# faas Published articles for faas. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## AtomicFaaS: Offloading Side-Effect Compensation to the Serverless Platform DevFeed: [AtomicFaaS: Offloading Side-Effect Compensation to the Serverless Platform]() Original publisher: [Read original article]() Author: Jonas Heisterberg, Sebastian Koch, Trever Schirmer, David Bermbach Published: 2026-11-05T05:00:00Z Content type: article Language: en Sources: [arXiv: Distributed Computing]() Topics: [function as a service](), [Serverless]() Tags: [code](), [crashes](), [cs-dc](), [developer](), [developers](), [distributed](), [effect](), [effects](), [extension](), [faas](), [failure](), [function](), [function-as-a-service](), [function-as-a-service-faas](), [latency](), [load](), [offloading](), [platform](), [retries](), [serverless](), [throughput]() ### AI overview AtomicFaaS moves compensation for failed serverless invocations from developers to the platform. It intercepts outbound calls and records their effects in a fault-tolerant log, allowing the platform to compensate effects left behind when a function fails or its instance crashes. Against a manually implemented saga, the authors report eliminating compensation code and leaving no compensatable effects after tested failures. Each intercepted call adds about 1 ms; under load, CPU-bound functions have lower sustainable throughput, while latency overhead becomes negligible when external service calls dominate invocation time. This summary is based on the supplied abstract. ### Source excerpt arXiv:2611.06601v1 Announce Type: new Abstract: Function-as-a-Service (FaaS) simplifies distributed application development by offloading operational concerns to the platform, but developers remain responsible for recovering from failures. Retries require idempotent functions, which means that developers have to write complex, error-prone code that undoes or skips the already completed operations of a failed function. To address this, we propose AtomicFaaS, a platform extension that provides semantic atomicity for the external side effects of a function invocation, whether the function fails or its instance crashes, without developer-written compensation code. AtomicFaaS intercepts outbound calls and records their effects in a fault-tolerant log, from which the platform compensates leaked effects outside the failed instance. Compared with a manually implemented saga, AtomicFaaS eliminates compensation code and leaves no compensatable effects after any tested failure, while adding ~1 ms per intercepted call. Under load, it lowers the sustainable throughput of CPU-bound functions, while its latency overhead becomes negligible once calls to external services dominate the invocation time. ## Same Function, Different CPU: Quantifying Hardware Heterogeneity in Commercial FaaS Platforms DevFeed: [Same Function, Different CPU: Quantifying Hardware Heterogeneity in Commercial FaaS Platforms]() Original publisher: [Read original article]() Author: Jonas Heisterberg, Natalie Carl, Trever Schirmer, David Bermbach Published: 2026-11-03T05:00:00Z Content type: article Language: en Sources: [arXiv: Distributed Computing]() Topics: [function as a service](), [cloud-infrastructure](), [Distributed Systems & Parallel Computing](), [cloud cost management]() Tags: [abstract](), [abstraction](), [alibaba](), [all](), [arxiv](), [availability](), [aws](), [aws-lambda](), [azure](), [azure-functions](), [bill](), [can](), [cloud](), [cloud-run](), [composition](), [cpu](), [cs-dc](), [faas](), [function](), [hardware](), [platforms]() ### AI overview Across four commercial FaaS platforms, hardware differences can strongly affect function performance and duration-based cost, though their role varies by provider. Variance decomposition attributes 4% to 97% of performance variance to CPU heterogeneity on AWS Lambda and Google Cloud Run Functions. On Azure Functions, a platform-level mechanism creates bimodal execution durations and overshadows CPU effects. AWS Lambda's CPU composition shifts within a week, while CPU types on Google Cloud Run Functions and Alibaba Function Compute differ in instruction extensions such as SHA-NI, widening performance gaps. The slowest CPU type can cost up to three times as much per invocation as the fastest. ### Source excerpt arXiv:2611.02990v1 Announce Type: new Abstract: Serverless computing, particularly Function-as-a-Service (FaaS), abstracts operational concerns such as scaling, scheduling, and resource management from users. This abstraction, however, conceals a heterogeneous hardware fleet on which function execution environments are scheduled behind a unified function interface. Consequently, otherwise identical invocations may run on different CPU types that differ in performance, a hardware lottery that users can neither see nor control. Since the resulting variability is entangled with other sources, such as noisy neighbors or diurnal effects, its contribution to overall performance variability and how this contribution differs across providers remain unclear. In this paper, we systematically study hardware heterogeneity and its impact on function performance across four widely used commercial FaaS platforms, covering regional, configurational, and temporal differences. Using variance decomposition, we find that CPU heterogeneity alone explains 4 % to 97 % of the performance variance on AWS Lambda and Google Cloud Run Functions. On Azure Functions, in contrast, we identify a platform-level mechanism that produces bimodal execution durations and overshadows CPU effects. We observe that the CPU composition on AWS Lambda shifts within a single week. Additionally, we reveal that on Google Cloud Run Functions and Alibaba Function Compute, the availability of instruction set extensions such as SHA-NI differs between CPU types, widening the performance gap further. Since all platforms bill by duration, performance gaps between CPU types translate directly into cost, with invocations on the slowest CPU type costing up to three times as much as those on the fastest. ## Optimizing OpenFaaS on Kubernetes: Comparative Analysis of Language Runtimes and Cluster Distributions DevFeed: [Optimizing OpenFaaS on Kubernetes: Comparative Analysis of Language Runtimes and Cluster Distributions]() Original publisher: [Read original article]() Author: Ehsan Ataie, Mohammadreza Pooshani, Hossein Aqasizade Published: 2026-10-30T04:00:00Z Content type: comparison Language: en Sources: [arXiv: Distributed Computing](), [arXiv: Software Engineering]() Topics: [OpenFaaS](), [Kubernetes](), [function as a service](), [performance-engineering](), [Go Language](), [Node.js]() Tags: [concurrent](), [cpu](), [cs-dc](), [cs-pf](), [cs-se](), [delay](), [faas](), [go](), [impact](), [infrastructure](), [insights](), [k3s](), [kubernetes](), [latency](), [low-latency](), [metrics](), [node-js](), [performance](), [python](), [runtimes](), [serverless](), [throughput](), [trade]() ### AI overview The study evaluates OpenFaaS on Kubeadm, K3s, MicroK8s, and K0s using CloudLab, comparing Python, Go, and Node.js functions under different levels of concurrent invocation. It measures throughput, CPU usage, and delay. The abstract reports that Go consistently achieves higher throughput and lower CPU usage than Python and Node.js. K3s and Kubeadm perform best among the Kubernetes distributions: K3s shows high throughput, while Kubeadm maintains low latency and efficient CPU usage. The excerpt does not provide detailed measurements or results for MicroK8s and K0s. ### Source excerpt arXiv:2604.05496v2 Announce Type: replace-cross Abstract: Serverless computing, particularly Function-as-a-Service (FaaS), has revolutionized cloud computing by abstracting infrastructure management and enabling dynamic resource allocation. This paper examines the performance and compatibility of OpenFaaS, an open-source serverless platform, when deployed on various Kubernetes distributions, including Kubeadm, K3s, MicroK8s, and K0s. Moreover, leveraging the CloudLab infrastructure, this study examines the impact of Python, Go, and Node$.$js programming languages on the performance of Kubernetes-enabled OpenFaaS, specifically when these languages are used to develop functions deployed on the platform. The performance is evaluated and analyzed under various levels of concurrent invocations using several usage-level metrics, such as throughput and CPU usage, as well as responsiveness metrics, such as delay. According to our findings, Go consistently outperforms Python and Node$.$js in terms of throughput and CPU usage, making it the ideal runtime for serverless applications. Among the Kubernetes distributions, K3s and Kubeadm exhibit superior performance, with Kubeadm maintaining low latency and efficient CPU usage, and K3s demonstrating high throughput. This study provides valuable insights into optimizing the Kubernetes-enabled OpenFaaS platform, highlighting the strengths and trade-offs of different Kubernetes distributions and language runtimes. ## A Unified Queueing-Based Framework for Comparative Evaluation of Serverless Autoscaling Strategies under Bursty Workloads DevFeed: [A Unified Queueing-Based Framework for Comparative Evaluation of Serverless Autoscaling Strategies under Bursty Workloads]() Original publisher: [Read original article]() Author: Zahra Zafarzade, Ehsan Ataie Published: 2026-10-27T04:00:00Z Content type: news Language: en Sources: [arXiv: Distributed Computing]() Topics: [autoscaling](), [queueing theory](), [function as a service](), [Simulation](), [Latency]() Tags: [activation](), [automatic](), [autoscaling](), [behavior](), [capacity](), [concurrency](), [configuration](), [critical](), [cs-dc](), [delay](), [evaluation](), [faas](), [framework](), [h2](), [java](), [load](), [metric](), [metrics](), [modeling](), [serverless](), [strategies]() ### AI overview The study compares scale-per-request (SPR), concurrency-level scaling (CLS), and metric-based scaling (MBS) using a unified queueing model: analytical queueing networks combined with queueing Petri nets in Java Modeling Tools, evaluated under two-phase hyper-exponential arrivals. It tracks throughput, mean response time, system population, and power while modeling function-instance activation, concurrency limits, and queue buildup at saturation. The abstract reports a performance-stability trade-off: SPR has the lowest delay at moderate load but is fragile near capacity; CLS depends on concurrency tokens and is highly configuration-sensitive under bursty conditions; MBS provides the steadiest QoS across the tested workload range by limiting utilization and queue growth under critical load. ### Source excerpt arXiv:2610.35564v1 Announce Type: new Abstract: Serverless computing platforms must preserve quality of service (QoS) while adapting to highly bursty and unpredictable workloads. To cope with workload fluctuations while satisfying QoS requirements and service-level agreements (SLAs), these platforms rely on automatic resource scaling. Commercial function-as-a-service (FaaS) platforms employ different autoscaling strategies to dynamically adjust the number of active function instances and the available processing capacity. In this work, three representative autoscaling strategies are modeled and compared: scale-per-request (SPR), concurrencylevel scaling (CLS), and metric-based scaling (MBS). To enable systematic evaluation independent of proprietary platform-specific implementations, we develop a unified modeling framework that combines analytical queueing network (QN) modeling with queueing Petri nets (QPN) implemented in Java Modeling Tools (JMT) to evaluate autoscaling behavior under two-phase hyper-exponential (H2) arrivals. Evaluation is conducted using four system-level metrics: throughput, mean response time, system population, and system power. The framework captures dynamic function instance (FI) activation, concurrency constraints, and queue buildup under saturation. Comparative analysis reveals a clear trade-off between peak performance and stability: SPR provides the lowest delay under moderate load but becomes fragile near capacity limits; CLS is governed by concurrency tokens and is highly sensitive to configuration under bursty conditions; whereas MBS provides the most stable QoS across the workload range by keeping utilization below a critical region and preventing explosive queue growth under critical load. ## Serverless Framework Is Now Built for AI Coding Agents DevFeed: [Serverless Framework Is Now Built for AI Coding Agents]() Original publisher: [Read original article]() Author: Serverless Team Published: 2026-09-28T00:00:00Z Content type: release Language: en Sources: [Serverless]() Topics: [Serverless](), [serverless framework]() Tags: [agent-skills](), [ai-coding-agents](), [aws](), [aws-lambda](), [cloud-computing](), [faas](), [function-as-a-service](), [infrastructure-as-code](), [news](), [serverless](), [serverless-architecture](), [serverless-framework]() ### AI overview Serverless Framework v4.43.0 adds agentic onboarding, commands, and bundled Agent Skills to help AI coding agents set up, build, and deploy services independently. The document also mentions improvements in v4.42.0 and v4.43.0. ### Source excerpt Serverless Framework v4.43.0 adds agentic onboarding, new agent commands, and bundled Agent Skills so AI coding agents can set up, build, and deploy services on their own. Plus the improvements shipped in v4.42.0 and v4.43.0. ## Deploy MCP Servers on AWS Lambda with Serverless Framework v4 DevFeed: [Deploy MCP Servers on AWS Lambda with Serverless Framework v4]() Original publisher: [Read original article]() Author: Serverless Team Published: 2026-08-11T00:00:00Z Content type: tutorial Language: en Sources: [Serverless]() Topics: [Model Context Protocol](), [AWS Lambda](), [serverless framework](), [Streaming]() Tags: [aws](), [aws-lambda](), [cloud-computing](), [faas](), [function-as-a-service](), [infrastructure-as-code](), [mcp](), [news](), [pricing](), [serverless](), [serverless-architecture](), [serverless-framework](), [streaming](), [yaml]() ### AI overview A Serverless Framework v4.41.0 deployment workflow for MCP servers on AWS Lambda, using YAML and providing an HTTPS endpoint, response streaming, and pay-per-request pricing. ### Source excerpt Serverless Framework v4.41.0 deploys MCP servers to AWS Lambda in a few lines of YAML - a real HTTPS endpoint, response streaming, and pay-per-request pricing. ## Easily Develop AWS Lambda MicroVM Sandbox Architectures DevFeed: [Easily Develop AWS Lambda MicroVM Sandbox Architectures]() Original publisher: [Read original article]() Author: Serverless Team Published: 2026-07-21T00:00:00Z Content type: article Language: en Sources: [Serverless]() Topics: [AI Sandbox](), [AWS Lambda](), [virtual machines](), [Firecracker](), [Amazon Web Services]() Tags: [agent](), [aws](), [aws-lambda](), [cloud-computing](), [code](), [emulator](), [faas](), [firecracker](), [framework](), [function-as-a-service](), [infrastructure-as-code](), [local](), [microvm](), [news](), [sandbox](), [serverless](), [serverless-architecture](), [serverless-framework](), [test](), [virtual](), [virtual-machines]() ### AI overview AWS Lambda MicroVMs run untrusted or agent-generated code in isolated Firecracker virtual machines. The Serverless Framework exposes them as Sandboxes in `serverless.yml`, with one-command deployment and a local MicroVM emulator for building and testing before shipping. ### Source excerpt AWS Lambda MicroVMs are a new compute primitive for running untrusted and agent-generated code in fully isolated Firecracker virtual machines. The Serverless Framework brings them into serverless.yml as Sandboxes, with one-command deploys and a full local MicroVMs emulator so you can build and test before you ship. ## AWS Bedrock AgentCore, S3 File Mounts, Python & Install Improvements DevFeed: [AWS Bedrock AgentCore, S3 File Mounts, Python & Install Improvements]() Original publisher: [Read original article]() Author: Serverless Team Published: 2026-05-27T00:00:00Z Content type: release Language: en Sources: [Serverless]() Topics: [Amazon Bedrock](), [Amazon S3](), [Python](), [Framework](), [Serverless](), [Security]() Tags: [aws](), [aws-lambda](), [bedrock](), [bug](), [bug-fixes](), [cloud-computing](), [faas](), [framework](), [function-as-a-service](), [improvements](), [infrastructure-as-code](), [install](), [news](), [python](), [s3](), [security](), [serverless](), [serverless-architecture](), [serverless-framework](), [update]() ### AI overview Serverless Framework's May 2026 update covers built-in AWS Bedrock AgentCore support, S3 file system mounts for Lambda, faster Framework installs, Python improvements, bug fixes, and security updates. ### Source excerpt Serverless Framework's May 2026 update covering built-in AWS Bedrock AgentCore support, S3 file system mounts for Lambda, faster Framework installs, Python improvements, bug fixes, and security updates. ## PaaS vs IaaS vs SaaS: What Each Means and Who Should Pick What in 2026 DevFeed: [PaaS vs IaaS vs SaaS: What Each Means and Who Should Pick What in 2026]() Original publisher: [Read original article]() Author: Angelo Saraceno Published: 2026-05-27T00:00:00Z Content type: article Language: en Sources: [Railway]() Topics: [Comparing SaaS, PaaS, and IaaS](), [Platform as a Service (PaaS)](), [The Three Main Cloud Service Models](), [Use Cases for Every Cloud Service Model](), [Emerging Cloud Service Models](), [Infrastructure as a Service (IaaS)](), [Serverless](), [Shared Responsibility Model](), [virtual machines]() Tags: [2026](), [agentic](), [cloud](), [faas](), [framework](), [guide](), [iaas](), [railway](), [saas](), [vs]() ### AI overview The article expands the cloud service model from IaaS, PaaS, and SaaS to include FaaS and agentic-PaaS, where platforms expose structured controls to LLM agents, often through MCP. It frames the choice as a tradeoff: more abstraction reduces operational work but limits flexibility. IaaS or bare metal suit teams needing custom hardware, kernels, isolation, or platform-level control; PaaS fits teams that want to deploy applications without managing machines; FaaS suits bursty, event-driven tasks but can constrain long-running processes; SaaS is for using a complete hosted application. The author recommends weighing team and workload shape, control and compliance needs, ops capacity, and traffic variability. The author favors PaaS and discloses working for Railway, a PaaS provider; the piece acknowledges that IaaS can be right for specialized hardware, platform engineering teams, or workloads where unit economics favor it. ### Source excerpt The classic three-tier model needs an update. A 2026 guide to IaaS, PaaS, SaaS, FaaS, and the emerging agentic-PaaS tier, with concrete examples and a decision framework. ## What is a PaaS? A Developer's Guide for 2026 DevFeed: [What is a PaaS? A Developer's Guide for 2026]() Original publisher: [Read original article]() Author: Angelo Saraceno Published: 2026-05-25T00:00:00Z Content type: article Language: en Sources: [Railway]() Topics: [Platform as a Service (PaaS)](), [Comparing SaaS, PaaS, and IaaS](), [Use Cases for Every Cloud Service Model](), [The Three Main Cloud Service Models](), [CI/CD](), [function as a service](), [Emerging Cloud Service Models](), [Developer experience](), [GitHub]() Tags: [2026](), [add-ons](), [agent](), [agentic](), [agents](), [anthropic](), [api](), [application](), [aws](), [aws-ec2](), [aws-elastic-beanstalk](), [aws-lambda](), [azure-virtual-machines](), [cloud](), [developer](), [faas](), [guide](), [heroku](), [iaas](), [railway](), [saas](), [what-is]() ### AI overview A PaaS abstracts the host OS, runtime, builds, secrets, TLS and managed services so developers deploy an application process while the platform runs it. The article defines five core primitives: managed runtime, build abstraction, managed add-ons, Git-based deploys and promotion of the same build across environments. It distinguishes PaaS from IaaS (more infrastructure control), SaaS (finished hosted software) and FaaS (event-triggered functions, suited to spiky workloads but poorly suited to long-running stateful services). Modern platforms add private networking, managed databases, multi-region options and agent-driven workflows through APIs or MCP. Its evaluation checklist covers native deploy and promotion, database integration, pricing incentives, agent access, portability, regional support and who handles on-call. It names Railway, Render, Fly.io and Northflank as current PaaS options with differing tradeoffs, without offering a detailed comparative ranking. ### Source excerpt The contract Heroku invented in 2007, the five primitives that define a real PaaS today, the difference between PaaS / IaaS / SaaS / FaaS, and how to evaluate one in 2026. ## Newsletter: Managed Instances, Durable Functions, Core Integrations DevFeed: [Newsletter: Managed Instances, Durable Functions, Core Integrations]() Original publisher: [Read original article]() Author: Serverless Team Published: 2026-01-20T00:00:00Z Content type: news Language: en Sources: [Serverless]() Topics: [serverless framework](), [Serverless](), [Amazon Web Services](), [Single sign-on (SSO)]() Tags: [2026](), [aws](), [aws-lambda](), [cloud-computing](), [faas](), [function-as-a-service](), [infrastructure-as-code](), [integrations](), [lambda](), [news](), [serverless](), [serverless-architecture](), [serverless-framework](), [sso]() ### AI overview A Serverless Framework Q1 2026 newsletter covers Lambda managed instances, durable functions, native AppSync support, built-in pruning, AWS SSO login, and integrations moved from plugins into core. ### Source excerpt Serverless Framework's Q1 2026 newsletter covering Lambda managed instances, durable functions, native AppSync support, built-in pruning, AWS SSO login, and plugin-to-core integrations. ## AWS re:Invent serverless features available in Serverless Framework v4 DevFeed: [AWS re:Invent serverless features available in Serverless Framework v4]() Original publisher: [Read original article]() Author: Serverless Team Published: 2025-12-09T00:00:00Z Content type: article Language: en Sources: [Serverless]() Topics: [Serverless](), [serverless framework](), [Amazon Web Services](), [Streaming](), [identity and access management](), [Network]() Tags: [apis](), [aws](), [aws-lambda](), [cloud-computing](), [faas](), [features](), [framework](), [function-as-a-service](), [iam](), [infrastructure-as-code](), [livestream](), [networking](), [news](), [serverless](), [serverless-architecture](), [serverless-framework](), [streaming](), [subscription]() ### AI overview Serverless Framework v4 already includes several AWS re:Invent serverless launches, including new runtimes, streaming APIs, tenant isolation, and expanded IAM and networking controls. The article also mentions a livestream deep-dive and a limited-time subscription offer. ### Source excerpt AWS re:Invent's biggest serverless launches are already live in Serverless Framework v4, with new runtimes, streaming APIs, tenant isolation, enriched IAM and networking controls, and more - all ready to use today, alongside a livestream deep-dive and a limited-time subscription offer. ## Newsletter: Improved Python Support, Merging Plugins into Core, and more DevFeed: [Newsletter: Improved Python Support, Merging Plugins into Core, and more]() Original publisher: [Read original article]() Author: Serverless Team Published: 2025-11-18T00:00:00Z Content type: news Language: en Sources: [Serverless]() Topics: [serverless framework](), [Python](), [configuration](), [identity and access management](), [OpenTelemetry]() Tags: [aws](), [aws-lambda](), [cloud-computing](), [configuration](), [faas](), [function-as-a-service](), [how-to](), [iam](), [infrastructure-as-code](), [news](), [opentelemetry](), [plugins](), [python](), [serverless](), [serverless-architecture](), [serverless-framework](), [updates]() ### AI overview A Serverless Framework newsletter recaps built-in Python support, plugin consolidation into core, reliability improvements during upstream outages, IAM changes, and planned OpenTelemetry configuration and expanded variable resolution. It also mentions a limited-time discount and hiring for the Framework team. ### Source excerpt Serverless Framework is closing out the year with major updates--new Python support built in, improved reliability during upstream outages, key IAM changes, and upcoming features like OpenTelemetry configuration and expanded variable resolution--while also offering a limited-time 20% end-of-year discount and actively hiring for the Framework team. This post recaps what's new, what's coming next, and how to get involved. ## Self-Hosting AI Agents on AWS with Serverless Container Framework v2 DevFeed: [Self-Hosting AI Agents on AWS with Serverless Container Framework v2]() Original publisher: [Read original article]() Author: Serverless Team Published: 2025-06-05T00:00:00Z Content type: article Language: en Sources: [Serverless]() Topics: [Amazon Web Services](), [Serverless](), [Self-hosted](), [Framework]() Tags: [agents](), [ai](), [ai-agents](), [apis](), [aws](), [aws-lambda](), [build](), [cloud-computing](), [container](), [engineering](), [events](), [faas](), [framework](), [function-as-a-service](), [infrastructure](), [infrastructure-as-code](), [news](), [self-hosting](), [serverless](), [serverless-architecture](), [serverless-framework](), [slack]() ### AI overview Serverless Container Framework v2 is presented as a way to deploy AI agents on self-managed AWS infrastructure, with integrations for APIs, events, and Slack. ### Source excerpt Serverless Container Framework v2 (SCF v2) makes it easy to deploy powerful AI agents on your own AWS infrastructure--integrating seamlessly with APIs, events, and Slack. Skip the PaaS limitations and build agents that actually fit your stack. ## Introducing the Serverless MCP DevFeed: [Introducing the Serverless MCP]() Original publisher: [Read original article]() Author: Serverless Team Published: 2025-04-17T00:00:00Z Content type: release Language: en Sources: [Serverless]() Topics: [Model Context Protocol](), [ide](), [Amazon API Gateway](), [AWS Lambda](), [debugging](), [Amazon Web Services](), [Monitoring]() Tags: [api-gateway](), [applications](), [aws](), [aws-lambda](), [cloud-computing](), [debugging](), [faas](), [function-as-a-service](), [ide](), [infrastructure-as-code](), [mcp](), [monitoring](), [news](), [serverless](), [serverless-architecture](), [serverless-framework]() ### AI overview The Serverless MCP is presented as a debugging tool that brings AWS Lambda, DynamoDB, and API Gateway state and logs into an IDE. It supports troubleshooting serverless applications with AI-assisted diagnostics and resource monitoring. ### Source excerpt The Serverless MCP is a powerful debugging tool that brings AWS Lambda, DynamoDB, and API Gateway state and logs directly into your IDE. Eliminate AWS console visits while troubleshooting serverless applications with AI-assisted diagnostics and resource monitoring. ## Introducing Serverless Container Framework DevFeed: [Introducing Serverless Container Framework]() Original publisher: [Read original article]() Author: Serverless Team Published: 2025-02-04T00:00:00Z Content type: release Language: en Sources: [Serverless]() Topics: [Serverless](), [AWS Fargate](), [AWS Lambda](), [container](), [Deployment](), [Framework](), [software-development]() Tags: [aws](), [aws-fargate](), [aws-lambda](), [cloud-computing](), [container](), [containers](), [deployment](), [development](), [faas](), [framework](), [function-as-a-service](), [infrastructure-as-code](), [news](), [serverless](), [serverless-architecture](), [serverless-framework]() ### AI overview Serverless introduces a Container Framework for deploying containers to AWS Lambda and AWS Fargate without rearchitecting. ### Source excerpt A rich development and deployment experience for deploying containers to AWS Lambda and AWS Fargate, without rearchitecting. Serverless Container Framework gives you total flexibility. ## Introducing The AWS AI Stack DevFeed: [Introducing The AWS AI Stack]() Original publisher: [Read original article]() Author: Serverless Team Published: 2024-09-11T00:00:00Z Content type: release Language: en Sources: [Serverless]() Topics: [Artificial Intelligence](), [Amazon Web Services](), [Amazon Bedrock](), [Large Language Model](), [AWS Lambda](), [Boilerplate](), [Claude](), [React](), [Serverless](), [Back end](), [Front end](), [llama]() Tags: [ai](), [aws](), [aws-lambda](), [back-end](), [bedrock](), [ci-cd](), [claude](), [cloud-computing](), [faas](), [front-end](), [function-as-a-service](), [infrastructure-as-code](), [llama3](), [llms](), [news](), [react](), [serverless](), [serverless-architecture](), [serverless-framework]() ### AI overview The AWS AI Stack is introduced as a full-stack, serverless boilerplate for building AI applications on AWS. It uses Bedrock LLMs including Claude 3.5 Sonnet and Llama3.1, with a React front end, AWS Lambda back end, and built-in CI/CD. ### Source excerpt Full-stack, serverless, boilerplate for AI applications on AWS, featuring Bedrock LLMs like Claude 3.5 Sonnet and Llama3.1, a React front-end, AWS Lambda back-end, built-in CI/CD and more. ## Blog: Introducing Falco Talon v0.1.0 DevFeed: [Blog: Introducing Falco Talon v0.1.0]() Original publisher: [Read original article]() Published: 2024-09-09T00:00:00Z Content type: release Language: en Sources: [Falco]() Topics: [Falco](), [Kubernetes](), [No-code](), [function as a service](), [Security]() Tags: [blog](), [faas](), [falco](), [kubernetes](), [kubernetes-clusters](), [no-code](), [release](), [security](), [talon](), [version]() ### AI overview This release introduces Falco Talon v0.1.0, a response engine for managing threats in Kubernetes clusters. It is designed to let users define rules and react to Falco events without building and maintaining custom FaaS-based response systems. ### Source excerpt More than 7 years ago, frustrated by the lack of integrations between Falco and third parties, I created Falcosidekick. The tool evolved much more than expected, with the help of dozens of contributors, individuals or for companies, to have now almost 70 different integrations, and more are coming. Its baby brother came few years later, Falcosidekick UI, helping people to visualize in real time the alerts leveraged by Falco and fine tuning their rules. A frustation remained after all. With Falco, we have an amazing tool to detect suspicious events in our Linux hosts, VM and Kubernetes clusters, with Falcosidekick, we can easily notify our Dev/Secops, index the alerts in some SIEM, etc. But a last piece was missing: how to react to these events? With the integrations of well known FaaS in Falcosidekick, we started a series of blog posts to show how to create from scratch what we call a "response engine". All these systems are modular, flexible, robust, but they all require a considerable amount of work from the user, to deal with the Falco payload format, the errors, the retries, the authentication to the API (AWS, Kubernetes Control Plane), the logs, the metrics, etc. Not all users and companies have the skills and/or the budget to maintain such an architecture. To answer these needs, we designed and created Falco Talon. The the first version is officially out!. What is Falco Talon? Falco Talon is a Response Engine for managing threats in Kubernetes clusters. It enhances the solutions proposed by the Falco community with a no-code tailor-made solution. With easy rules, you can react to events from Falco in milliseconds. Why did we created Falco Talon? Over the years, the Falco community proposed different methods to react to the Falco Events, what we call a response engine. All these methods rely on a 3rd party FaaS (Function as a Service) and come with drawbacks, all actions must be developped by the users to manage: The errors The Falco event format The authentica ## Axiom Integration for AWS Lambda Observability in Serverless Framework DevFeed: [Axiom Integration for AWS Lambda Observability in Serverless Framework]() Original publisher: [Read original article]() Author: Serverless Team Published: 2024-08-26T00:00:00Z Content type: article Language: en Sources: [Serverless]() Topics: [AWS Lambda](), [serverless framework](), [observability](), [Amazon Web Services](), [log management](), [tracing](), [Amazon CloudWatch Logs]() Tags: [aws](), [aws-lambda](), [cloud-computing](), [engineering](), [faas](), [function-as-a-service](), [infrastructure-as-code](), [logs](), [metrics](), [news](), [observability](), [serverless](), [serverless-architecture](), [serverless-framework](), [traces]() ### AI overview The article describes integrating Axiom with Serverless Framework to provide logs, metrics, and traces for AWS Lambda observability, with a claimed cost advantage over AWS CloudWatch. ### Source excerpt Optimize AWS Lambda observability with Axiom and Serverless Framework. Save on costs compared to AWS CloudWatch while enjoying seamless logs, metrics, and traces integration. Simple setup, advanced configuration, and non-disruptive transition for efficient serverless deployments. ## Serverless Framework Now Supports AWS SAM & Cloudformation DevFeed: [Serverless Framework Now Supports AWS SAM & Cloudformation]() Original publisher: [Read original article]() Author: Serverless Team Published: 2024-08-26T00:00:00Z Content type: release Language: en Sources: [Serverless]() Topics: [serverless framework](), [AWS CloudFormation](), [AWS Lambda](), [Amazon Web Services](), [software-development]() Tags: [aws](), [aws-lambda](), [cloud-computing](), [cloudformation](), [faas](), [function-as-a-service](), [infrastructure-as-code](), [news](), [sam](), [serverless](), [serverless-architecture](), [serverless-framework](), [workflow]() ### AI overview Serverless Framework 4.2.3 adds support for AWS SAM and CloudFormation templates, enabling deployment of SAM and CloudFormation projects alongside Serverless Variables for AWS Lambda development. ### Source excerpt Serverless Framework 4.2.3 now supports AWS SAM and CloudFormation templates, simplifying AWS Lambda development. Deploy SAM/CF projects seamlessly, use Serverless Variables, and streamline your workflow. Perfect for organizations seeking to consolidate serverless tools and standardize development processes. ## Serverless Framework V4 Generally Available DevFeed: [Serverless Framework V4 Generally Available]() Original publisher: [Read original article]() Author: Serverless Team Published: 2024-06-13T00:00:00Z Content type: release Language: en Sources: [Serverless]() Topics: [serverless framework](), [software-development](), [TypeScript](), [Terraform](), [Logging](), [Security]() Tags: [aws](), [aws-lambda](), [cloud-computing](), [development](), [faas](), [function-as-a-service](), [infrastructure-as-code](), [logging](), [news](), [security](), [serverless](), [serverless-architecture](), [serverless-framework](), [terraform](), [typescript]() ### AI overview Serverless Framework V4 is generally available, adding native TypeScript support, a revamped Dev Mode, integrations with HashiCorp Terraform and Vault, and advanced AWS Lambda logging. ### Source excerpt Discover the power of Serverless Framework V.4! Native TypeScript support, revamped Dev Mode, and seamless integration with HashiCorp Terraform and Vault. Enjoy faster development, enhanced security, and powerful new features like advanced AWS Lambda logging. Get started now via GitHub or npm. ## Serverless Framework V4: A New Model DevFeed: [Serverless Framework V4: A New Model]() Original publisher: [Read original article]() Author: Serverless Team Published: 2023-10-26T00:00:00Z Content type: article Language: en Sources: [Serverless]() Topics: [serverless framework](), [Framework]() Tags: [aws](), [aws-lambda](), [cloud-computing](), [faas](), [fees](), [framework](), [function-as-a-service](), [infrastructure-as-code](), [news](), [revenue](), [serverless](), [serverless-architecture](), [serverless-framework]() ### AI overview Serverless Framework V4 will introduce revenue sharing for those extending the Framework and fees for organizations with more than $2 million in annual revenue. ### Source excerpt Serverless Framework V.4 will introduce revenue sharing w/ those extending the Framework & fees for orgs w/ $2M+ in annual revenue. ## Meta's serverless platform processes trillions of function calls a day DevFeed: [Meta's serverless platform processes trillions of function calls a day]() Original publisher: [Read original article]() Author: Engineer's Codex Published: 2023-10-22T17:32:27Z Content type: article Language: en Sources: [Engineer's Codex]() Topics: [Meta](), [function as a service](), [AWS Lambda](), [Google Cloud Platform (GCP)](), [Cloud](), [Benchmark]() Tags: [aws](), [aws-lambda](), [azure](), [azure-functions](), [cloud-functions](), [faas](), [function-as-a-service-faas](), [meta](), [performance](), [serverless]() ### AI overview This article summarizes and explains Meta's XFaaS, a private serverless platform for non-user-facing functions. It describes how XFaaS reduces startup overhead, predicts spiky workloads, and spreads work across time and datacenters to improve hardware utilization and manage cost. ### Source excerpt XFaaS is Meta's private platform for "Hyperscale and Low Cost Serverless Functions." It is more efficient than AWS Lambda, Azure Functions, and Google Cloud Functions. ## Serverless Cloud spins off as Ampt DevFeed: [Serverless Cloud spins off as Ampt]() Original publisher: [Read original article]() Author: Austen Collins Published: 2022-11-15T00:00:00Z Content type: news Language: en Sources: [Serverless]() Topics: [Serverless](), [observability]() Tags: [aws](), [aws-lambda](), [cloud-computing](), [faas](), [function-as-a-service](), [infrastructure-as-code](), [news](), [observability](), [serverless](), [serverless-architecture](), [serverless-framework]() ### AI overview Serverless Cloud has spun off as Ampt, while Serverless Inc. remains committed to observability. ### Source excerpt Serverless Cloud spins off as Ampt while Serverless Inc. stays committed to the observability mission. [Next page]()