWS Serverless Application Model (AWS SAM).