Today we announced a $41M Series C fundraise from Madrona Ventures, NEA, Tola Capital, and Strike Capital. These new funds will help us accelerate momentum, keep innovating with the best infrastructure as code technology on the market, and expand into new product areas to solve even more of our customers’ most pressing cloud challenges. We’ve had a year of exciting milestones – surpassing 150,000 end users in our community, 2,000 customers, and 100 employees – and look forward to many more to come. The future is full of cloud, and yet incredibly bright!

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We’ve had an exciting quarter at Pulumi, shipping all kinds of improvements from our providers to our Cloud service. To stay up-to-date on all the details and additional improvements between release blogs, be sure to check out the pulumi/pulumi repo changelog for CLI enhancements and the Pulumi Cloud features in the new features blogs for updates on Pulumi Service features. We have a lot to cover, so let’s get started!

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Bicep is a DSL developed by Microsoft to simplify the authoring of ARM templates and deploy resources to Azure. Today I will be sharing with you a new Pulumi converter plugin that I have been working on that converts Bicep code to any of the supported Pulumi languages.

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This is the second post in a series about performance optimizations we’ve made to the Pulumi CLI and SDKs. In this post, we’ll go deep on a performance improvement we made for Pulumi Python programs. You can read more about Amazing Performance in the first post in the series.

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Pulumi loves Go, it’s what powers Pulumi. We’ve kept a close eye on the design and development of support for generics in the Go programming language over the years, a feature that allows developers to write type-safe, concise, and reusable code. We’ve been exploring what it’d look like to improve Pulumi’s Go SDKs with generics and recently published a public RFC detailing our plans. We’ve been making progress on the implementation and are excited to announce preview support for Go generics in our core and AWS Go SDKs. If you’re using Go with Pulumi, we’d love for you to give it a try and share your feedback!

// Given
var a pulumi.IntOutput
var b pulumi.StringOutput

// Before (could panic at runtime if you got something wrong)
o := pulumi.All(a, b).ApplyT(func(vs []interface{}) string { // could panic
    a := vs[0].(int) // could panic
    b := vs[1].(string) // could panic
    return strconv.Itoa(a) + b
}).(pulumi.StringOutput) // could panic

// After (compile-time type-safety)
o := pulumix.Apply2(a, b, func(a int, b string) string {
    return strconv.Itoa(a) + b
})

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Earlier this year we introduced Pulumi Insights, a collection of features that bring intelligence to cloud infrastructure using Pulumi. Two key components of that launch were Pulumi AI, a generative AI assistant purpose-built to create cloud infrastructure using natural language, and Pulumi Resource Search, multi-cloud search and analytics across every cloud resource and environment in your organization. Today, we are excited to bring Pulumi Insights into the pulumi CLI with the new pulumi org search and pulumi ai commands.

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The process of taking an idea and turning it into reality has been nothing short of extraordinary since we started innovating with Artificial Intelligence. With this technology, machines learn about and communicate with people, while also helping us in ways we never could have imagined only a few years ago. If you’ve been following along, you might recall our previous article where we used Python and Pulumi to a chatbot API (named katwalk) to the cloud.

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Running models from Hugging Face on Amazon SageMaker is a popular deployment option for AI/ML services. While the SageMaker console allows for provisioning these cloud resources, this deployment pattern is labor intensive to document and vulnerable to human errors when reproducing as a regular operations practice. Infrastructure as Code (IaC) offers a reliable and easy to duplicate deployment practice. By developing this IaC with Pulumi, practitioners can choose to write their infrastructure code in Python and seamlessly develop both AI application code and IaC code in the same language.

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Getting your Pulumi notifications into Microsoft Teams is now easier than ever. Organizations using Pulumi Cloud have previously been setting up Microsoft Teams notifications for their stacks using Pulumi Cloud Webhooks. Webhooks can attach to a Pulumi organization or a Pulumi stack. Starting today, customers can set up a Microsoft Teams integration, for organization or stack notifications, with fewer steps and without needing to host the infrastructure themselves.

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The AI industry is stealing the show as tech’s goldrush of the ’20s. Just looking at ChatGPT’s record setting user growth, and rapid 3rd party integration by top brands, it is not surprising the hype suggests this is the beginning of a major digital transformation. However, using AI/ML in your own products has some major challenges and obstacles. Below is a diagram of the end to end workflow of building and using an AI model: preparing the data, training a model, fine-tuning a model, hosting and running a model, building a backend service to serve the model, and building the user interface that interacts with the model.

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