Pulumi

Managing infrastructure on #GoogleCloud often starts simply but becomes more complex as projects and environments grow. Changes are harder to reason about, configurations drift, and maintaining consistency across teams becomes harder. This hands-on workshop introduces Infrastructure as Code on Google Cloud using familiar programming languages and established software engineering practices. You will see how infrastructure can be defined, deployed, and managed as code, and how this approach connects application and infrastructure workflows. The session focuses on practical #GCP use cases and shows how teams manage infrastructure consistently across projects and environments. It is designed for engineers who want a clearer, more reliable foundation for working with Google Cloud. #InfrastructureAsCode #DevOps

Managing secrets and configuration usually means bouncing between YAML, CLIs, and web consoles, with too little visibility into what is actually happening. Pulumi ESC already gives teams one way to manage secrets and config across clouds, vaults, and tools using short lived credentials and rotation. The new ESC Web Editor makes that experience smoother. You can move freely between YAML and a rich UI, with clear context for every value, provider, and function. It is now much easier to set up things like #OIDC credentials, securely edit secrets, and export config to stacks or environment variables, all without losing track of how your environment is defined. Introducing the new and improved ESC Editor, learn more at https://hubs.ly/Q03-ZY9N0 #Pulumi #CloudSecurity #DevSecOps

Introducing the Stash resource in Pulumi IaC, a new built-in resource for saving values directly to your stack's state. Great for first-run scenarios where you need to capture and preserve values from the initial deployment. Learn more at https://hubs.ly/Q03-PVxp0 #Pulumi #DevOps #InfrastructureAsCode

Pulumi Cloud just unlocked a major performance jump for infrastructure as code. Updates can now run up to 20x faster by recording only what changed instead of rewriting full state on every operation. The idea comes from database transaction journaling. State is captured as a stream of incremental entries rather than heavy snapshots. That keeps reliability and recovery intact while dramatically reducing the amount of data that has to move on every update. For teams running large stacks or deploying frequently, this changes how fast you can iterate on real production infrastructure without trading off safety or correctness. Learn more at https://hubs.ly/Q03-CSPj0 #Pulumi #DevOps #PlatformEngineering #InfrastructureAsCode

𝗪𝗵𝗮𝘁 𝗶𝗳 𝘆𝗼𝘂𝗿 𝗼𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝘀𝘆𝘀𝘁𝗲𝗺 𝗰𝗼𝘂𝗹𝗱 𝗳𝗶𝘅 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 𝗶𝘁 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘀? Join Liz Fong-Jones (honeycomb.io), Marina Novikova (#AWS), and Engin Diri (Pulumi) on January 14 for a hands-on workshop that shows how AI agents can close the loop between telemetry and infrastructure. Instead of jumping between dashboards and IaC, you will see #OpenTelemetry data from Honeycomb drive real infrastructure changes on AWS, with Pulumi Neo generating patches and submitting pull requests for human approval. You will deploy a GenAI workload, surface anomalies, watch an agent reason about the root cause, and produce infrastructure as code updates in real time using modern, enterprise-ready workflows. Register here: https://hubs.ly/Q03-nT9W0 #Observability #PlatformEngineering #DataEngineering

AI teams are building across #AWS Bedrock, #Azure OpenAI, GCP Vertex AI, and specialized GPU providers. Managing this complexity shouldn't require learning proprietary configuration languages or getting locked into a single vendor's tooling. ▪️Supabase manages 80,000 resources across 16 AWS regions in TypeScript. The same language they use for their application services. ▪️Snowflake runs 100,000+ daily deployments across three major clouds. ▪️BMW Group enables 15,000 developers to access self-service infrastructure. Infrastructure that scales with superintelligence workloads while remaining accessible to every ML team. Built with code, not configuration languages. Learn more at https://hubs.ly/Q03-0shd0 #AIinfrastructure #PlatformEngineering #MLops

𝗶𝗻𝗴𝗿𝗲𝘀𝘀-𝗻𝗴𝗶𝗻𝘅 is scheduled to retire in early 2026, and many #Kubernetes teams still rely on it as the default way traffic enters their clusters. That timeline may feel far off, but networking changes tend to be the hardest to rush once pressure sets in. The move toward the Gateway API is not just about replacing an ingress controller. It reflects a shift toward a more expressive, standardized model for traffic management that better supports modern Kubernetes workloads and shared ownership between platform and application teams. If you are wondering what this transition actually looks like in practice and which Gateway implementations are ready for real production use, this walkthrough breaks down the tradeoffs and highlights why 𝗸𝗴𝗮𝘁𝗲𝘄𝗮𝘆 is emerging as a strong option. Read the full guide at https://hubs.ly/Q03ZV6vk0 #InfrastructureAsCode #DevOps #PlatformEngineering

Getting an AI agent to work is only the first step. The real challenge is deciding what comes next once the idea shows promise. This project follows a fraud detection agent from a small experiment into something that can actually run in production. Structure is added only when it becomes necessary. A managed runtime when reliability matters. Memory when context matters. Clear boundaries when the agent needs access to tools. Infrastructure as code, so the setup can be reproduced as it grows. The focus is not on building a perfect system. It is on showing a realistic path from an idea to an experiment and eventually to something a team can run and evolve without rewriting everything along the way. Follow along with the full project at https://hubs.ly/Q03ZKG2q0 #AIagent #DevOps #MLops #MachineLearning

Platform and data teams spend too much time stitching together infrastructure data from APIs, providers, and custom scripts, even though the insights already live in Pulumi Cloud. The 'tap-pulumi-cloud' connector makes that data usable by loading Pulumi Cloud data directly into tools teams already rely on, including BigQuery, Snowflake, Redshift, Postgres, and more via Meltano. Instead of building and maintaining custom integrations, teams get structured, analytics ready data they can use to answer real questions, such as: • Trends in resources under management • Average deploy times and DORA metrics • Updates per user and team activity • Cost and utilization reporting This creates a clean data foundation for dashboards, reporting, and deeper analysis without extra operational overhead. Read more at https://hubs.ly/Q03Ztvg60 #Telemetry #DataWarehouse #DataEngineering #PlatformEngineering

ARM templates often grow into a tangle of JSON with copy-pasted logic, brittle parameters, and limited tooling. For C# developers, that’s a workflow that doesn’t scale. Pulumi changes the game. Manage #Azure infrastructure with the same language, libraries, and tooling you use to build your applications. Write infrastructure in C#, reuse logic, and integrate it into your existing .NET development processes. It’s not just easier, it’s more reliable, more testable, and far more maintainable. Explore what’s possible with C# and Pulumi on Azure: https://hubs.ly/Q03YdwV40 #DevOps #InfrastractureAsCode #ARMtemplates #MicrosoftAzure