Posts Tagged aws

We all know how easy it is to create, deploy, and manage any cloud infrastructure with Pulumi using your favorite programming language. With the rise of artificial intelligence (AI) more and more developers are working on LLM-powered applications and services. And with this, the need to have the same ease of use for creating, deploying, and managing the infrastructure for these applications is growing.

In this blog post, we will show you how to this can be achieved with combining Pulumi and LangServe.

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At AWS re:Invent 2023, AWS announced the new Amazon S3 Express One Zone storage class. This new service provides incredible performance by collocating your S3 buckets closer to the workloads that use the data. Compared to the S3 Standard storage class, the Express One Zone storage class is up to 10x faster, handles 100,000s of requests per second, offers single-digit millisecond latency, and can reduce request costs by 50%. This can be extremely beneficial for data-intensive workloads such as AI/ML, media, finance, realtime, and high-performance computing scenarios. This blog post shows how to get started with Amazon S3 Express One Zone using Pulumi infrastructure as code.

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Redis Enterprise Cloud makes apps faster by providing an in-memory database that enables teams to create a real-time data platform. In this post, you’ll learn how to enable connectivity between Redis Enterprise Cloud and your workloads using Pulumi and the Redis Cloud provider.

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In a world where cloud computing is the backbone of modern applications, managing environments and secrets is of the utmost importance. Earlier this month we released a new service called Pulumi ESC (Environments, Secrets, and Configuration), the focus of which is to help alleviate the burden of managing cloud configuration and secrets by providing a centralized way to handle these critical aspects of cloud development. It’s like having a Swiss Army knife in your toolkit, ready to tackle the challenges of cloud infrastructure. This post will highlight the specific challenge of credentials management, and we’ll specifically dive into how using the esc run functionality of Pulumi ESC will make that easier.

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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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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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We are excited to announce 6.0 of the Pulumi AWS provider. The AWS provider is the most heavily used provider across the entire Pulumi ecosystem, and offers access to the full surface area of the upstream Terraform AWS Provider in Pulumi projects in all supported languages. The 6.0 release brings a substantial set of fixes and improvements to the provider, including a number of breaking changes as part of the major version release.

This blog post walks you through the list of notable changes in the new major version.

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Danny Zalkind is the Senior Director of Infrastructure Engineering for Skai, an award-winning intelligent marketing platform. He brings his 15 years of experience of managing tech teams to his current role where he’s dedicated to allow Skai R&D to efficiently produce and serve software. You can find him on Linkedin.

As Skai continues its journey towards fully migrating to the cloud using Pulumi, we’ve taken another large bite out of the migration pie, moving our most critical data to AWS on top of Amazon Keyspaces, an Apache Cassandra–compatible database service.

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In this blog series, you will learn how to create a hub-and-spoke network architecture in AWS with centralized egress and traffic inspection. In this second installment, we’ll show you how to create spoke VPCs to run your workloads, verify centralized egress is working, and then add centralized traffic inspection using Pulumi, the infrastructure as code tool that enables you to manage infrastructure with real programming languages!

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Since its introduction in 2014, AWS Lambda has rapidly expanded its capabilities from simple “functions in the cloud” at launch to a comprehensive serverless platform with support for containerized functions and public per-function URL endpoints.

As serverless applications have increased in sophistication, developers have used functions-as-a-service as a first-class tool in their microservices strategy. As organizations increasingly look to break up their monolithic applications into services, adoption of AWS Lambda has not been a viable option for applications that return payloads larger than the 6 MiB Lambda service limit. In addition to the payload response limit, AWS Lambda has been limited to returning the entire response once it has been fully buffered in memory within the Lambda service. This historical limitation meant that end users would not see any response until the entire response was available. Both of these limitations made Lambda difficult or impossible to use for data-intensive or latency-sensitive use cases.

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