# IaaS Published articles for IaaS. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Cloud Service Models Explained: IaaS vs PaaS vs SaaS DevFeed: [Cloud Service Models Explained: IaaS vs PaaS vs SaaS]() Original publisher: [Read original article]() Author: Chinwendu Enyinna Published: 2026-10-29T15:39:58Z Content type: article Language: en Sources: [Free Code Camp]() Topics: [The Three Main Cloud Service Models](), [Comparing SaaS, PaaS, and IaaS](), [Use Cases for Every Cloud Service Model](), [Shared Responsibility Model](), [Infrastructure as a Service (IaaS)](), [Platform as a Service (PaaS)](), [Software as a service](), [function as a service](), [Serverless]() Tags: [amazon](), [amazon-dynamodb](), [amazon-ec2](), [amazon-eks](), [amazon-rds](), [amazon-s3](), [amazon-vpc](), [amazon-web-services](), [amazon-web-services-aws](), [cloud](), [cloud-computing](), [cloud-engineering](), [cloud-services](), [gmail](), [iaas](), [it](), [models](), [net](), [saas](), [servers](), [slack](), [zoom]() ### AI overview The models differ in how much of the computing stack the provider manages. With IaaS, you rent virtual servers, storage, and networking but handle the operating system, software, updates, and scaling setup. PaaS manages servers, operating systems, and runtimes so you can deploy code and data, with less control and possible platform limits or lock-in. SaaS provides a finished application; you configure users, access, and data while the provider operates and updates it. The article also describes CaaS for managed containers and FaaS for event-triggered functions billed by use; FaaS has execution limits and can have cold starts. Serverless refers to operating and billing characteristics, not a separate stack model. It recommends starting with the most managed option that meets the project's needs, while noting customers remain responsible for their data and access. ### Source excerpt You likely use cloud services every day. When you check Gmail, join a Zoom meeting, or message your team on Slack, you use cloud-hosted software. But you don't manage the servers it runs on or maintai ## Neoclouds and the enterprises that need them DevFeed: [Neoclouds and the enterprises that need them]() Original publisher: [Read original article]() Author: David Linthicum Published: 2026-10-09T09:00:00Z Content type: opinion Language: en Sources: [InfoWorld]() Topics: [AI Infrastructure](), [Infrastructure as a Service (IaaS)](), [Submodules]() Tags: [ai](), [ai-training](), [ai-training-and-inference](), [ai-workloads](), [artificial-intelligence](), [artificial-intelligence-cloud-architecture-cloud-computing-iaas](), [cloud](), [cloud-architecture](), [cloud-computing](), [cloud-platform](), [clusters](), [compute](), [cooling](), [coreweave](), [crusoe](), [data](), [data-center](), [data-centers](), [effect](), [efficiently](), [enterprise](), [enterprise-architecture](), [enterprises](), [iaas](), [language-en](), [neoclouds](), [storytype-opinion]() ### AI overview The article argues that neocloud providers offering GPU compute, high-bandwidth memory, and fast interconnects for AI workloads have a place between hyperscalers and managed service providers. It says unclear positioning and weak enterprise sales messaging are limiting adoption, and recommends stronger solutions architecture support and clearer explanations of how neoclouds fit into enterprise architectures. ### Source excerpt I've been tracking the rise of neoclouds in the past couple of years, and I'll start by admitting that the numbers are genuinely staggering. Synergy Research Group reports that neocloud revenues hit $9 billion in the fourth quarter of 2025 alone, up 223% year over year, exceeding $25 billion for the full year. The market is forecast to approach $400 billion by 2031 at a sustained 58% compound annual growth rate. ABI Research puts GPU-as-a-service revenue from neocloud providers on a path to surpass $250 billion by 2030, up from $42 billion in 2025, and forecasts more than 2,200 neocloud-operated data centers in operation globally by 2035, up from just 558 in 2025. CoreWeave alone has eclipsed $5 billion in annual revenue faster than any cloud platform in history. These numbers describe the fastest-scaling category of infrastructure the industry has ever seen. So what is a neocloud, exactly? In short, it is a specialized cloud that focuses entirely on serving up AI-based systems. Instead of the thousands of services a hyperscaler offers, a neocloud delivers GPU compute, high-bandwidth memory, and fast interconnect purpose-built for AI training and inference workloads. Providers such as CoreWeave, Lambda, Nebius, and Crusoe deploy dense GPU clusters with liquid cooling and high-speed networking that can be brought online in months rather than the three to five years a traditional hyperscale data center build requires. Neoclouds are, in effect, an architectural response to the fact that AI workloads impose rigid constraints around parallelism, locality, and compute concentration that generalized cloud elasticity was never designed to serve. I see a clear place for them in the modern enterprise architecture life cycle, sitting between public cloud hyperscalers and managed service providers (MSPs), a purpose-built layer for the specific AI workloads that neither hyperscalers nor MSPs serve efficiently. There is nothing about that role I disagree with. The value propositi ## AWS cloud region damage in Bahrain and the UAE exposes disaster recovery limits DevFeed: [AWS cloud region damage in Bahrain and the UAE exposes disaster recovery limits]() Original publisher: [Read original article]() Author: David Linthicum Published: 2026-09-29T09:00:00Z Content type: opinion Language: en Sources: [InfoWorld]() Topics: [Disaster Recovery](), [Amazon Web Services](), [Cloud](), [cloud-infrastructure]() Tags: [amazon-web-services](), [availability](), [business-continuity](), [business-continuity-cloud-computing-iaas-saas-security](), [cloud](), [cloud-computing](), [iaas](), [infrastructure](), [iran](), [israel](), [middle-east](), [military](), [saas](), [security](), [uae](), [us](), [war]() ### AI overview Physical damage from strikes left AWS unable to restore access to its Bahrain region and a UAE availability zone, with restoration updates expected months later or in early 2027. The article argues that the incident challenges assumptions about independent availability zones and highlights the risk to data and workloads that were not migrated elsewhere. ### Source excerpt I've covered cloud computing for a long time, and one of the most persistent myths I encounter is that public clouds exist above the laws of physics that govern everything else in technology. The events coming out of the Middle East should put that myth to rest for good. Reuters reported this week that Amazon Web Services says it cannot restore access to its cloud region in Bahrain and to one of its three data-hosting zones in the United Arab Emirates, following damage during the Iran war. The status update is stark reading. AWS admitted that the damage in Bahrain spanned multiple availability zones (AZs) and, in its own words, "exceeded what our regional and multi-AZ services are designed to withstand." Think about that for a moment. The entire architectural premise of availability zones is that they fail independently, so a multi-AZ deployment survives a localized disaster. The war destroyed that assumption in a single stroke. The disruptions began in March, after US and Israeli attacks on Iran prompted Tehran to retaliate with missile and drone strikes on Israel and on Gulf states hosting US military bases. Two of AWS's facilities in the UAE were directly struck, and a drone strike close to a Bahrain facility physically damaged infrastructure there. A second Bahrain availability zone went down in April, and the region became effectively unavailable. In the UAE, AWS says it cannot restore resources and data hosted exclusively in the zone known as mec1-az2. The company has notified authorities in both countries and is helping customers re-establish operations in other regions. AWS expects to provide a UAE restoration update in the coming months, with a Bahrain update not until early 2027. Read that timeline again: Operations interrupted in March may not be resolved until early next year. That's a year of impact for data and workloads that weren't migrated in time. Some of that data, I suspect, will never come back. I bring this up not to pile on AWS. Frankly, no cl ## 7 decisions that make an Azure landing zone enterprise-ready DevFeed: [7 decisions that make an Azure landing zone enterprise-ready]() Original publisher: [Read original article]() Author: Sachin Suryawanshi Published: 2026-09-22T09:00:00Z Content type: article Language: en Sources: [InfoWorld]() Topics: [Azure](), [cloud-infrastructure](), [Cloud](), [Security](), [Network](), [Monitoring](), [observability](), [Deployment](), [CI/CD](), [Security Information and Event Management (SIEM)](), [GitHub]() Tags: [architecture](), [azure](), [ci-cd](), [cloud](), [cloud-computing](), [cloud-computing-cloud-security-enterprise-architecture-iaas-microsoft-azure-security](), [cloud-security](), [configuration](), [contributor](), [deployment](), [engineering](), [enterprise](), [enterprise-architecture](), [firewalls](), [github](), [iaas](), [microsoft-azure](), [monitoring](), [network](), [observability](), [security](), [security-operations]() ### AI overview An engineering practitioner explains seven decisions for making an Azure landing zone enterprise-ready. The article treats the landing zone as an operating model covering governance, security, networking, observability, deployment workflows, and application-team autonomy. ### Source excerpt As a designer of enterprise-scale Azure landing zones, I've found that drawing a landing zone is fairly easy. However, building one that engineering teams can use effectively is far more challenging. When I started designing an enterprise Azure landing zone, the building blocks were familiar: management groups, subscriptions, virtual networks, policies, firewalls, monitoring and CI/CD. The difficult part was determining how they would interact without producing a platform that appeared secure on paper yet proved difficult to maintain. Microsoft's Cloud Adoption Framework provided an excellent starting point. However, a reference architecture can only go so far. Real-world environments have security standards, compliance requirements, deployment pipelines and application teams that need enough autonomy to build software without working around governance. My design incorporated two Azure regions in an active-active configuration. Production traffic was routed across both regions via Azure Front Door, with health probes used to identify unhealthy origins and remove them from rotation when necessary. I selected Azure Virtual WAN over a traditional hub-and-spoke network, integrated Palo Alto Networks Cloud NGFW into the networking design, used Datadog for observability and used a dedicated cloud SIEM for security operations. Lastly, I integrated GitHub larger runners with Azure VNets so deployment workflows could access private resources without exposing them publicly. The choices I made were not simply about enabling Azure services. They were about where control should reside, where teams require flexibility and how to make the secure option the easiest option. 1. Consider your landing zone as an operating model, not simply a network I wanted to prevent the landing-zone project from becoming solely a networking exercise. While networking is essential, a landing zone should also address other questions. Who can create resources? Where should workloads reside? How do poli ## AWS bets that AI agents need an inbox, not another chat window DevFeed: [AWS bets that AI agents need an inbox, not another chat window]() Original publisher: [Read original article]() Author: Anirban Ghoshal Published: 2026-09-16T16:07:53Z Content type: news Language: en Sources: [Computerworld]() Topics: [Artificial Intelligence](), [App](), [LangChain](), [Langgraph](), [Model Context Protocol](), [Self-hosted](), [Ollama](), [Amazon Bedrock]() Tags: [agents](), [ai](), [ai-agents](), [amazon-bedrock](), [amazon-web-services](), [amazon-web-services-artificial-intelligence-cloud-computing-iaas-productivity-software](), [anthropic](), [artificial-intelligence](), [aws](), [cloud-computing](), [iaas](), [langchain](), [langgraph](), [mcp](), [ollama](), [openai](), [productivity-software]() ### AI overview AWS has open-sourced Pizza Bot, a self-hosted application that uses an inbox-style interface to manage work delegated to background AI agents. It organizes ongoing, completed, and paused tasks, while its architecture uses LangChain Deep Agents and LangGraph to checkpoint state and support resumable work. The application connects agents with tools, MCP servers, and multiple model providers, including local models through Ollama. ### Source excerpt AWS is betting that AI agents need a different interface as they move beyond answering prompts and start working autonomously in the background. The company has open-sourced Pizza Bot, a self-hosted application that gives users an inbox for managing work delegated to AI agents, with separate threads for ongoing tasks and a queue for work that is completed or needs human input, rather than keeping the management of agents restricted inside a conventional chat window. The rationale, according to AWS, is that background agents do not always need a user's attention while they work, and an inbox model will let users hand off longer-running tasks, return to them later, and see which jobs are complete or require intervention. Under the hood That approach is reflected in how the inbox organizes work with the help of an "All" tab that contains the history of each task or conversation, including the agent's messages and work performed, the "Unread" tab that flags completed work that users have yet to review, and an "Action" tab that surfaces tasks paused while waiting for user input or approval. The inbox interface also has a panel named Activity that shows users how an agent handled a particular task along with the transcript, AWS wrote in a blog post introducing Pizza Bot. AWS' inbox-oriented rationale also extends to Pizza Bot's architecture. It uses LangChain's Deep Agents as the harness and LangGraph as the stateful runtime, with a combination of the two allowing an agent to checkpoint its progress as it works, preserving its messages, tool activity and current state so a task can be paused and resumed rather than being tied to a live chat session, the hyperscaler wrote. Pizza Bot's server sits on top of that stack, connecting the agent runtime with the user interface, skills, MCP servers and the model provider, with developers able to choose from Anthropic, OpenAI, Google Gemini and Amazon Bedrock, as well as local models through Ollama, it added. As for its out-of-the- ## AWS bets that AI agents need an inbox, not another chat window DevFeed: [AWS bets that AI agents need an inbox, not another chat window]() Original publisher: [Read original article]() Author: Anirban Ghoshal Published: 2026-09-16T16:04:24Z Content type: news Language: en Sources: [InfoWorld]() Topics: [Artificial Intelligence](), [App](), [LangChain](), [Langgraph](), [Model Context Protocol](), [Ollama](), [User Interfaces](), [Stateful](), [Self-hosted]() Tags: [agents](), [ai](), [ai-agents](), [amazon-bedrock](), [amazon-web-services](), [amazon-web-services-artificial-intelligence-cloud-computing-iaas-open-source-software-developme](), [artificial-intelligence](), [cloud-computing](), [developers](), [iaas](), [langchain](), [langgraph](), [mcp](), [ollama](), [open-source](), [openai](), [self-hosted](), [software-development](), [stateful](), [user-interface]() ### AI overview AWS has open-sourced Pizza Bot, a self-hosted application that gives users an inbox for managing work delegated to background AI agents. The architecture uses LangChain Deep Agents and LangGraph to preserve task state, while supporting multiple model providers, local models, Agent Skills, and MCP servers. ### Source excerpt AWS is betting that AI agents need a different interface as they move beyond answering prompts and start working autonomously in the background. The company has open-sourced Pizza Bot, a self-hosted application that gives users an inbox for managing work delegated to AI agents, with separate threads for ongoing tasks and a queue for work that is completed or needs human input, rather than keeping the management of agents restricted inside a conventional chat window. The rationale, according to AWS, is that background agents do not always need a user's attention while they work and an inbox model will let users hand off longer-running tasks, return to them later, and see which jobs are complete or require intervention. Under the hood That approach is reflected in how the inbox organizes work with the help of an "All" tab that contains the history of each task or conversation, including the agent's messages and work performed, the "Unread" tab that flags completed work that users have yet to review, and an "Action" tab that surfaces tasks paused while waiting for user input or approval. The inbox interface also has a panel named Activity that shows users how an agent handled a particular task along with the transcript, AWS wrote in a blog post introducing Pizza Bot. AWS' inbox-oriented rationale also extends to Pizza Bot's architecture. It uses LangChain's Deep Agents as the harness and LangGraph as the stateful runtime, with a combination of the two allowing an agent to checkpoint its progress as it works, preserving its messages, tool activity and current state so a task can be paused and resumed rather than being tied to a live chat session, the hyperscaler wrote. Pizza Bot's server sits on top of that stack, connecting the agent runtime with the user interface, skills, MCP servers and the model provider, with developers able to choose from Anthropic, OpenAI, Google Gemini and Amazon Bedrock, as well as local models through Ollama, it added. As for its out-of-the-b ## The complicated AI infrastructure market DevFeed: [The complicated AI infrastructure market]() Original publisher: [Read original article]() Author: David Linthicum Published: 2026-09-15T09:00:00Z Content type: article Language: en Sources: [InfoWorld]() Topics: [AI Infrastructure](), [cloud-infrastructure](), [data centers](), [GPU](), [Orchestration](), [Kubernetes](), [Computing]() Tags: [ai-infrastructure](), [architecture](), [artificial-intelligence](), [artificial-intelligence-cloud-computing-critical-infrastructure-data-and-information-security-ia](), [cloud-computing](), [compute](), [critical-infrastructure](), [data-and-information-security](), [data-centers](), [gpu](), [iaas](), [infrastructure](), [orchestration](), [security](), [technology]() ### AI overview The article examines the emerging neocloud market, describing providers built specifically for AI workloads and distinguishing them from general-purpose hyperscalers. It focuses on the infrastructure required for AI, including GPU clusters, power, cooling, networking, orchestration, and inference services. ### Source excerpt There is a quiet war happening underneath the AI boom, and most people are not paying attention to the right front. While headlines focus on which model is winning, which startup is raising money, or which chatbot is gaining users, a parallel race is playing out in data centers and corporate boardrooms across the world. The race is for AI infrastructure: the GPU clusters, power contracts, cooling systems, networking fabric, orchestration tools, and inference services that make modern AI actually work. The companies fighting in that race are called neoclouds. Understanding the importance of neoclouds matters for the same reason understanding cloud computing mattered in 2008. The companies that first saw the shift to cloud and positioned themselves around it correctly ended up controlling a massive layer of the technology industry. The same dynamics are playing out in AI infrastructure right now. But the neocloud market is more complicated than it looks. Not all of these companies do the same thing. Grouping them together as if they are all just GPU rental businesses misses the point entirely. The more useful way to understand the space is to recognize that neoclouds are evolving into four distinct categories, each with a different focus, a different customer, and a different role in the AI stack. What a neocloud actually is Before getting into categories, it helps to establish what the term means. A neocloud is a cloud infrastructure provider designed specifically for AI workloads. That sounds simple, but it covers a lot of ground. General-purpose hyperscalers like AWS, Azure, and Google Cloud were engineered to support a wide variety of workloads across many industries. They built architecture around flexibility, breadth of services, and broad enterprise appeal. Neoclouds took a different approach. They built around one mandate: make AI compute as fast, dense, and scalable as possible. That means designing facilities around high power density, integrating fast inter ## 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. ## PaaS + IaaS: Heroku and AWS for Cloud-Native Enterprise Agility DevFeed: [PaaS + IaaS: Heroku and AWS for Cloud-Native Enterprise Agility]() Original publisher: [Read original article]() Author: Gillian Gual Hinkle Published: 2025-12-04T19:50:28Z Content type: article Language: en Sources: [Heroku]() Topics: [Comparing SaaS, PaaS, and IaaS](), [Platform as a Service (PaaS)](), [Amazon Web Services](), [Heroku](), [software-development]() Tags: [agentforce](), [agents](), [ai](), [applink](), [apps](), [automated](), [aws](), [cloud](), [cloud-infrastructure](), [cloud-native](), [cloud-native-applications](), [development](), [engineering](), [enterprise](), [enterprise-infrastructure](), [enterprises](), [heroku](), [heroku-enterprise](), [iaas](), [infrastructure](), [infrastructure-as-a-service-iaas](), [services](), [velocity]() ### AI overview The excerpt presents Heroku PaaS as a way for enterprise teams to use AWS infrastructure while reducing the operational work of building and running applications. AWS offers a broad set of services and enterprise scale, but requires effort and expertise; Heroku handles tasks such as OS patching, security updates, and routine maintenance so developers can focus on application code. At Salesforce, the DET team uses Heroku to build, deploy, and maintain five applications serving more than 76,000 employees. The excerpt says these secure shared services are accessible to people through Slack and AI agents through Agentforce, with Heroku AppLink controlling authorized data access and token management. It frames the choice as using AWS effectively through a managed platform, not avoiding AWS. ### Source excerpt For modern enterprises building cloud-native applications, success hinges on achieving maximum development velocity at every scale. Infrastructure as a Service (IaaS) providers like AWS provide hundreds of services with the unmatched reliability and scale needed for enterprise infrastructure, but they can require significant effort and expertise for organizations to be effective and efficient. To achieve [...] The post PaaS + IaaS: Heroku and AWS for Cloud-Native Enterprise Agility appeared first on Heroku. ## Critical infrastructure and latency in gaming DevFeed: [Critical infrastructure and latency in gaming]() Original publisher: [Read original article]() Author: Kealan Parr Published: 2022-07-19T00:00:00Z Content type: article Language: en Sources: [FusionAuth]() Topics: [Critical Infrastructure](), [Latency](), [Self-hosted](), [Infrastructure as a Service (IaaS)](), [Availability](), [Software as a service](), [Scalability](), [Network](), [Server](), [Authentication]() Tags: [availability](), [critical-infrastructure](), [education](), [iaas](), [infrastructure](), [latency](), [network](), [product](), [saas](), [scalability](), [self-hosting](), [servers]() ### AI overview The article explains how hosting choices for critical gaming infrastructure affect network latency and gameplay. It compares self-hosting with third-party IaaS and SaaS providers and discusses infrastructure components such as game-logic, multiplayer data, authentication, and download servers, along with availability, disaster recovery, and scalability. ### Source excerpt What is critical infrastructure and how does it affect gaming latency?