Apple TV+ has landed the screen rights to Cosmere, the sprawling literary universe created by Brandon Sanderson. "The first titles being eyed for adaptation are the Mistborn series, for features, and The Stormlight Archive series, for television," reports the Hollywood Reporter. From the report: The deal is rare one, coming after a competitive situation which saw Sanderson meet with most of the studio heads in town. It gives the author rarefied control over the screen translations, according to sources. Sanderson will be the architect of the universe; will write, produce and consult; and will have approvals. That's a level of involvement that not even J.K. Rowling or George R.R. Martin enjoys.
Sanderson's literary success and fan following helped pave the way for such a deal. One of the most prolific and beloved fantasy authors working today, he has sold over 50 million copies of his books worldwide, collectively across his series. [...] While the Cosmere books are set in various worlds and eras, the underlying premise concerns a being named Adolnasium who is killed by a group of conspirators. The being's power is broken into 16 shards, which are then spread out throughout many worlds by the conspirators, spreading many kinds of magic across the universe.
1155. This week, we look at whether it’s actually true that Inuit languages have hundreds of words for snow with Dr. Charles Kemp. We look at how researchers used a database of 18 million volumes to find out how our environment shapes our vocabulary using the Nida-Conklin principle. We also look at a surprising finding about words for rain being abundant in non-rainy regions.
Join us as we explore Swift beyond iOS with Sebastien Stormacq, AWS Developer Advocate and Swift specialist. Discover why Swift is becoming a compelling choice for server-side development, offering native compilation, memory safety without garbage collection, and modern concurrency features that deliver exceptional performance and cost efficiency.
Seb shares how Apple processes billions of daily requests using Swift on AWS infrastructure, achieving 40% better performance and 30% lower costs when migrating services from Java. We dive into the technical advantages that make Swift competitive with traditional backend languages, explore the vibrant server-side ecosystem with frameworks like Vapor and Hummingbird, and discuss practical implementations including serverless architectures on AWS Lambda.
Whether you're a Swift developer curious about server-side possibilities, a full-stack developer looking to unify your tech stack, or a backend engineer evaluating language options, this conversation offers practical insights into Swift's capabilities beyond the client.With Sebastien Stormacq, Principal Developer Advocate, AWS
🚀 Stop wasting time digging through code! In this practical walkthrough, SSW Solution Architect and Microsoft MVP Gordon Beeming reveals how he uses GitHub Copilot CLI, a powerful meta prompt, and Docker to generate detailed 30+ page investigation reports. Whether you're onboarding or deep-diving into legacy code, this workflow will help you ask better questions, extract meaningful insights, and produce high-quality documentation in minutes.
🔍 Gordon covers everything from setting up your Copilot environment, managing paths, and crafting structured prompts – to exporting clean PDF reports you can store in source control. Supercharge your productivity and let Copilot do the grunt work!
00:00 | Introduction 00:10 | What’s a Meta Prompt? 00:35 | Setting Up the Investigation 00:55 | Prompt Breakdown: Structure & Goals 01:18 | Targeting the Xero Integration 01:38 | Getting SQL Scripts via Prompting 02:00 | Clarifying Questions from Copilot 02:43 | Submitting Scope and Preferences 03:35 | Viewing the Auto-Generated Report 04:32 | Exporting to PDF in VS Code 05:12 | Reviewing and Iterating on Reports
Welcome to episode 339 of The Cloud Pod, where the forecast is always cloudy! Justin and Matt are in the studio today to bring you all the latest in cloud and AI announcements, including more personnel shifts (and it doesn’t seem like it was very friendly), a new way to get much needed copper, and Azure marketplace advertising 4,000 different models. What’s the real story? Let’s get into it and find out!
Titles we almost went with this week
US-EAST-1: Still the Least Reliable Friend You Keep Inviting to Parties **OpenAI
0⃣ From Zero to Inference: BigQuery Makes Open Models a Two-SQL Problem
AWS Goes Full Brandenburg Gate: Sovereign Cloud Opens for Business
Seven Ate Nine: AWS Skips G7 and Goes Straight to G7e Instances
From Crawling to Calling: Cloudflare Buys Human Native to Fix AI’s Data Problem
Finally, an AI That Actually Listens to Your War Room Panic
Tag, You’re Governed: AWS Automation Takes the Wheel
Cloudflare Reaches for the Stars: Astro Framework Acquisition Lands
Gemini Gets Personal: Google AI Finally Reads Your Email (With Permission)
AWS Strikes Ore: Amazon Cuts Out the Middleman in Copper Supply Chain
When Your Region Goes Down More Often Than Your Kubernetes Cluster
ChatGPT Go: OpenAI’s New Middle Child Gets $8 Allowance
Cloudflare acquires The Astro Technology Company, bringing the popular open-source web framework in-house while maintaining its MIT license and multi-cloud deployment capabilities.
Major platforms like Webflow Cloud, Wix Vibe, and Stainless already use Astro on Cloudflare infrastructure to power customer websites.
Astro 6 introduces a redesigned development server built on Vite Environments API that runs code locally using the same runtime as production deployment. When using the Cloudflare Vite plugin, developers can test against workerd runtime with access to Durable Objects, D1, KV, and other Cloudflare services during local development.
The framework focuses on content-driven websites through its Islands Architecture, which renders most pages as static HTML while allowing selective client-side interactivity using any UI framework.
This approach addresses the complexity that made building performant websites difficult before 2021, providing a simpler foundation for both human developers and AI coding agents.
Astro 6 adds stable Live Content Collections for real-time data updates without site rebuilds and includes first-class Content Security Policy support.
The acquisition positions Cloudflare to serve better platform builders who extend Cloudflare services to their own customers through Cloudflare for Platforms.
Tailwind recently laid off 80% of their staff, ostensibly due to AI, so this may have been an opportune moment for an exit.
04:15 Matt – “I would assume that they heavily use it (AI) internally, so hopefully it’s something that they can leverage and continue to grow and they don’t have to redevelop their platform.”
Cloudflare acquired Human Native, a UK-based AI data marketplace that transforms multimedia content into structured, searchable data for AI training.
The acquisition accelerates Cloudflare’s AI Index initiative, which uses a pub/sub model to let websites push structured content updates to AI developers in real time, rather than relying on traditional web crawling.
Human Native’s platform focuses on licensed, high-quality training data rather than scraped content, with one UK video AI company reportedly discarding its existing training data after achieving better results with Human Native’s curated datasets.
This approach addresses the growing problem of crawl-to-referral ratios reaching tens of thousands of bot crawls per human visitor.
The acquisition builds on Cloudflare’s existing AI Crawl Control and Pay Per Crawl products, giving content owners more control over how AI systems access their content.
Human Native’s technology will help customers structure their content for both AI consumption and traditional human audiences while enabling new monetization models.
Cloudflare is positioning this work alongside the x402 Foundation (partnered with Coinbase) to enable machine-to-machine transactions for digital resources.
The combination aims to create new economic models where AI developers can subscribe to structured content feeds and content creators receive fair compensation for their data.
05:30 Justin – “We block you from getting to people’s AI content, and now we offer you a way to buy better content. Well played.”
Anthropic is launching Labs as a dedicated team focused on incubating experimental AI products at the frontier of Claude’s capabilities, led by Instagram co-founder Mike Krieger and Ben Mann.
This organizational shift separates rapid experimentation from production scaling, with Ami Vora taking over as head of Product to focus on enterprise-grade Claude experiences.
The Labs approach has already produced several products that moved from research to production, including Claude Code, which reached $1 billion in revenue within six months of launch, and the Model Context Protocol, which now has 100 million monthly downloads and has become an industry standard for connecting AI systems to tools and data.
Recent Labs outputs include Skills, Claude in Chrome, and Cowork, which launched as a research preview to bring Claude’s agentic capabilities to desktop environments. This demonstrates the team’s focus on exploring new interaction models and deployment patterns for large language models beyond traditional chat interfaces.
The organizational structure creates two parallel tracks: Labs for frontier experimentation with unpolished versions and early user testing, and the core Product organization partnering with CTO Rahul Patil to scale proven experiences for millions of daily users and enterprise customers.
This separation aims to balance innovation velocity with reliability requirements.
Anthropic is actively hiring for Labs positions, specifically targeting builders with experience creating consumer products and working with emerging technologies.
The team structure reflects the company’s view that rapid AI advancement requires different organizational approaches than traditional product development cycles.
08:04 Matt – “The fact that you can get a lab to a GA customer product…is a really hard thing. They seem to have done a pretty good job of that with all these different technologies.”
Barret Zoph, who served as CTO, along with co-founder Luke Metz and researcher Sam Schoenholz, returned to OpenAI in what reports suggest was not an amicable departure.
The startup has now lost four key personnel in under a year, including co-founder Andrew Tulloch, who left for Meta in October.
Soumith Chintala has been promoted to replace Zoph as CTO, bringing over a decade of AI field experience to the role.
The rapid co-founder departures raise questions about Thinking Machines’ internal dynamics and strategic direction, particularly given the company secured backing from major investors, including Andreessen Horowitz, Accel, Nvidia, and AMD. The startup has not publicly disclosed what products or services it is developing despite the substantial funding.
This talent movement highlights the ongoing competition for AI research talent among major players, with OpenAI CEO of applications Fidji Simo noting the returns had been in the works for several weeks. The pattern mirrors OpenAI’s own history of co-founder departures to competing ventures, including John Schulman, who left for Anthropic before joining Thinking Machines.
12:35 Matt – “It’s interesting that they’re going back to OpenAI. I’m curious, with NDAs and all of that stuff in place, how that is going to work.”
OpenAI is adding 750MW of dedicated low-latency inference capacity through a partnership with Cerebras, with deployment rolling out in phases through 2028.
Cerebras uses a unique architecture with a single giant chip that combines compute, memory, and bandwidth to eliminate traditional bottlenecks in AI inference.
The partnership focuses specifically on accelerating real-time AI responses for workloads like complex queries, code generation, image creation, and AI agents.
OpenAI’s strategy is to match specialized hardware to specific workload types rather than using one-size-fits-all infrastructure.
Cerebras systems are purpose-built for fast token generation during the output phase of inference, which is critical for interactive AI applications where users expect immediate responses. This addresses the request-think-respond loop that determines user experience quality.
The integration represents OpenAI’s approach to building a diversified compute portfolio, adding specialized low-latency systems alongside their existing infrastructure.
This allows them to optimize different types of AI workloads based on performance requirements rather than using general-purpose hardware for everything.
14:29 Justin – “In general, anybody that can get you AI capacity is apparently a musto-do.”
The Go tier provides 10x more messages, file uploads, and image creation than the free tier, with access to GPT-5.2 Instant, plus longer memory and context windows for improved conversation continuity.
The pricing strategy positions Go as an entry-level paid option for users who need more capacity than the free tier but don’t require the advanced reasoning capabilities of GPT-5.2 Thinking (Plus) or GPT-5.2 Pro.
OpenAI reports that Go became their fastest-growing product after initial rollout to 170 countries, with strong adoption for writing, learning, image creation, and problem-solving tasks.
OpenAI plans to introduce advertising in both the free tier and ChatGPT Go in the US, while Plus, Pro, Business, and Enterprise tiers remain ad-free.
This ad-supported model aims to sustain free and low-cost access points, while generating revenue from users who don’t need premium features.
The tiered approach reflects a shift toward market segmentation similar to traditional SaaS models, with clear differentiation between casual users (Go), professionals (Plus), and power users (Pro). The $8 price point is localized in some markets, suggesting OpenAI is optimizing for purchasing power parity to maximize global adoption.
17:00 Matt – “Ads are coming to AI. We all knew it was coming; they have to find additional ways to monetize it.”
1Password has integrated with Cursor, the AI-powered IDE, to provide just-in-time secrets management through Cursor Hooks that validate and inject credentials at runtime without ever storing them on disk.
This eliminates the common security risk of developers hard-coding API keys or committing secrets to source control while working with AI coding assistants.
The integration works by running a Hook Script before Cursor’s AI agent executes shell commands, verifying that the required environment files from 1Password Environments are properly configured and prompting users to authorize access only when needed.
Secrets remain in memory for the runtime session only, and never touch disk or Git history, maintaining zero-trust principles while keeping development velocity high.
This addresses a critical gap in AI-assisted development where AI agents could potentially access unrestricted credentials, or developers might paste tokens directly into config files for convenience.
The solution lets project owners configure secrets management centrally while individual developers maintain control over authorization through 1Password’s existing access policies and vault permissions.
Plans include granular, task-specific access rules for AI agents, broader support for the Model Context Protocol in external API interactions, automated secret rotation for AI workflows, and enhanced audit visibility for security teams.
The goal is to make secure access a native part of AI-powered development rather than an afterthought bolted on later.
This matters because AI coding tools like Cursor are rapidly becoming standard in developer workflows, but most teams lack proper secrets management for these new AI-driven interactions.
The integration provides a practical path to adopt AI assistance without compromising security posture or requiring developers to change existing 1Password policies.
20:34 Justin – “The one thing they don’t mention, which I think is also a big threat, is you’re sending your context to their servers, and if you’re putting your password into the context, that password is now going to the inference systems, and that could potentially get exposed. So it would be nice if this also had the ability to prevent a secret from getting transmitted to the third party LLM.”
Harness launched the Human-Aware Change Agent, an AI system that listens to incident response conversations in Slack, Teams, and Zoom to extract operational clues like “the checkout button froze after they updated their cart” and automatically correlates them with actual production changes, including deployments, feature flags, and config updates.
This solves the problem where critical incident context lives in human conversations but never makes it into automated investigation tools.
The agent is part of Harness AI SRE, which includes an AI Scribe that filters incident-related conversation from noise and feeds it to the change investigation engine.
Instead of just transcribing chat or generating generic RCA summaries, it produces evidence-backed hypotheses like “deployment to checkout-service 12 minutes before the incident introduced new retry config, followed by latency spike and downstream timeouts.”
The system integrates with existing observability and incident management tools, including Datadog, PagerDuty, Jira, ServiceNow, Slack, and Teams through native integrations and webhooks.
It also includes Automation Runbooks for standardized response and On-Call management to route incidents to the right owners.
The core innovation is treating human insight as operational data rather than assuming incidents can be solved purely through logs, metrics, and traces. This addresses the reality that on-call engineers often identify patterns through conversation before they show up in dashboards, especially as AI-assisted development increases code velocity and reduces clear ownership of changes.
The tool aims to shorten the incident response cycle from “What are we seeing” to “What changed” to “What should we do” by connecting human observations with machine-driven change intelligence in real time during active incidents.
25:22 Justin – “Human awareness of how the system works as a whole – because typically AI systems don’t have the context to handle the whole system view – is also very valuable to the AI as well, so I guess we’re going to serving the AI someday, instead of the otherway around.”
AWS launches X8i instances with custom Intel Xeon 6 processors offering up to 6 TB of memory and 3.9 GHz sustained all-core turbo frequency, delivering 1.5x more memory capacity and 3.4x more memory bandwidth than previous X2i generation.
These SAP-certified instances target memory-intensive workloads like in-memory databases, data analytics, and EDA applications.
Performance improvements are substantial across multiple workloads: 50% higher SAP HANA performance, 47% faster PostgreSQL, 88% faster Memcached, and 46% faster AI inference compared to X2i instances. Real customer deployments show Orion reduced SQL Server licensing costs by 50% while maintaining performance thresholds by using fewer active cores.
The instances come in 14 sizes, including three new larger options (48xlarge, 64xlarge, 96xlarge) and two bare metal variants, with network bandwidth up to 100 Gbps supporting Elastic Fabric Adapter and 80 Gbps EBS throughput.
The instance bandwidth configuration feature allows flexible allocation between network and EBS bandwidth with up to 25% scaling capability.
Currently available in US East N. Virginia, US East Ohio, US West Oregon, and Europe Frankfurt regions with standard purchasing options including On-Demand, Savings Plans, and Spot Instances.
Pricing follows standard EC2 memory-optimized instance rates available on the EC2 pricing page.
These instances can handle models up to 70B parameters with FP8 precision on a single GPU, with configurations scaling up to 8 GPUs and 768GB total GPU memory per node.
The instances feature substantial networking improvements with 4x the bandwidth of G6e instances (up to 1,600 Gbps) and support for NVIDIA GPUDirect RDMA via Elastic Fabric Adapter for multi-node workloads.
GPUDirect P2P enables direct GPU-to-GPU communication over PCIe with 4x the inter-GPU bandwidth compared to previous generation L40s GPUs, reducing latency for distributed model inference.
G7e instances target generative AI inference, spatial computing, and scientific computing workloads with support for GPUDirect Storage integration with FSx for Lustre, providing up to 1.2 Tbps throughput for rapid model loading. Configurations range from single GPU instances to 8-GPU systems with up to 192 vCPUs and 2TB of system memory.
AWS European Sovereign Cloud is now generally available with its first region in Brandenburg, Germany, operating as a physically and logically separate infrastructure partition (aws-eusc) entirely within the EU.
The service launches with comprehensive AWS capabilities, including SageMaker, Bedrock, EC2, Lambda, EKS, Aurora, DynamoDB, S3, and other core services, backed by a 7.8 billion EUR investment expected to contribute 17.2 billion EUR to the European economy through 2040.
Expansion plans include sovereign Local Zones in Belgium, the Netherlands, and Portugal, plus options for Dedicated Local Zones, AI Factories, and Outposts deployments.
The operational model features EU-based management through German legal entities, with Stephane Israel appointed as managing director and an advisory board of EU citizens providing sovereignty oversight.
The infrastructure maintains AWS security standards, including Nitro System isolation, ISO/IEC 27001, SOC 1/2/3 reports, and BSI C5 attestation, with a Sovereign Reference Framework available in AWS Artifact.
Data residency guarantees ensure all customer content and metadata, including roles, permissions, and configurations, remain within the EU, using dedicated European trust service providers for certificate authority operations and European TLDs for Route 53 name servers.
Pricing is in EUR with billing available in eight supported currencies through Amazon Web Services EMEA SARL.
Major AWS partners, including Adobe, Cisco, SAP, Snowflake, and Wiz, are making their solutions available in the sovereign cloud, enabling public sector and highly regulated industry customers to meet strict compliance requirements while accessing modern cloud capabilities without being stuck in legacy on-premises environments.
31:53 Justin – “Google’s got the same thing on a partnership with Thales in France. I think Azure is doing something similar as well… but the question is kind of, a European entity owned by a US corporation, does that actually fulfill the concerns the European Union has?”
AWS becomes the first customer for Rio Tinto’s Nuton bioleaching technology, which uses microorganisms to extract copper from ore at the Johnson Camp mine in Arizona.
The process produces 99.99% pure copper cathode directly at the mine without traditional smelters or refineries, achieving a carbon footprint of 2.82 kgCO2e/kg Cu compared to the global range of 1.5-8.0 kgCO2e/kg Cu.
The two-year agreement supplies low-carbon copper for AWS data center components, including electrical cables, busbars, transformers, circuit boards, and processor heat sinks.
Johnson Camp is now the lowest-carbon primary copper producer in the U.S., targeting approximately 30,000 tonnes of refined copper over four years with 71 liters of water per kilogram versus the industry average of 130 liters.
AWS provides cloud-based data and analytics support to optimize Nuton’s bioleaching operations, including heap-leach performance simulation and advanced analytics for acid and water usage.
The modular system enables rapid scaling and customization for different ore bodies while recovering value from previously classified waste material.
This collaboration addresses supply chain resilience by producing critical materials domestically for U.S. data centers while supporting Amazon’s Climate Pledge goal of net-zero carbon by 2040.
The partnership demonstrates how industrial mining operations can integrate cloud technology to reduce environmental impact and shorten mine-to-market supply chains.
34:39 Justin – “It also tells me how much you desperately need it (copper) for all the AI investments you’re about to be making.”
Kiro CLI version 1.24.0 introduces Skills, a new resource type for progressive context loading that only loads metadata at startup and fetches full documentation content on demand when the AI agent needs it.
This addresses memory constraints when working with large documentation sets by requiring YAML frontmatter with descriptive metadata to help agents determine when to load complete content.
The release adds built-in code intelligence for 18 programming languages, including Python, JavaScript, Go, Rust, and others, without requiring LSP setup. Developers get immediate access to symbol search, definition navigation, and structural code searches, plus a new /code overview command for quick workspace analysis.
New AST-based pattern-search and pattern-rewrite tools enable precise code refactoring by matching syntax tree patterns instead of text regex. This eliminates false matches in string literals and comments, providing more reliable code transformations for AI agents.
Conversation Compaction addresses context window limitations with a /compact command that summarizes conversation history while preserving key information. The feature triggers automatically when context limits are reached and creates a new session while allowing users to resume the original conversation, with configurable retention settings for message pairs and context window percentage.
The update includes granular URL permissions for the web_fetch tool using regex patterns to control which domains AI agents can access, plus remote authentication support for Google and GitHub when running Kiro CLI on remote machines via SSH, SSM, or containers.
BigQuery now supports SQL-native inference for open models from Hugging Face and Vertex AI Model Garden through a two-step process: CREATE MODEL with a model ID string, then run inference using AI.GENERATE_TEXT or AI.GENERATE_EMBEDDING functions.
This eliminates the need for separate infrastructure management or API integrations outside of BigQuery.
The service includes automated resource management with configurable idle timeout settings that automatically undeploy endpoints when not in use, preventing runaway costs from idle GPU instances.
Users can customize machine types, replica counts, and leverage Compute Engine reservations for consistent GPU availability on demanding workloads.
This extends BigQuery’s existing managed inference capabilities beyond Google’s Gemini models and partner models like Anthropic and Mistral to any compatible open model.
The entire lifecycle from deployment to cleanup happens through SQL statements, making LLM inference accessible to data analysts without requiring ML engineering expertise.
The feature is currently in Preview and supports both text generation and embedding generation workloads directly on data stored in BigQuery tables.
Cost control includes both automated endpoint recycling based on idle time and manual undeploy options via ALTER MODEL statements, with automatic cleanup of all Vertex AI resources when models are dropped.
39:45 Matt – “This all seems crazy to me; this is where we’re at, where AI is writing, creating models, running all of these things for us.”
Google released TranslateGemma, a new family of open translation models based on Gemma 3, available in 4B, 12B, and 27B parameter sizes supporting 55 languages.
The models use a two-stage training process combining supervised fine-tuning on parallel data from human translations and Gemini-generated synthetic translations, followed by reinforcement learning using MetricX-QE and AutoMQM reward models.
The 12B TranslateGemma model outperforms the baseline Gemma 3 27B model on WMT24++ benchmarks while using less than half the parameters, delivering higher throughput and lower latency.
The 4B model matches the performance of the 12B baseline, making it suitable for mobile inference and edge deployment.
TranslateGemma retains Gemma 3’s multimodal capabilities, showing improved performance on the Vistra image translation benchmark without specific multimodal fine-tuning.
The models were trained on nearly 500 language pairs beyond the core 55, providing a foundation for researchers to fine-tune for specific language pairs or low-resource languages.
The models are optimized for different deployment scenarios: 4B for mobile and edge devices, 12B for consumer laptops, and 27B for a single H100 GPU or TPU cloud deployment.
All three sizes are available now for developers and researchers to download and use.
41:50 Justin – “I am excited about the idea of models that specialize in supporting language translations; and so this is things that power future products inside of your Android phones someday, where Apple has a feature where it can slowly translate things through your Airpods… it’s a little delayed but it works relatively well. I’m sure this will bring similar type capabilities to you and your Android phone.”
Microsoft positions its Marketplace as a central hub for AI adoption with over 11,000 pre-packaged models (“models”) and 4,000 AI apps and agents, offering organizations flexible build-buy-blend strategies for implementing AI solutions.
The platform integrates directly into existing Microsoft tools like Copilot Studio and Azure Foundry, allowing teams to discover and deploy AI components within their normal workflows rather than switching between separate procurement systems.
The Marketplace supports both pro-code development with full control over custom logic and IP ownership, and low-code approaches through Copilot Studio using models from providers like Anthropic, OpenAI, Meta, and NVIDIA.
Organizations with Azure consumption commitments can apply Marketplace purchases dollar-for-dollar against their contracts with no limit, potentially improving ROI on existing Microsoft agreements.
Microsoft emphasizes a blended approach where companies can extend partner solutions with proprietary components, illustrated by financial services firms deploying pre-built fraud detection models while customizing them with internal data pipelines and compliance workflows.
This strategy reduces the engineering effort and compliance review cycles compared to building detection systems from scratch while maintaining data security through Managed Identity within Azure tenants.
The platform includes try-before-you-buy capabilities with trials and proofs-of-concept that run within customer Microsoft environments, allowing validation before full deployment.
Solutions are filtered by product, category, and industry to match specific organizational needs, with agents available directly in Microsoft 365 Copilot and models accessible through the Azure portal.
StatusGator published an analysis of AWS outages from January through December 2025, focusing on regional reliability and service-level incidents across all commercial AWS regions
N. Virginia (us-east-1) is the least reliable AWS region: 10 outages, 34 hours of downtime, 126 components affected
October 20, 2025, was one of AWS’s most significant outages ever: 76 components down for ~15 hours, cascading failures across thousands of SaaS platforms
Compute and ML services hit hardest: EC2 (14 outages), SageMaker (11), Glue (10), EMR (10), ECS (10)
Multi-region (“Regionless”) outages increased: 12 incidents, 32 hours of downtime
Status Gator speculates reasons:
Customer density: us-east-1 has 2x the users of Oregon and 3x other regions
Higher service density creates more interconnected dependencies and potential failure points
Heavier API traffic and more complex multi-AZ coordination
No evidence that the age of the region or architectural differences are factors
Best Practices from Status Gator
Avoid over-reliance on a single region, especially us-east-1
Design for multi-region resilience and failover
Monitor authentication/identity services (STS) as critical dependencies
Consider the blast radius when selecting primary regions
Closing
And that is the week in the cloud! Visit our website, the home of the Cloud Pod, where you can join our newsletter, Slack team, send feedback, or ask questions at theCloudPod.net or tweet at us with the hashtag #theCloudPod
Tom Gerencer and Rajan Khanna join us to discuss Roger Zelazny’s classic novel Roadmarks, about a magical highway that runs through time and space. Ad-free episodes are available to our paid supporters over at patreon.com/geeks.
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