# Deployment Strategies Deployment strategies are processes for delivering software from one version to another, including in-place, blue/green, canary, linear, rolling, and all-at-once deployments. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## New Hampshire software freedom bill hearing scheduled for 21 February 2023 DevFeed: [New Hampshire software freedom bill hearing scheduled for 21 February 2023]() Original publisher: [Read original article]() Author: Leah Rowe Published: 2026-09-17T04:32:50.666044Z Content type: news Language: en Sources: [News about Libreboot releases and development]() Topics: [Software](), [Deployment](), [Deployment Strategies](), [systems]() Tags: [article](), [bios](), [canoeboot](), [coreboot](), [deployment](), [deployment-strategies](), [free-software](), [information-technology](), [libre](), [libreboot](), [mastodon](), [news](), [opensource](), [uefi]() ### AI overview The article reports on an upcoming New Hampshire House Bill 556-FN hearing concerning software freedom. It describes provisions that would require the New Hampshire Information Technology Council to study promoting libre software and reducing citizens' reliance on proprietary software in state interactions. ### Source excerpt Article: NEW New Hampshire software freedom bill hearing on 21 February 2023 needs your support! Web link: https://libreboot.org/news/usa-libre-part3.html ## Five Deployment Strategies for Safer Software Releases DevFeed: [Five Deployment Strategies for Safer Software Releases]() Original publisher: [Read original article]() Author: Saurabh Dashora Published: 2026-05-12T07:05:17Z Content type: article Language: en Sources: [System Design Codex]() Topics: [Deployment Strategies](), [Deployment](), [progressive delivery](), [App]() Tags: [blue-green-deployment](), [deployment-strategies](), [downtime](), [load-balancer](), [progressive-delivery](), [rollback]() ### AI overview This article discusses deployment strategies for reducing release risk, downtime, and user impact. It explains blue/green deployment and canary deployment, including their workflows, benefits, tradeoffs, and suitable use cases, and introduces A/B testing as part of a release process. ### Source excerpt Every developer should know these ## Platform Engineering Extends DevOps by Treating the Internal Developer Platform as a Product DevFeed: [Platform Engineering Extends DevOps by Treating the Internal Developer Platform as a Product]() Original publisher: [Read original article]() Author: Wix Engineering Published: 2025-12-21T09:12:39Z Content type: opinion Language: en Sources: [Wix Engineering]() Topics: [Platform Engineering](), [DevOps](), [internal developer platform](), [Kubernetes](), [workflow automation](), [Deployment Strategies](), [GitHub Actions](), [GitLab](), [networking](), [Security]() Tags: [automation](), [deployment-strategies](), [devops](), [github](), [gitlab](), [internal-developer-platform](), [kubernetes](), [networking](), [pipelines](), [platform-engineering](), [policy](), [workflow-automation]() ### AI overview The article argues that platform engineering is a distinct discipline from DevOps. It focuses on treating an internal developer platform as a product and providing reusable, self-service systems that reduce infrastructure friction and operational complexity for developers. ### Source excerpt Imagine starting your day as a senior engineering manager at a fast growing tech company. You're barely through your first coffee when your phone lights up with a critical ticket. The Kubernetes environments don't match across stages. DevOps is in place. CI/CD, automation, monitoring, on-call rotations. And still, developers spend more time fighting infrastructure than building products. This is the moment many organizations mistakenly try to "add more DevOps." In reality, this is the moment... ## How the Fury ecosystem manages high-frequency deployments with automation, monitoring, and resilience DevFeed: [How the Fury ecosystem manages high-frequency deployments with automation, monitoring, and resilience]() Original publisher: [Read original article]() Author: Juan José Orjuela Published: 2025-10-21T13:12:29Z Content type: tutorial Language: en Sources: [Mercado Libre Tech]() Topics: [Deployment](), [Deployment Strategies](), [Automation](), [Resilience](), [configuration](), [Environment Variables]() Tags: [architecture](), [automation](), [configuration](), [continuous-deployment](), [deployment](), [deployment-strategies](), [devops](), [environment-variables](), [metrics](), [on-call](), [optimization](), [resilience](), [scalability](), [vulnerabilities]() ### AI overview This article explains how the Fury ecosystem defines and manages deployments at high frequency. It covers deployment strategies, configuration changes, automatic redeployments, testing, monitoring, alerts, metrics, and on-call practices intended to maintain operational stability. ### Source excerpt "How do you manage to perform 30.000 deployments a day and still operate normally?" For many, that number sounds simply unattainable. Deploying thousands of times a day suggests chaos, constant risk, and an operation that's hard to sustain. But the reality is different: not only do we make it possible -- we do it with control, resilience, and full visibility. It's not magic or luck. It's about a deployment ecosystem built to scale, with robust processes, smart automation, and practices that let us maintain stability even with frequent changes. +750K deployments in July 2025, broken down by deployment strategy. In this article, we'll explain how our model works: what we consider a deployment, how our system is structured, what tools we use to monitor operational health, and how we respond to alerts and failures -- whether they happen in test environments or in the most critical areas of the business. Yes, we'll also cover alerts, metrics, and on-call rotations -- and why they're essential to maintaining a continuous and reliable deployment model at this scale. General outline of a deployment in the Fury ecosystem.What do we consider a deployment? In Fury, a deployment isn't just about pushing new code. We consider a deployment to be any action that updates the state of an application in a given environment. For example: Deploying a new code change to the platform using any of our supported strategies, such as Blue-Green, Canary-Safe, Rolling Update, or All-In. Updating environment variables or configuration properties: any change to these parameters requires a deployment, as it involves recreating the application with the new active configuration. Automatic re-deployments: these can be triggered not only due to detected vulnerabilities but also due to optimization opportunities that help reduce computing resource usage and operational costs. Running validations or tests in test environments before promoting changes to production. Supported deployment strategies Our plat ## Announcing Worker Versioning Public Preview: Pin Workflows to a single code version DevFeed: [Announcing Worker Versioning Public Preview: Pin Workflows to a single code version]() Original publisher: [Read original article]() Author: Drew Hoskins Published: 2025-09-24T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [version](), [Deployment Strategies](), [reliability](), [Code](), [Kubernetes]() Tags: [code](), [deployment-strategies](), [kubernetes](), [preview](), [product-news](), [reliability](), [version]() ### AI overview Temporal announces Worker Versioning in public preview, which pins each Workflow to a single code version. The article explains rolling, blue-green, and rainbow deployment strategies for managing Worker deployments and reducing version compatibility and routing risks. ### Source excerpt Announcing Worker Versioning in public preview -- pin each Workflow to a single code version for safer blue-green and rainbow deploys, with Kubernetes support. ## New GraphOS Platform APIs Enable Blue-Green and Canary Deployments DevFeed: [New GraphOS Platform APIs Enable Blue-Green and Canary Deployments]() Original publisher: [Read original article]() Author: Andrew I. Carlson Published: 2024-01-25T12:06:29Z Content type: release Language: en Sources: [Apollo Blog]() Topics: [GraphOS](), [Deployment Strategies](), [blue green and canary deployments](), [Deployment](), [API](), [CI/CD]() Tags: [announcement](), [api](), [blue-green-and-canary-deployments](), [ci-cd](), [deployment](), [deployment-strategies](), [graphos]() ### AI overview Apollo released enhanced GraphOS Platform APIs to support immutable blue-green and canary deployment workflows for supergraph schemas. The updates include mutations and queries for publishing subgraphs, polling launch status, and fetching supergraph SDL, while supporting consolidated metrics, insights, and launch history for deployment stages. ### Source excerpt As platforms, teams, and products evolve, we sometimes need new or different ways to deploy our APIs. Strong Continuous Integration / Continuous Deployment (CI/CD) practices and recovery strategies are necessary components of any Recovery Time Objective (RTO) and Recovery Point Objective (RPO). ## 5 SRE tips for Rails workloads in Kubernetes DevFeed: [5 SRE tips for Rails workloads in Kubernetes]() Original publisher: [Read original article]() Author: Doximity Published: 2022-12-02T13:00:00Z Content type: article Language: en Sources: [Doximity]() Topics: [Kubernetes](), [Rails](), [Deployment Strategies](), [Kafka](), [Sidekiq](), [site-reliability-engineering]() Tags: [bugs](), [deployment](), [deployment-strategies](), [kafka](), [kubernetes](), [performance](), [rails](), [sidekiq]() ### AI overview The article presents five changes made to Kubernetes clusters to better support Rails workloads and workflows. The supplied text focuses on deployment strategies: RollingUpdate is used for web traffic, while Recreate is used for Kafka and Sidekiq workloads because RollingUpdate caused rebalancing, performance, and compatibility issues during deployments. ### Source excerpt In a previous post we shared some suggestions for how to adjust a Rails application to make it run better in a containerized setting. In this post I'd like to explore the opposite relationship: five changes we have made to our Kubernetes clusters to better accommodate Rails workloads and workflows. All of these ideas can be relevant to non-Rails apps too! Selecting the right deployment strategies The two deployment strategies Kubernetes offers out of the box are RollingUpdate or Recreate. Initially we selected RollingUpdate for all of our workloads; with this strategy some new pods are created before old ones are terminated. The RollingUpdate strategy is ideal for our web traffic because if Kubernetes shuts down all of the existing pods before it starts up any new pods, users will see 5XX errors until the new web pods are healthy, not good. That said, the RollingUpdate strategy created issues with our Sidekiq and Kafka workloads so we switched those to use the Recreate strategy. Looking at Kafka first, consumers are basically just a group of processes working together to handle messages. Kafka consumers follow a rebalancing protocol when a new member wants to join a consumer group. During deployments we encountered bugs and performance issues while using RollingUpdate approach because new pods kept trickling-in and wanting to join the consumer group therefore triggering multiple rebalances. Switching our Kafka consumers to deploy using the Recreate strategy means all the existing Pods get killed before any new pods get created. This results in much more predictable deployment behavior, and now we see only a single rebalance with all new consumers joining the group at basically the same time. Next, looking at Sidekiq, it does not stop workers from processing jobs when a new worker process joins, so we didn't have quite as many issues with using the RollingUpdate approach. However, it did occasionally cause subtle bugs on some deployments. For example, if a team creat ## Temporal Transparency Update #8 DevFeed: [Temporal Transparency Update #8]() Original publisher: [Read original article]() Author: Ryland Goldstein Published: 2020-09-18T04:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Testing](), [Deployment Strategies](), [elasticsearch](), [Replication]() Tags: [announcements](), [deployment](), [strategies](), [testing](), [update]() ### AI overview Temporal's eighth transparency update reports that the V1 stabilization effort is code complete, required tests are running in the pipeline, some system components have been stabilized, and several features remain experimental for the V1 release. ### Source excerpt Hey Temporal community, it's Friday! It's been a long week but the end is almost here, but not before we share some exciting updates about Temporal. ## How to Rollback a Deployment in Kubernetes DevFeed: [How to Rollback a Deployment in Kubernetes]() Original publisher: [Read original article]() Author: Gergely Risko Published: 2019-10-17T00:00:00Z Content type: tutorial Language: en Sources: [Learnk8s]() Topics: [Kubernetes](), [Deployment](), [kubectl](), [Deployment Strategies](), [zero downtime deployment](), [Microservice]() Tags: [commands](), [deployment](), [deployment-strategies](), [how-to](), [kubernetes](), [production](), [rollback](), [rolling-update](), [zero-downtime-deployment]() ### AI overview This tutorial explains Kubernetes deployment rollbacks and the default rolling update strategy. It describes how Pods are replaced incrementally, how readiness checks support traffic transitions, and why a rollback plan is important when a production change breaks an application. ### Source excerpt Learn how to roll back breaking changes in Kubernetes. Master kubectl rollback commands and deployment strategies to quickly recover from production issues. ## Target open-sources Winnaker to audit Spinnaker deployments DevFeed: [Target open-sources Winnaker to audit Spinnaker deployments]() Original publisher: [Read original article]() Author: Target Brands, Inc Published: 2017-02-13T06:00:00Z Content type: release Language: en Sources: [Target]() Topics: [Cloud](), [Tool](), [Continuous Delivery (CD)](), [Automation](), [Open Source](), [Deployment Strategies](), [Monitoring](), [Kubernetes]() Tags: [api](), [audit](), [automation](), [cloud](), [deployment](), [deployment-strategies](), [health-checks](), [identity-management](), [infrastructure](), [kubernetes](), [monitoring](), [open-source](), [platform](), [spinnaker](), [winnaker]() ### AI overview Target announces Winnaker, an open-source tool for auditing Spinnaker deployments from an end-user perspective. The article explains how Winnaker automates whole-system deployment testing and helps identify connectivity, API rate-limit, and infrastructure-configuration problems. ### Source excerpt Win the cloud with Winnaker! I am happy to announce that we, at Target, decided to open source a tool called Winnaker. This tool will allow the user to audit Spinnaker from an end user point of view. But first what is Spinnaker? The first time I heard the word Spinnaker, my reaction was, "wait, what does that even mean in English?" Shortly after, I found myself implementing a demo of Spinnaker as a potential replacement for our internal cloud deployment tool. Spinnaker is a cloud agnostic continuous delivery tool, which means we can push our code to any cloud provider we like. In fact, Spinnaker takes agnosticism to the next level by introducing three abstractions. Load balancers Server groups Security groups By enforcing this level of simplicity, it allows the implementation of deployment strategies such as Highlander, Red/Black on a vast different type of infrastructure (VM, Container, Kubernetes, public cloud, private cloud) with a high level of confidence and an incredible level of ease of use for the app developers. Spinnaker also roots for the immutable infrastructure design pattern. Baking your image once and deploying the image everywhere is another bold move that differentiates Spinnaker from the other tools. Why Winnaker ? Short answer is because of automation! Test the functionality of the CD system as a whole. Spinnaker has different components (CloudDriver, Rosco, Deck,...). Each of these components have their own unit tests and health checks that can be monitored. We learned the hard way that relying only on component health checks is not effective enough to ensure developers won't face any error when they deploy their apps. A few things can go wrong when off monitoring radar: Connectivity between the separate components Maxing out cloud provider API rate limit Base infrastructure configurations (subnet address space, identity management roles) So we decided to audit Spinnaker and cloud's whole functionality with a sample app. If baking and deploying our