325: Db2 or Not Db2: That Is the Backup Question

Welcome to episode 325 of The Cloud Pod, where the forecast is always cloudy! Justin is on vacation this week, so it’s up to Ryan and Matthew to bring you all the latest news in cloud and AI, and they definitely deliver! This week we have an AWS invoice undo button, Sora 2, and quite a bit of news DigitalOcean – plus so much more. Let’s get started! Titles we almost went with this week AWS Shoots for the Cloud with NBA Partnership Nothing But Net: AWS Scores Big with Basketball AI Deal From Courtside to Cloud-side: AWS Dunks on Sports Analytics PostgreSQL Gets a Gemini Twin for Natural Language Queries Fuzzy Logic: When Your Database Finally Speaks Your Language CLI and Let AI: Google’s Natural Language Database Assistant Satya’s Org Chart Shuffle: Now with More AI Synergy Microsoft Reorgs Again: This Time It’s Personal (and Commercial) Ctrl+Alt+Delete: Microsoft Reboots Its Sales Machine Sora 2: The Sequel Nobody Asked For But Everyone Will Use OpenAI Puts the “You” in YouTube (AI Edition) Sam Altman Stars in His Own AI-Generated Reality Show Grok and Roll: Microsoft’s New AI Model Rocks Azure To Grok or Not to Grok: That is the Question Grok Around the Clock: Azure’s 24/7 Reasoning Machine Spark Joy: Google Lights Up ML Inference for Data Pipelines DigitalOcean’s Storage Trinity: Hot, Cold, and Backed Up NFS: Not For Suckers (Network File Storage) The Goldilocks Storage Strategy: Not Too Hot, Not Too Cold, Just Right NAT Gonna Cost You: DigitalOcean’s Gateway to Savings BYOIP: Bring Your Own IP (But Leave Your Billing Worries Behind) The Great Invoice Escape: No More Support Tickets Required Ctrl+Z for Your AWS Bills: The Undo Button Finance Teams Needed Image Builder Finally Learns When to Stop Trying Pipeline Dreams: Now With Built-in Reality Checks EC2 Image Builder Gets a Failure Intervention Feature MCP: Model Context Protocol or Marvel Cinematic Protocol? AI is Going Great – Or How ML Makes Money 00:45 OpenAI’s Sora 2 lets users insert themselves into AI videos with sound – Ars Technica OpenAI’s Sora 2 introduces synchronized audio generation alongside video synthesis, matching Google’s Veo 3 and Alibaba’s Wan 2.5 capabilities. This positions OpenAI competitively in the multimodal AI space with what they call their “GPT-3.5 moment for video.” The new iOS social app feature allows users to insert themselves into AI-generated videos through “cameos,” suggesting potential applications for personalized content creation and social media integration at scale. Sora 2 demonstrates improved physical accuracy and consistency across multiple shots, addressing previous limitations where objects would teleport or deform unrealistically. The model can now simulate complex movements like gymnastics routines while maintaining proper physics. The addition of “sophisticated background soundscapes, speech, and sound effects” expands potential enterprise use cases for automated video production, training materials, and marketing content generation without separate audio post-processing. This development signals increasing competition in the video synthesis market, with major cloud providers likely to integrate similar capabilities into their AI services portfolios to meet growing demand for automated content creation tools. 02:04 Matt – “So, before, when you could sort of trust social media videos, now you can’t anymore.” 03:25 Jules introduces new tools and API for developers Google’s Jules AI coding agent now offers command-line access through Jules Tools and an API for direct integration into developer workflows, moving beyond its original chat interface to enable programmatic task automation. The Jules API allows developers to trigger coding tasks from external systems like Slack bug reports or CI/CD pipelines, enabling automated code generation, bug fixes, and test writing as part of existing development processes. Recent updates include file-specific context selection, persistent memory for user preferences, and structured environment variable management, addressing reliability issues that previously limited production use. This positions Jules as a workflow automation tool rather than just a coding assistant, competing with GitHub Copilot and Amazon CodeWhisperer by focusing on asynchronous task execution rather than inline code completion. The shift to API-based access enables enterprises to integrate AI coding assistance into their existing toolchains without requiring developers to switch contexts or adopt new interfaces. 04:41 Matt – “We’re just adding to the tools; then we need to figure out which one is gong to be actually useful for you.” 05:17 OpenAI Doubles Down on Chip Diversity With AMD, Nvidia Deals –Business Insider OpenAI signed a multi-year deal with AMD for chips requiring up to 6 gigawatts of power, plus an option to acquire tens of billions in AMD stock, diversifying beyond its heavy reliance on Nvidia GPUs accessed through Microsoft Azure . The AMD partnership joins recent deals including 10 gigawatts of Nvidia GPUs with $100 billion investment, a Broadcom partnership for custom AI chips in 2025, and a $300 billion Oracle compute deal, signaling OpenAI’s strategy to secure diverse hardware supply chains. This diversification could benefit the broader AI ecosystem by increasing competition in the AI chip market, potentially lowering prices and reducing supply chain vulnerabilities from geopolitical risks or natural disasters. AMD expects tens of billions in revenue from the deal, marking a significant validation of their AI technology in a market where Nvidia holds dominant market share, while OpenAI gains negotiating leverage and supply redundancy. These massive infrastructure investments serve as demand signals for continued AI growth, though they concentrate risk on OpenAI’s success – if OpenAI fails to grow as projected, it could impact multiple chip manufacturers and the broader AI infrastructure buildout. 06:51 Ryan – “I’m stuck on this article sort of gigawatts of power as a unit of measurement for GPU. Like, that’s hilarious to me. we’re just, there’s not this many, not this many GPUs, but like this much in power of GPUs.” AWS 07:55 AWS to Become the Official Cloud and Cloud AI Partner of the NBA, WNBA, NBA G League, Basketball Africa League and NBA Take-Two Media AWS becomes the official cloud and AI partner for NBA, WNBA, and affiliated leagues, launching “NBA Inside the Game powered by AWS” – a new basketball intelligence platform that processes billions of data points using Amazon Bedrock and SageMaker to deliver real-time analytics and insights during live games. The platform introduces AI-powered advanced statistics that analyze 29 data points per player using machine learning to generate previously unmeasurable performance metrics, with initial stats rolling out during the 2025-26 season accessible via NBA App, NBA.com, and Prime Video broadcasts. Play Finder” technology uses AI to analyze player movements across thousands of games, enabling instant search and retrieval of similar plays for broadcasters and eventually allowing teams direct access to ML models for coaching and front office workflows. The NBA App, NBA.com, and NBA League Pass will run entirely on AWS infrastructure, supporting global fan engagement with personalized, in-language content delivery while complementing Amazon’s 11-year media rights agreement for 66 regular-season games on Prime Video. This partnership demonstrates AWS’s expanding role in sports analytics beyond traditional cloud infrastructure, showcasing how AI services like Bedrock and SageMaker can transform real-time data processing for consumer-facing applications at massive scale. 10:51 Ryan – “I do like the AI analytics for sports, like AWS is already in the NFL and F! Racings and it’s sort of a neat add-on when they integrate it.” 12:45 AWS Introduces self-service invoice correction feature AWS launches self-service invoice correction feature allowing customers to instantly update purchase order numbers, business legal names, and addresses on their invoices through the Billing and Cost Management console without contacting support. This addresses a common pain point for enterprise customers who need accurate invoices for accounting compliance and reduces manual support ticket volume for AWS teams. The guided workflow in the console lets customers update both their account settings and select existing invoices, providing immediate corrected versions for download. Available in all AWS regions except GovCloud and China regions, making it accessible to most commercial AWS customers globally. Particularly valuable for organizations with strict procurement processes or those who’ve undergone mergers, acquisitions, or address changes that require invoice updates for proper expense tracking. 17:53 EC2 Image Builder now provides enhanced capabilities for managingimage pipelines EC2 Image Builder now automatically disables pipelines after consecutive failures, preventing unnecessary resource creation and reducing costs from repeatedly failed builds – a practical solution for teams dealing with flaky build processes. The new custom log group configuration allows teams to set specific retention periods and encryption settings for pipeline logs, addressing compliance requirements and giving better control over log management costs. This update targets a common pain point where failed image builds could run indefinitely, consuming resources and generating costs without producing usable outputs – particularly valuable for organizations running frequent automated builds. The features are available at no additional cost across all AWS commercial regions including China and GovCloud , making them immediately accessible for existing Image Builder users through Console, CLI, API, CloudFormation , or CDK. These enhancements position Image Builder as a more mature CI/CD tool for AMI creation, competing more effectively with third-party solutions by addressing operational concerns around cost control and logging flexibility. 16:22 Matt – “I just like this because it automatically disables the pipeline, and I feel like this is more for all those old things that you forgot about that are running that just keep triggering daily that break at one point – or you hope break, so you actually don’t keep spending the money on them. That’s a pretty nice feature, in my opinion, there where it just stops it from running forever.” 18:26 Open Source Model Context Protocol (MCP) Server now available for AmazonBedrock AgentCore AWS releases an open-source Model Context Protocol (MCP) server for Amazon Bedrock AgentCore , providing a standardized interface for developers to build, analyze, and deploy AI agents directly in their development environments with one-click installation support for IDEs like Kiro , Claude Code , Cursor , and Amazon Q Developer CLI . The MCP server enables natural language-driven agent development, allowing developers to iteratively build agents and transform agent logic to work with the AgentCore SDK before deploying to development accounts, streamlining the path from prototype to production. This integration addresses the complexity of AI agent development by providing a unified protocol that works across multiple development tools, reducing the friction between local development and AWS deployment while maintaining security and scale capabilities. Available globally via GitHub, the MCP server represents AWS’s commitment to open-source tooling for generative AI development, complementing the broader AgentCore platform which handles secure deployment and operation of AI agents at scale. For businesses looking to implement AI agents, this reduces development time and technical barriers while maintaining enterprise-grade security and scalability, with pricing following the standard Amazon Bedrock AgentCore model. 20:50 Ryan- “This is one of those things where I’m a team of one right now doing a whole bunch of snowflake development of internal services, and so I’m like, what’s this for? I don’t understand the problem. But I can imagine that this is something that’s really useful more when you’re spreading out against teams so that you can get unification on some of these things, because if you have a team of people all developing separate agents that are, in theory, somehow going to work together…so I think this is maybe a step in that direction.” 22:02 Amazon ECS now supports one-click event capture and event history querying in the AWS Management Console Amazon ECS adds one-click event capture in the console that automatically creates EventBridge rules and CloudWatch log groups , eliminating manual setup for monitoring task state changes and service events. The new event history tab provides pre-built query templates for common troubleshooting scenarios like stopped tasks and container exit codes, keeping data beyond the default retention limits without requiring CloudWatch Logs Insights knowledge. This addresses a long-standing pain point where ECS task events would disappear after tasks stopped, making post-mortem debugging difficult – now operators can query historical events directly from the ECS console with filters for time range, task ID, and deployment ID. The feature is available in all AWS Commercial and GovCloud regions at standard CloudWatch Logs pricing, making it accessible for teams that need better visibility into container lifecycle events without additional tooling. For DevOps teams managing production ECS workloads, this simplifies incident response by consolidating event data in one place rather than jumping between multiple AWS consoles to piece together what happened during an outage. 23:14 Jonathan – “It’s a great click ops feature.” 24:04 AWS Knowledge MCP Server now generally available AWS launches a free MCP (Model Context Protocol) server that provides AI agents and LLM applications direct access to AWS documentation, blog posts, What’s New announcements, and Well-Architected best practices in a format optimized for language models. The server includes regional availability data for AWS APIs and CloudFormation resources, helping AI agents provide more accurate responses about service availability and reduce hallucinations when answering AWS-related questions. No AWS account required and available at no cost with rate limits, making it accessible for developers building AI assistants or chatbots that need authoritative AWS information without manual context management. Compatible with any MCP client or agentic framework supporting the protocol, allowing developers to integrate trusted AWS knowledge into their AI applications through a simple endpoint configuration. This addresses a common challenge where AI models provide outdated or incorrect AWS information by ensuring responses are anchored in official, up-to-date AWS documentation and best practices. 25:46 Jonathan – “It’s the rate limiting; it’s putting realistic in controls in place, whereas before they would just scrap everything.” 28:48 Automatic quota management is now generally available for AWS Service Quotas AWS Service Quotas now automatically monitors quota usage and sends proactive notifications through email, SMS, or Slack before customers hit their limits, preventing application interruptions from quota exhaustion. The feature integrates with AWS Health and CloudTrail events, enabling customers to build automated workflows that respond to quota threshold alerts and potentially request increases programmatically. This addresses a common operational pain point where teams discover quota limits only after hitting them, causing service disruptions or failed deployments during critical scaling events. (Really though, is there any other way?) The service is available at no additional cost across all commercial AWS regions, making it accessible for organizations of any size to improve their quota management practices. For DevOps teams managing multi-account environments, this provides centralized visibility into quota consumption patterns across services, helping predict future needs and plan capacity more effectively. 32:06 Amazon RDS for Db2 launches support for native database backups RDS for Db2 now supports native database-level backups, allowing customers to selectively back up individual databases within a multi-database instance rather than requiring full instance snapshots. This enables more granular control for migrations and reduces storage costs. The feature addresses a common enterprise need for moving specific databases between environments – customers can now easily migrate individual databases to another RDS instance or back to on-premises Db2 installations using standard backup commands. Development teams benefit from the ability to quickly create database copies for testing environments without duplicating entire instances, while compliance teams can maintain separate backup copies of specific databases to meet regulatory requirements. Cost optimization becomes more achievable as customers only pay for storage of the specific databases they need to back up rather than full instance snapshots, particularly valuable for instances hosting multiple databases where only some require frequent backups. The feature is available in all regions where RDS for Db2 is offered, with pricing following standard RDS storage rates detailed at aws.amazon.com/rds/db2/pricing. GCP 34:19 Gemini CLI for PostgreSQL in action | Google Cloud Blog Google introduces Gemini CLI extension for PostgreSQL that enables natural language database management, allowing developers to implement features like fuzzy search through conversational commands instead of manual SQL configuration and extension management. The tool automatically identifies appropriate PostgreSQL extensions (like pg_trgm for fuzzy search), checks installation status, handles setup, and generates optimized queries with proper indexing recommendations – reducing typical multi-step database tasks to simple English requests. Key capabilities include full lifecycle database control from instance creation to user management, automatic code generation based on table schemas, and intelligent schema exploration – positioning it as a database assistant rather than just a command line tool. This addresses a common developer pain point of context switching between code editors, database clients, and cloud consoles, potentially accelerating feature development for applications requiring advanced PostgreSQL capabilities like search functionality. Available through GitHub at github.com/gemini-cli-extensions/postgres, this represents Google’s broader push to integrate Gemini AI across their cloud services, though pricing details and performance benchmarks compared to traditional database management approaches aren’t specified. 35:35 Matt – “I really like the potentially increasing people, because they don’t have context switch. It’s like it’s a feature.” 39:01 Google announces new $4 billion investment in Arkansas Google is investing $4 billion in Arkansas through 2027 to build its first data center in the state at West Memphis, expanding GCP’s regional presence and capacity for cloud and AI workloads in the central US. The investment includes a 600 MW solar project partnership with Entergy and programs to reduce peak power usage, addressing the growing energy demands of AI infrastructure while improving grid stability. Google is providing free access to Google AI courses and Career Certificates to all Arkansas residents, starting with University of Arkansas and Arkansas State University students, to build local cloud and AI talent. The $25 million Energy Impact Fund for Crittenden County residents demonstrates Google’s approach to community investment alongside data center development, potentially setting a model for future expansions. This positions GCP to better serve customers in the central US with lower latency and regional data residency options, competing with AWS and Azure’s existing presence in neighboring states. 40:25 Ryan – “So per some live research, Walmart is using both Azure and Google as their own private data center infrastructure.” Azure 43:36 Accelerating our commercial growth Microsoft is restructuring its commercial organization under Judson Althoff as CEO of commercial business, consolidating sales, marketing, operations, and engineering teams to accelerate AI transformation services for enterprise customers. The reorganization creates a unified commercial leadership team with shared accountability for product strategy, go-to-market readiness, and sales execution, potentially streamlining how Azure AI services are delivered to customers. Operations teams now report directly to commercial leadership rather than corporate, which should tighten feedback loops between customer needs and Azure service delivery. This structural change allows Satya Nadella and engineering leaders to focus on datacenter buildout, systems architecture, and AI innovation while commercial teams handle customer-facing execution. The move signals Microsoft’s push to position itself as the primary partner for enterprise AI transformation, likely intensifying competition with AWS and Google Cloud for AI workload dominance. 45:47 Matt – “Yeah, I think it’s just the AI. Even our account team changed their name a bunch; they al have AI in their name now.” 46:31 Grok 4 is now available in Microsoft Azure AI Foundry | Microsoft Azure Blog Microsoft brings xAI’s Grok 4 model to Azure AI Foundry with a 128K token context window, native tool use, and integrated web search capabilities, positioning it as a competitor to GPT-4 and Claude for enterprise reasoning tasks. The model features “think mode” for first-principles reasoning that breaks down complex problems step-by-step, making it particularly suited for research analysis, tutoring, and troubleshooting scenarios where logical consistency matters. Pricing starts at $2 per million input tokens and $10 per million output tokens for Grok 4, with faster variants available at lower costs – Grok 4 Fast Reasoning at $0.60/$2.40 and Fast Non-Reasoning at $0.30/$1.20 per million tokens. Azure AI Content Safety is enabled by default for all Grok models, addressing enterprise concerns about responsible AI deployment while Microsoft continues safety testing and compliance checks. The extended context window allows processing entire code repositories or hundreds of pages of documents in a single request, reducing the need to manually chunk large inputs for analysis tasks. 48:18 Ryan – “I like competition generally, and so it’s good to see another competitor model developer, but it is it like they’re adding features that are one model behind Anthopic and OpenAI.” 49:06 Microsoft to allow consumer Copilot in corporate environs • The Register Question one: What? Microsoft now allows employees to use personal Copilot subscriptions (Personal, Family, or Premium) with work Microsoft 365 accounts, effectively endorsing shadow IT practices while maintaining that enterprise data protections remain intact through Entra identity controls . IT administrators can disable this feature (which they are rushing to do right now) through cloud policy controls and audit personal Copilot interactions, though the default enablement removes their initial authority over AI tool adoption within their organizations. This move positions Microsoft to boost Copilot adoption statistics by any means necessary counting personal usage in enterprise environments, while competing AI vendors may view this as Microsoft leveraging its Office dominance to crowd out alternatives. Government tenants (GCC/DoD) are excluded from this capability, and employees should note that their personal Copilot prompts and responses will be captured and auditable by their employers. The feature represents Microsoft’s shift from preventing shadow IT to managing it, potentially creating compliance challenges for organizations with strict data governance requirements while offering a controlled alternative to completely unmanaged AI tools. 50:44 Ryan – “I think this is nutso.” 53:00 Fabric Mirroring for Azure SQL Managed Instance (Generally Available) | Microsoft Fabric Blog | Microsoft Fabric Azure SQL Managed Instance Mirroring enables near real-time data replication to Microsoft Fabric’s OneLake without ETL processes, supporting both data changes and schema modifications like column additions/drops unlike traditional CDC approaches. The feature provides free compute and storage based on Fabric capacity size (F64 capacity includes 64TB free mirroring storage), with OneLake storage charges only applying after exceeding the free limit. Mirrored data becomes immediately available across all Fabric services including Power BI Direct Lake mode, Data Warehouse, Notebooks, and Copilots, allowing cross-database queries between mirrored databases, warehouses, and lakehouses. Microsoft positions this as a zero-code, zero-ETL solution competing with AWS Database Activity Streams and GCP Datastream, targeting enterprises seeking simplified operational data access and reduced total cost of ownership. The service extends beyond Managed Instance to include Azure SQL Database and SQL Server 2016-2025, creating a unified mirroring approach across Microsoft’s entire SQL portfolio into their analytics platform. Interested in pricing? Find that here . 54:55 Ryan – “Because Microsoft SQL server is so memory intensive for performance, being able to do large queries across, you know, datasets has always been difficult with that…So I can see why this is very handy if you’re Microsoft SQL on Azure. And then the fact that they’re giving you so much for free is the incentive there. They know what they’re doing.” 56:35 Generally Available: Azure Firewall Updates – IP Group limit increased to 600 per Firewall Policy Azure Firewall Policy now supports 600 IP Groups per policy, tripling the previous limit of 200, allowing organizations to consolidate more network security rules into fewer, more manageable groups. This enhancement directly addresses enterprise scalability needs by reducing rule complexity – instead of maintaining thousands of individual IP addresses across multiple policies, administrators can organize them into logical groups like “branch offices” or “partner networks.” The increased limit brings Azure Firewall closer to parity with AWS Network Firewall and GCP Cloud Armor, which have historically offered more flexible rule management options for large-scale deployments. Primary beneficiaries include large enterprises and managed service providers who manage complex multi-tenant environments, as they can now implement more granular security policies without hitting artificial limits. While the feature itself is free, customers should note that Azure Firewall pricing starts at $1.25 per deployment hour plus data processing charges, making efficient rule management critical for cost optimization. 57:50 Matt – “Azure Firewall isn’t cheap, but it’s also your but it’s also your IDS and IPS, so if you’re comparing it to Apollo Alto or any of these other massive ones, the Premiere version is not cheap, but it does give you a lot of those security things.” Other Clouds 58:54 Announcing cost-efficient storage with Network file storage, cold storage, and usage-based backups | DigitalOcean DigitalOcean is launching Network File Storage (NFS) on October 20th, a managed file system service starting at 50 GiB increments that supports NFSv3/v4 and allows multiple GPU/CPU droplets to mount the same share for AI/ML workloads . This addresses the need for shared high-performance storage without the typical 1TB+ minimums of competitors. Spaces cold storage enters public preview at $0.007/GiB per month with one free retrieval monthly, targeting petabyte-scale datasets that need instant access but are rarely used. The pricing model avoids unpredictable retrieval fees common with other providers by including one monthly retrieval in the base price. Usage-based backups now support 4, 6, or 12-hour backup intervals with retention from 3 days to 6 months, priced from $0.01-0.04/GiB-month based on frequency. This consumption-based model helps businesses meet strict RPO requirements without paying for unused capacity. All three services target AI/ML workloads and data-intensive applications, with NFS optimized for training datasets, cold storage for archived models, and frequent backups for GPU droplet protection. The combination provides a complete storage strategy for organizations dealing with growing data footprints. The services are initially available in limited regions (NFS in ATL1 and NYC) with preview access requiring support tickets or form submissions, indicating a measured rollout approach typical of infrastructure services. 1:01:24 Matt – “At lot of these companies don’t need the scale, the flexibility and everything else that AWS, GCP, and Azure provide…this is probably all they need.” 1:02:36 Build Smarter Agents with Image Generation, Auto-Indexing, VPC Security, and new AI Tools on DigitalOcean Gradient AI Platform | DigitalOcean DigitalOcean’s Gradient AI Platform now supports image generation through OpenAI’s gpt-image-1 model, marking their first non-text modality and enabling developers to create images programmatically via the same API endpoint used for text completions. Auto-indexing for Knowledge Bases automatically detects, fetches, and re-indexes new or updated documents from connected sources into OpenSearch databases, reducing manual maintenance for keeping AI agents’ knowledge current. New VPC integration allows AI agents and indexing jobs to run on private networks within DigitalOcean’s managed infrastructure, addressing enterprise security requirements without exposing services to the public internet. Two new developer tools are coming: the Agent Development Kit (ADK) provides a code-first framework for building and deploying AI agent workflows, while Genie offers VS Code integration for designing multi-agent systems using natural language. These updates position DigitalOcean to compete more directly with major cloud providers in the AI platform space by offering multimodal capabilities, enterprise security features, and developer-friendly tooling for building production AI applications. 1:04:14 Matt – “Theyre really learning about their audience, and they’re going to build specific to what their customer needs… and they’ve determined that their customers need these image generation AI features. They’re not always the fastest, but they always get there.” 1:05:11 Announcing per-sec billing, new Droplet plans, BYOIP, and NAT gateway preview to reduce scaling costs | DigitalOcean DigitalOcean is switching from hourly to per-second billing for Droplets starting January 1, 2026, with a 60-second minimum charge, which seems like the standard now. This change could reduce costs by up to 80% for short-lived workloads like CI/CD pipelines that previously paid for full hours when only using minutes. New intermediate Droplet sizes bridge the gap between shared and dedicated CPU plans, allowing in-place upgrades without IP changes or data migration. The new plans include 5x SSD variants for CPU Optimized and 6.5x SSD variants for General Purpose, addressing the previous large cost jump between tiers. Bring Your Own IP (BYOIP) is now generally available with a 7-day setup time compared to 1-4 weeks at hyperscalers. This allows businesses to maintain their IP reputation and avoid breaking client allow-lists when migrating to DigitalOcean. VPC NAT Gateway enters public preview at $40/month including 100GB bandwidth, supporting up to 500,000 simultaneous connections. This managed service provides centralized egress with static IPs for private resources without the complexity of self-managed NAT instances. These updates target cost optimization and migration friction points, particularly benefiting ephemeral workloads, auto-scaling applications, and businesses needing to maintain IP continuity during cloud migrations. 1:09:31 Introducing Snowflake Managed MCP Servers for Secure, Governed Data Agents Snowflake is introducing Managed MCP (Model Context Protocol) Servers that enable secure data agents to access enterprise data while maintaining governance and compliance controls. This addresses the challenge of giving AI agents access to sensitive data without compromising security. The MCP protocol, originally developed by Anthropic, allows AI assistants to interact with external data sources through a standardized interface. Snowflake’s implementation adds enterprise-grade security layers including authentication, authorization, and audit logging. Data agents can now query Snowflake databases, run SQL commands, and retrieve results without requiring direct database credentials or exposing sensitive connection strings. All interactions are governed by Snowflake’s existing role-based access controls and data governance policies. This integration enables organizations to build AI applications that can answer questions about their business data while ensuring compliance with data residency, privacy regulations, and internal security policies. The managed service handles infrastructure complexity and scaling automatically. Developers can connect popular AI frameworks and tools to Snowflake data through the MCP interface, reducing the complexity of building secure data pipelines for AI applications. This positions Snowflake as a bridge between enterprise data warehouses and the emerging AI agent ecosystem. Closing And that is the week in the cloud! 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