# Serverless Serverless is a cloud application development and execution model in which developers deploy code without managing the underlying server infrastructure. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Build a serverless PII redaction pipeline with Amazon Bedrock Data Automation DevFeed: [Build a serverless PII redaction pipeline with Amazon Bedrock Data Automation]() Original publisher: [Read original article]() Author: Samantha Stuart Published: 2026-09-16T15:17:37Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock](), [pii](), [Serverless](), [AWS Lambda](), [AWS Step Functions](), [Artificial Intelligence](), [Amazon Web Services](), [Ansible]() Tags: [advanced-300](), [amazon-bedrock](), [amazon-bedrock-data-automation](), [aws](), [aws-lambda](), [aws-step-functions](), [how-to](), [pii-redaction](), [pipeline](), [precision](), [serverless](), [technical-how-to]() ### AI overview This tutorial presents a serverless AWS pipeline for detecting and redacting personally identifiable information in scanned documents and images. It uses Amazon Bedrock Data Automation with a custom blueprint, AWS Step Functions, and AWS Lambda, with a token-matching quality check to improve recall on degraded and handwritten documents. ### Source excerpt Learn how to automate end-to-end PII detection and redaction from scanned documents at scale using Amazon Bedrock Data Automation with a custom blueprint, AWS Step Functions, and AWS Lambda. A custom blueprint redacts sensitive fields with field-level precision, and a token matching quality check raises recall across degraded and handwritten documents. ## How Khan Academy Scaled to 2.5x Traffic During the Coronavirus Pandemic DevFeed: [How Khan Academy Scaled to 2.5x Traffic During the Coronavirus Pandemic]() Original publisher: [Read original article]() Author: Khan Academy Published: 2020-05-09T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [Scalability](), [Architecture & Design](), [Serverless](), [Caching](), [Google Cloud Platform (GCP)](), [Load Balancing]() Tags: [architecture](), [caching](), [cdn](), [engineering](), [gcp](), [google-cloud](), [load-balancing](), [news](), [scalability](), [serverless](), [web]() ### AI overview Marta Kosarchyn explains how Khan Academy handled site usage reaching 2.5 times the previous year's level during the coronavirus pandemic. The article attributes the scalability to Google Cloud services, serverless infrastructure, Datastore, Memcache, Fastly CDN caching, and advance preparation. ### Source excerpt By Marta Kosarchyn Talk about rapid scaling... A few months ago I posted some thoughts on scaling and ... Read more ## Build a WhatsApp AI agent with Appwrite Functions and TablesDB DevFeed: [Build a WhatsApp AI agent with Appwrite Functions and TablesDB]() Original publisher: [Read original article]() Author: Atharva Deosthale Published: 2026-09-16T00:00:00Z Content type: tutorial Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [AI Agent](), [Messaging](), [Serverless](), [API](), [Database]() Tags: [ai](), [ai-agent](), [api](), [app](), [build](), [data](), [database](), [messages](), [messaging](), [meta](), [serverless](), [support](), [tutorial](), [tutorials](), [whatsapp]() ### AI overview This tutorial shows how to build a WhatsApp AI support agent with Appwrite Functions and TablesDB. One function receives and stores incoming messages, while a second function retrieves conversation history, uses an order-lookup tool, sends replies through Meta's WhatsApp Cloud API, and stores the responses. Conversation summaries help keep long threads within the model's token budget. ### Source excerpt Turn a WhatsApp number into an AI support agent. Two Appwrite Functions receive messages and reply, TablesDB keeps the conversation history, and a compaction step keeps the context small. ## Elastic announces GA of cross-project search on Serverless, enabling teams to query across all linked projects without moving a byte DevFeed: [Elastic announces GA of cross-project search on Serverless, enabling teams to query across all linked projects without moving a byte]() Original publisher: [Read original article]() Author: Jordi Mon Companys,Lucy Wang Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [Serverless](), [Elastic Cloud](), [Cloud](), [observability](), [Security](), [SIEM, Security, Observability]() Tags: [architecture-cloud-migration-migrating-cloud-native-scaling-search-analytics-search-applicatio](), [cloud](), [cloud-elasticsearch-platform](), [customers](), [elastic](), [elastic-cloud](), [observability](), [observability-security-search](), [security](), [serverless]() ### AI overview Elastic announced the general availability of cross-project search for Elastic Cloud Serverless. It lets teams query multiple linked projects in a single session across regions, project types, and cloud providers, while keeping data in place and avoiding certificates, remote-cluster configuration, and per-connection authentication. ### Source excerpt Cross-project search is now GA on Elastic Cloud Serverless. Link multiple projects and query all of them from a single Discover or ES|QL session: no certificates, no remote cluster config, and three clicks to set up. Available from September 1, 2026. ## Build an AI-powered product tagging system with Amazon SageMaker serverless model customization DevFeed: [Build an AI-powered product tagging system with Amazon SageMaker serverless model customization]() Original publisher: [Read original article]() Author: Linpo Guo Published: 2026-09-15T16:11:36Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Artificial Intelligence](), [Amazon SageMaker AI](), [Fine-tuning](), [rlvr](), [grpo](), [Inference](), [model-deployment](), [Reinforcement learning](), [SDK](), [Serverless]() Tags: [ai](), [amazon-sagemaker](), [amazon-sagemaker-ai](), [aws](), [compute](), [cost](), [customization](), [expert-400](), [fine-tuning](), [inference](), [jobs](), [reinforcement-learning](), [rlvr](), [sdk](), [technical-how-to]() ### AI overview This walkthrough shows how to build a product tagging system by customizing Qwen3-8B with supervised fine-tuning and reinforcement learning with verifiable rewards on Amazon SageMaker serverless model customization. It then deploys the optimized model for asynchronous inference to enrich retail catalogs. ### Source excerpt Manually tagging thousands of catalog products is slow and inconsistent. This walkthrough shows how to customize Qwen3-8B with supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR) on Amazon SageMaker serverless model customization, then deploy it for asynchronous inference to build a cost-efficient product tagging system. ## What Managed Postgres Services Handle for Teams DevFeed: [What Managed Postgres Services Handle for Teams]() Original publisher: [Read original article]() Author: Databricks Staff Published: 2026-09-14T23:36:59Z Content type: article Language: en Sources: [Databricks]() Topics: [PostgreSQL](), [Database](), [Serverless](), [databricks](), [AI Development]() Tags: [data-plus-ai-foundations](), [database](), [databricks](), [developer-tooling](), [migration](), [postgres](), [postgresql](), [recovery](), [security](), [serverless]() ### AI overview The article defines managed Postgres by the operational responsibilities a provider assumes, including patching, scaling, failover, and backups. It discusses how these responsibilities can vary between providers and describes Lakebase Postgres as a serverless offering with automatic scaling, PostgreSQL compatibility, recovery, and Databricks integrations. ### Source excerpt Every Postgres vendor calls itself "managed." Few of them agree on what that word... ## Abnormal AI: Amazon Bedrock AgentCore for agentic email security at scale DevFeed: [Abnormal AI: Amazon Bedrock AgentCore for agentic email security at scale]() Original publisher: [Read original article]() Author: Aswin Vasudevan Published: 2026-09-14T21:22:45Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock AgentCore](), [threat detection](), [Amazon Bedrock](), [Security](), [Serverless](), [API](), [Amazon Web Services](), [AWS CloudTrail](), [Amazon CloudWatch](), [Amazon S3]() Tags: [advanced-300](), [agentic](), [agents](), [amazon-bedrock-agentcore](), [amazon-cloudwatch](), [amazon-s3](), [analysis](), [api](), [aws](), [aws-cloudtrail](), [code](), [customer-solutions](), [data](), [data-processing](), [security](), [serverless](), [threat-detection]() ### AI overview Abnormal AI uses Amazon Bedrock AgentCore Code Interpreter as an ephemeral, serverless compute scratch pad for real-time inline email threat detection. The article describes its sandbox isolation, networking and file-handling options, preloaded Python capabilities, observability integrations, and use at billion-message scale. ### Source excerpt Learn how Abnormal AI deployed Amazon Bedrock AgentCore Code Interpreter as an ephemeral compute scratch pad for the agents behind its real-time email threat detection at billion-message scale, plus the sandbox design decisions and practical lessons for builders deploying Code Interpreter in production. ## Model routing with Google Cloud API Gateway DevFeed: [Model routing with Google Cloud API Gateway]() Original publisher: [Read original article]() Author: Mak Ahmad; Sanjay Pujare Published: 2026-09-12T11:04:33.891311Z Content type: article Language: en Sources: [Google Developers Blog]() Topics: [Amazon API Gateway](), [Model Routing](), [Google](), [Cloud](), [OpenAPI Specification](), [Serverless](), [Claude](), [OpenAI]() Tags: [ai-gateway](), [api-gateway](), [claude](), [cloud](), [google](), [model-routing](), [models](), [openai](), [openapi](), [serverless]() ### AI overview Google Cloud API Gateway adds model routing in Public Preview, providing a serverless, OpenAI-compatible endpoint that dynamically routes requests to Gemini, Claude, or OpenAI OSS-GPT. Developers can configure routing in OpenAPI specifications, centralize model changes, and separate application authentication from backend model credentials. ### Source excerpt Google Cloud API Gateway now offers a model routing feature in Public Preview, allowing developers to dynamically route traffic to models like Gemini, Claude, or OpenAI OSS-GPT without hardcoding endpoints or managing open-source proxies. Developers can easily configure these routing rules directly within their OpenAPI 3.x specifications by mapping virtual model names to specific backend targets on a shared host. Once deployed, the Gateway acts as a serverless ingress layer that accepts standard OpenAI-compatible requests, automatically transcodes the payload to the native schema of the target model, and routes the traffic on the fly. ## The Architecture for Serving 100 Fine-Tuned Models on One GPU DevFeed: [The Architecture for Serving 100 Fine-Tuned Models on One GPU]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-11T21:25:15Z Content type: tutorial Language: en Sources: [Daily Dose of Data Science]() Topics: [GPU](), [vllm](), [Fine-tuning](), [lora](), [Serverless](), [Deployment](), [Code]() Tags: [code](), [deployment](), [fine-tuning](), [gpu](), [lora](), [memory](), [models](), [production](), [serverless](), [vllm](), [workers]() ### AI overview This tutorial compares architectures for serving 100 fine-tuned 7B model variants on GPUs. It explains how separate merged models increase storage, GPU memory use, scaling pools, cold starts, and idle capacity, while a shared base model with LoRA adapters enables adapter reuse through vLLM. The article plans to test merged, unmerged startup-loaded, request-time adapter loading, and hosted-per-tenant deployments on Runpod Serverless. ### Source excerpt ...explained with code. ## Deploying Parallel Remote Coding Agents with Decode DevFeed: [Deploying Parallel Remote Coding Agents with Decode]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-09-10T05:02:03Z Content type: tutorial Language: en Sources: [Decoding ML]() Topics: [AI-assisted coding](), [Command-line interface](), [Serverless](), [Python]() Tags: [cli](), [coding-agents](), [modal](), [parallel]() ### AI overview This tutorial explains how to deploy the Decode coding-agent harness as remote background jobs. It covers moving from a TUI to a headless CLI, running jobs on Modal, and supporting parallel sessions across multiple projects. ### Source excerpt Run the harness remotely on Modal, triggered by your CLI, a webhook, or a nightly cron. ## From dedicated Workers to Serverless Workers: Migrating Temporal Workflows to AWS Lambda DevFeed: [From dedicated Workers to Serverless Workers: Migrating Temporal Workflows to AWS Lambda]() Original publisher: [Read original article]() Author: Garima Gupta Published: 2026-09-09T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [AWS Lambda](), [Serverless](), [Amazon Web Services](), [Deployment](), [Testing](), [SDKs]() Tags: [aws](), [aws-lambda](), [deployment](), [go](), [java](), [kubernetes](), [net](), [python](), [serverless](), [temporal](), [temporal-voices](), [testing](), [typescript](), [versioning]() ### AI overview This guide explains how to migrate Temporal Workers from long-lived processes on Kubernetes to Temporal Cloud Serverless Workers running on AWS Lambda. It focuses on AWS Lambda with Python examples and covers lifecycle changes, invocation scaling, deployment, versioning, and testing. ### Source excerpt Learn to migrate Temporal Workers from dedicated infrastructure to AWS Lambda with Serverless Workers, with guidance on deployment, versioning, and testing. ## Turn your app into an MCP server with the Appwrite OAuth2 server DevFeed: [Turn your app into an MCP server with the Appwrite OAuth2 server]() Original publisher: [Read original article]() Author: Atharva Deosthale Published: 2026-09-04T00:00:00Z Content type: tutorial Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Model Context Protocol](), [OAuth 2.0](), [Authorization](), [Claude Code](), [Serverless](), [web applications](), [Databases]() Tags: [ai](), [ai-tools](), [app](), [auth](), [authorization](), [browser](), [claude](), [claude-code](), [code](), [databases](), [http](), [mcp](), [mcp-server](), [oauth2](), [remote-mcp-server](), [serverless](), [tutorial](), [tutorials](), [web](), [web-app]() ### AI overview This tutorial explains how to turn an Appwrite-based application into a remote MCP server. It uses an Appwrite Function, the OAuth2 server included in an Appwrite project, and a consent screen so AI tools such as Claude Code can access and act on behalf of signed-in users. ### Source excerpt Build a remote MCP server for your product, host it on Appwrite Functions, and let AI tools like Claude Code sign in through the OAuth2 server built into your Appwrite project. ## Build a memory MCP server on Appwrite DevFeed: [Build a memory MCP server on Appwrite]() Original publisher: [Read original article]() Author: Atharva Deosthale Published: 2026-09-04T00:00:00Z Content type: tutorial Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Model Context Protocol](), [MCP Server](), [OAuth 2.0](), [Serverless](), [Databases](), [Embeddings](), [Claude Code](), [web applications]() Tags: [ai](), [ai-assistants](), [ai-tools](), [api](), [claude](), [claude-code](), [embeddings](), [mcp](), [mcp-server](), [memory](), [net](), [oauth2](), [sdk](), [serverless](), [tutorial](), [tutorials]() ### AI overview This tutorial builds Recall, a small persistent-memory app on Appwrite. It hosts a stateless remote MCP server in an Appwrite Function, stores embedded memories in VectorsDB for similarity search, and uses OAuth2 for user-authorized access. It also explains connecting Claude Code to the server. ### Source excerpt Give your AI tools a shared, persistent memory. Host a stateless MCP server on Appwrite Functions, store memories in VectorsDB, and protect it with your project's OAuth2 server. ## Temporal expands its Google Cloud Partnership with Gemini integration and pay-as-you-go pricing on Google Cloud Marketplace DevFeed: [Temporal expands its Google Cloud Partnership with Gemini integration and pay-as-you-go pricing on Google Cloud Marketplace]() Original publisher: [Read original article]() Author: Jay Sivachelvan Published: 2026-08-31T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [Google Cloud Platform (GCP)](), [SDKs](), [Google](), [reliability](), [Artificial Intelligence](), [Cloud Run](), [Resilience](), [API](), [Serverless]() Tags: [agent](), [agent-development-kit](), [agent-framework](), [announcements](), [api](), [billing](), [cloud-marketplace](), [cloud-run](), [execution](), [gemini](), [google](), [google-cloud](), [integration](), [launch](), [partnership](), [pricing]() ### AI overview Temporal announces expanded Google Cloud integrations, including Public Preview support for the Google Gen AI Python SDK, durable execution for Gemini-based workflows, and pre-release Serverless Workers for Google Cloud Run. Temporal Cloud is also available on Google Cloud Marketplace with pay-as-you-go pricing. ### Source excerpt Temporal expands its Google Cloud partnership with Gemini integration, durable execution, and pay-as-you-go pricing on Google Cloud Marketplace. ## How to Confirm Phone Number Ownership with Lookup Identity Match DevFeed: [How to Confirm Phone Number Ownership with Lookup Identity Match]() Original publisher: [Read original article]() Author: Kelley Robinson Published: 2026-08-31T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [API](), [Code](), [Command-line interface](), [Serverless](), [JavaScript]() Tags: [api](), [cli](), [code](), [developer-insights](), [fake-accounts](), [fraud](), [how-to](), [javascript](), [reduce](), [request](), [serverless](), [verify]() ### AI overview This tutorial explains how to use Twilio Lookup Identity Match to confirm that a person is linked to a phone number. It covers matching submitted identity details against authoritative data, setting up a JavaScript project with the Twilio CLI and Serverless plugin, and using the result to reduce sign-up fraud. ### Source excerpt Send details like name, address, or date of birth and the Identity Match API will tell you if they match a provided phone number. Reduce fraud and verify identities quickly. ## Monitor Azure Functions across every hosting plan with Datadog DevFeed: [Monitor Azure Functions across every hosting plan with Datadog]() Original publisher: [Read original article]() Author: Duncan Harvey; Kathie Huang; Piyali Banerjee Published: 2026-08-24T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Azure](), [serverless monitoring](), [Serverless](), [telemetry](), [tracing](), [Traces](), [Application Performance Management (APM)](), [cpu](), [Node.js](), [Python](), [.NET]() Tags: [apm](), [azure](), [azure-functions](), [cpu](), [digital-experience-monitoring](), [metrics](), [monitor](), [monitoring](), [net](), [node-js](), [observability](), [performance](), [python](), [serverless](), [serverless-monitoring](), [telemetry](), [traces](), [tracing]() ### AI overview Datadog Serverless Monitoring expands observability for Azure Functions across Linux and Windows hosting plans, with telemetry collection, distributed tracing, enhanced CPU metrics, and continuous profiling. ### Source excerpt Trace requests, get enhanced CPU metrics, and profile code across Azure Functions hosting plans with Datadog Serverless Monitoring. ## Set up your AI coding agent to build with AWS Step Functions DevFeed: [Set up your AI coding agent to build with AWS Step Functions]() Original publisher: [Read original article]() Author: D Surya Sai Published: 2026-08-19T11:41:31Z Content type: release Language: en Sources: [AWS Compute Blog]() Topics: [AWS Step Functions](), [ai-coding](), [Model Context Protocol](), [Amazon Web Services](), [Serverless]() Tags: [ai-coding](), [announcements](), [aws](), [aws-step-functions](), [intermediate-200](), [mcp](), [serverless]() ### AI overview AWS Step Functions added a console button that provides a prompt for configuring compatible AI coding agents with Step Functions skills and an MCP server. The setup enables natural-language workflow development and AWS state-machine operations. ### Source excerpt AWS Step Functions has added a Copy agent prompt button to the console that configures your AI coding agent with Step Functions skills and an MCP server in one step. Paste the prompt into Claude Code, Kiro CLI, Cursor, or any MCP-compatible agent and start building workflows with natural language. ## Query Neon backend logs DevFeed: [Query Neon backend logs]() Original publisher: [Read original article]() Author: Andre Landgraf Published: 2026-08-18T12:00:00Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Back end](), [observability](), [log management](), [Command-line interface](), [MCP Server](), [Serverless](), [Server](), [Node.js](), [Amazon S3](), [SDKs]() Tags: [api](), [backend](), [cli](), [logs](), [mcp-server](), [node](), [observability](), [product](), [s3](), [tool]() ### AI overview Neon adds branch-scoped access to backend logs from Neon Functions and Object Storage outside the Console. The logs can be queried through the CLI, MCP server, Logs API, SDK, or Loki endpoints, with filters and raw LogQL support. ### Source excerpt We keep expanding our backend observability - the most recent addition: you can now query backend logs outside the Console! neon logs reads what Neon Functions and Object Storage emit on a branch, with filters for source, severity, and message text. ## Implementing dynamic feature flags with AWS AppConfig on AWS Lambda DevFeed: [Implementing dynamic feature flags with AWS AppConfig on AWS Lambda]() Original publisher: [Read original article]() Author: Daniel Abib Published: 2026-08-14T16:25:51Z Content type: tutorial Language: en Sources: [AWS Compute Blog]() Topics: [AWS Lambda](), [Serverless](), [experiments](), [configuration](), [Latency]() Tags: [a-b-testing](), [advanced-300](), [aws](), [aws-lambda](), [feature](), [feature-flags](), [how-to](), [latency](), [real-time](), [serverless](), [technical-how-to]() ### AI overview A tutorial on implementing dynamic feature flags with AWS AppConfig on AWS Lambda. It explains how feature flags support safe deployments, experiments, gradual rollouts, and changes without redeploying functions, using the AWS AppConfig Lambda extension to cache configuration locally and reduce latency. ### Source excerpt Feature toggles allow you to change application behavior in real time without deploying new code. Learn how to implement dynamic feature flags with AWS AppConfig on AWS Lambda for safe deployments, gradual rollouts, and instant rollback. ## Serverless vehicle tracking at scale: Bosch L.OS on AWS DevFeed: [Serverless vehicle tracking at scale: Bosch L.OS on AWS]() Original publisher: [Read original article]() Author: Yogish Kutkunje Pai Published: 2026-08-14T10:02:13Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [Amazon Web Services](), [Serverless](), [real-time](), [Amazon Elastic Container Service](), [AWS Lambda](), [cloud-infrastructure](), [data](), [Networks](), [Amazon EC2]() Tags: [amazon-elastic-container-service](), [architecture](), [aws](), [complexity](), [compliance](), [cost](), [customer-solutions](), [data](), [india](), [integration](), [intermediate-200](), [networks](), [operations](), [performance](), [platform](), [real-time](), [scale](), [serverless](), [supply-chain](), [workflows]() ### AI overview Bosch Mobility Platform Solutions built L.OS, a serverless platform on AWS that unifies fragmented vehicle-tracking data across India's spot logistics market. The article describes how the platform standardizes data from multiple providers and supports scalable, real-time visibility through workflows for discovery, tracking, and termination. ### Source excerpt Learn how Bosch Mobility Platform Solutions built L.OS, a serverless vehicle tracking platform on AWS that unifies India's fragmented spot logistics market into a single real-time visibility layer using Amazon ECS, AWS Lambda, and Amazon MSK. ## Neon Functions: backend logic next to your data DevFeed: [Neon Functions: backend logic next to your data]() Original publisher: [Read original article]() Author: Carlota Soto Published: 2026-08-12T12:00:00Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Back end](), [Node.js](), [Database](), [Serverless](), [WebSocket](), [Cloud]() Tags: [backend](), [database](), [hosting](), [network](), [node](), [node-js](), [product](), [serverless](), [vpc]() ### AI overview Neon Functions provide long-running Node.js 24 backend compute deployed beside a Neon Postgres branch. They automatically receive database and related Neon service credentials, reducing network hops and supporting streaming, WebSockets, SSE, and persistent connection pools. ### Source excerpt Neon Functions are Node.js 24 compute you deploy onto a Neon branch, in the same region as your Lakebase Postgres database, with DATABASE_URL injected automatically. They're long-running enough that agents can stream for minutes and WebSockets or SSE can stay open while data flows. ## AWS Weekly Roundup: AWS Heroes Summit, Web Search on Amazon Bedrock, Dogwood, Kiro Crew, and more (August 10, 2026) DevFeed: [AWS Weekly Roundup: AWS Heroes Summit, Web Search on Amazon Bedrock, Dogwood, Kiro Crew, and more (August 10, 2026)]() Original publisher: [Read original article]() Author: Channy Yun (윤석찬) Published: 2026-08-10T15:45:03Z Content type: article Language: en Sources: [AWS News Blog]() Topics: [Amazon Bedrock](), [Amazon Web Services](), [Artificial Intelligence](), [Amazon Bedrock AgentCore](), [Amazon DynamoDB](), [AWS Transform](), [Embeddings](), [Containers](), [Serverless](), [Mainframe](), [OpenAI]() Tags: [ai](), [amazon](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [amazon-dynamodb](), [applications](), [aws](), [aws-lambda](), [aws-transform](), [bedrock](), [containers](), [embeddings](), [kiro](), [modernization](), [news](), [open-source](), [product](), [serverless](), [week-in-review]() ### AI overview This AWS Weekly Roundup covers the AWS Heroes Summit and selected launches, including web search for OpenAI models in Amazon Bedrock, dedicated runtime instances for agents in Amazon Bedrock AgentCore, vector search in Amazon DynamoDB, and general availability of AWS Transform continuous modernization. ### Source excerpt Last week, we brought together AWS Heroes from around the world to connect, collaborate, and celebrate the builders who go above and beyond for the AWS community. The AWS Heroes Summit, an invite-only annual gathering, brings global experts specializing in fields like AI, serverless, and containers together for direct collaboration, technical deep-dives, and feedback sessions [...] ## Instrument serverless apps with agentic onboarding DevFeed: [Instrument serverless apps with agentic onboarding]() Original publisher: [Read original article]() Author: Rohan Agarwal; Piyali Banerjee Published: 2026-08-10T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Serverless](), [AWS Lambda](), [azure container apps](), [Cloud Run](), [Command-line interface](), [MCP Server](), [Artificial Intelligence](), [Claude Code](), [cursor](), [Node.js](), [Terraform]() Tags: [agentic](), [ai](), [aws](), [aws-lambda](), [azure](), [azure-container-apps](), [claude-code](), [cli](), [cursor](), [google-cloud](), [google-cloud-run](), [mcp-server](), [node](), [serverless](), [serverless-monitoring](), [terraform]() ### AI overview Datadog's agentic onboarding helps instrument serverless applications across AWS Lambda, Google Cloud Run, and Azure Container Apps. Developers can use an AI assistant connected through the Datadog MCP Server or run the AI Setup CLI to inspect projects, adapt setup to existing deployment tools and runtimes, and prepare configuration changes for review. ### Source excerpt Use Datadog agentic onboarding to instrument AWS Lambda, Google Cloud Run, and Azure Container Apps from an AI assistant or CLI. ## How Factory scaled its cloud backend to one billion monthly requests on Vercel DevFeed: [How Factory scaled its cloud backend to one billion monthly requests on Vercel]() Original publisher: [Read original article]() Author: Ben Sabic Published: 2026-08-03T04:00:00Z Content type: article Language: en Sources: [Vercel News]() Topics: [Next.js](), [Vercel](), [Back end](), [Software as a service](), [API](), [Authentication](), [Routing (disambiguation)](), [Serverless]() Tags: [api](), [authentication](), [backend](), [cloud](), [latency](), [next-js](), [routing](), [serverless](), [software-development](), [tooling](), [vercel](), [web-app]() ### AI overview The article describes how Factory runs its cloud backend on a single Next.js application deployed on Vercel. API routes, middleware, webhook handlers, and log-drain pipelines support a multi-surface SaaS platform handling one billion daily requests, with reported p95 response times of 350 milliseconds or below. Factory scaled memory limits and function duration as needed, while Vercel's fluid compute kept functions warm and avoided cold-start latency. ### Source excerpt Factory on Vercel One billion backend API requests served daily 350ms p95 response time Scaled backend, internal tooling, and security without a dedicated infrastructure team Factory's mission is to bring autonomy to every phase of the software development lifecycle, from signals to production. Not to replace engineering judgment, but to automate the repetitive work around it, giving engineers more time to focus on the decisions that matter most. To deliver this enterprise-grade platform, Factory adopted their ideal operation model internally: remaining lean and agile by deploying Droids to manage routine workloads, so their engineers could focus strictly on building the product. As Factory scaled from a simple web app into a multi-surface platform, serving everyone from individual developers to enterprises with tens of thousands of engineers, their backend scaled alongside them. Today, a single Next.js backend on Vercel powers every surface, handling one billion daily requests across API routes, middleware, and webhook handlers, without becoming a separate engineering project. Next.js as a full-stack backend for cloud SaaS: API routes, middleware, and backend logic at scale Most teams think of Next.js as a frontend framework. Factory runs its entire cloud backend on it: API routes handle the customer-facing API, which launched in its own section of their Next.js app Middleware manages authentication and routing logic across every surface Webhook handlers and log drain pipelines run alongside the web platform, feeding analytics downstream As Factory grew rapidly, the same backend absorbed every new workload. None of it required standing up separate infrastructure or making a new vendor decision. The only areas that required tuning were memory limits and function duration. Both scaled up without incident. Fluid compute keeps functions warm across requests, removing the cold start penalty that makes traditional serverless a liability for latency-sensitive workloads. T [Next page]()